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  • 1.
    Coenen, Adriaan
    et al.
    Erasmus Univ, Netherlands.
    Kim, Young-Hak
    Univ Ulsan, South Korea.
    Kruk, Mariusz
    Inst Cardiol, Poland.
    Tesche, Christian
    Med Univ South Carolina, SC 29425 USA.
    De Geer, Jakob
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Kurata, Akira
    Ehime Univ, Japan.
    Lubbers, Marisa L.
    Erasmus Univ, Netherlands.
    Daemen, Joost
    Erasmus Univ, Netherlands.
    Itu, Lucian
    Siemens SRL, Romania.
    Rapaka, Saikiran
    Siemens Healthcare, NJ USA.
    Sharma, Puneet
    Siemens Healthcare, NJ USA.
    Schwemmer, Chris
    Siemens Healthcare GmbH, Germany.
    Persson, Anders
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Schoepf, U. Joseph
    Med Univ South Carolina, SC 29425 USA.
    Kepka, Cezary
    Inst Cardiol, Poland.
    Yang, Dong Hyun
    Univ Ulsan, South Korea.
    Nieman, Koen
    Erasmus Univ, Netherlands; Stanford Univ, CA 94305 USA.
    Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography-Based Fractional Flow Reserve Result From the MACHINE Consortium2018In: Circulation Cardiovascular Imaging, ISSN 1941-9651, E-ISSN 1942-0080, Vol. 11, no 6, article id e007217Article in journal (Refereed)
    Abstract [en]

    Background: Coronary computed tomographic angiography (CTA) is a reliable modality to detect coronary artery disease. However, CTA generally overestimates stenosis severity compared with invasive angiography, and angiographic stenosis does not necessarily imply hemodynamic relevance when fractional flow reserve (FFR) is used as reference. CTA-based FFR (CT-FFR), using computational fluid dynamics (CFD), improves the correlation with invasive FFR results but is computationally demanding. More recently, a new machine-learning (ML) CT-FFR algorithm has been developed based on a deep learning model, which can be performed on a regular workstation. In this large multicenter cohort, the diagnostic performance ML-based CT-FFR was compared with CTA and CFD-based CT-FFR for detection of functionally obstructive coronary artery disease. Methods and Results: At 5 centers in Europe, Asia, and the United States, 351 patients, including 525 vessels with invasive FFR comparison, were included. ML-based and CFD-based CT-FFR were performed on the CTA data, and diagnostic performance was evaluated using invasive FFR as reference. Correlation between ML-based and CFD-based CT-FFR was excellent (R=0.997). ML-based (area under curve, 0.84) and CFD-based CT-FFR (0.84) outperformed visual CTA (0.69; Pamp;lt;0.0001). On a per-vessel basis, diagnostic accuracy improved from 58% (95% confidence interval, 54%-63%) by CTA to 78% (75%-82%) by ML-based CT-FFR. The per-patient accuracy improved from 71% (66%-76%) by CTA to 85% (81%-89%) by adding ML-based CT-FFR as 62 of 85 (73%) false-positive CTA results could be correctly reclassified by adding ML-based CT-FFR. Conclusions: On-site CT-FFR based on ML improves the performance of CTA by correctly reclassifying hemodynamically nonsignificant stenosis and performs equally well as CFD-based CT-FFR.

  • 2.
    De Geer, Jakob
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    On the use of computed tomography in cardiac imaging2016Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Background

    Cardiac Computed Tomography Angiography (CCTA) is becoming increasingly useful in the work‐up of coronary artery disease (CAD). Several potential methods for increasing the diagnostic yield of cardiac CT are available.

    Purpose

    Study I: To investigate whether the use of a 2‐D, non‐linear adaptive noise reduction filter can improve CCTA image quality.

    Study II: To evaluate the variation in adenosine stress dynamic CT perfusion (CTP) blood flow as compared to stress 99mTc SPECT. Secondly, to compare the perfusion results from manual and automatic myocardial CTP segmentation.

    Study III: To evaluate the accuracy of non‐invasive, CCTA‐derived Fractional Flow Reserve (cFFR).

    Study IV: To evaluate the prognostic value of CCTA in terms of major adverse cardiac events (MACE).

    Materials and methods

    Study I: Single images from 36 consecutive CCTA exams performed with two different dose levels were used. Image quality in full dose, low‐dose and noise‐reduced low‐dose images was graded using visual grading analysis. Image noise was measured.

    Study II: CTP and SPECT were performed in 17 patients, and the variation in per AHA‐segment blood flow was evaluated and compared. CTP results from manual and automated image segmentation were compared.

    Study III: CCTA datasets from 21 patients were processed using cFFR software and the results compared to the corresponding invasively measured FFR (invFFR).

    Study IV: 1205 consecutive patients with chest pain of unknown origin underwent CCTA. Baseline data and data on subsequent MACE were retrieved from relevant registries. Survival, hazard ratios and the three‐year incidence of cardiac events and readmissions were calculated.

    Results

    Study I: There was significant improvement in perceived image quality for all criteria when the filter was applied, and a significant decrease in image noise.

    Study II: The correlation coefficients for CTP vs. SPECT were 0.38 and 0.41 (p<0.001, for manual and automated segmentation respectively. Mean per patient CTP blood flow in normal segments varied between 94‐183 ml/100 ml tissue/min for manual segmentation, and 104‐196 ml/100 ml tissue/min for automated segmentation. The Spearman rank correlation coefficient for manual vs. automated segmentation CTP was ρ = 0.88 (p<0.001) and the Intraclass Correlation Coefficient (ICC) was 0.93 (p<0.001).

    Study III: The Spearman rank correlation coefficient for cFFR vs. invFFR was ρ = 0.77 (p<0.001) and the ICC was 0.73 (p<0.001). Sensitivity, specificity, positive predictive value and negative predictive value for significant stenosis (FFR<0.80, per vessel) were 0.83, 0.76, 0.56 and 0.93 respectively.

    Study IV: The hazard ratio for non‐obstructive CAD vs. normal coronary arteries was 5.13 (95% C.I 1.03‐25.43, p<0.05), and 151.40 (95% C.I 37.03‐619.08, p<0.001) for obstructive CAD vs. normal coronary arteries. The three‐year incidence of MACE was 1.1% for patients with normal vessels on CCTA, 2.5% for patients with non‐obstructive CAD and 42.7% for patients with obstructive CAD (p<0.001).

    Conclusions:

    Study I: Image quality and noise levels of low dose images were significantly improved with the filter, even though the improvement was small compared to the image quality of the corresponding diastolic full‐dose images.

    Study II: Correlation between dynamic CTP and SPECT was positive but weak. There were large variations in CTP blood flow in normal segments on SPECT, rendering the definition of an absolute cut‐off value for normal vs. ischemic myocardium difficult. Manual and automatic segmentation were equally useful.

    Study III: The correlation between cFFR and invFFR was good, indicating that noninvasively estimated cFFR performs on a similar level as invasively measure FFR.

    Study IV: The long‐term risk for MACE was very low in patients without obstructive CAD on CCTA, though there seemed to be a substantial increase in the risk for MACE even in patients with non‐obstructive CAD as compared to normal coronary arteries. In addition, even patients with normal coronary arteries or non‐obstructive CAD continued to have a substantial number of readmissions for chest pain or angina pectoris.

    List of papers
    1. The efficacy of 2D, non-linear noise reduction filtering in cardiac imaging: a pilot study
    Open this publication in new window or tab >>The efficacy of 2D, non-linear noise reduction filtering in cardiac imaging: a pilot study
    2011 (English)In: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 52, no 7, p. 716-722Article in journal (Refereed) Published
    Abstract [en]

    Background: Computed tomography (CT) is becoming increasingly popular as a non-invasive method for visualizing the coronary arteries but patient radiation doses are still an issue. Postprocessing filters such as 2D adaptive non-linear filters might help to reduce the dose without loss of image quality. less thanbrgreater than less thanbrgreater thanPurpose: To investigate whether the use of a 2D, non-linear adaptive noise reduction filter can improve image quality in cardiac computed tomography angiography (CCTA). less thanbrgreater than less thanbrgreater thanMaterial and Methods: CCTA examinations were performed in 36 clinical patients on a dual source CT using two patient dose levels: maximum dose during diastole and reduced dose (20% of maximum dose) during systole. One full-dose and one reduced-dose image were selected from each of the examinations. The reduced-dose image was duplicated and one copy postprocessed using a 2D non-linear adaptive noise reduction filter, resulting in three images per patient. Image quality was assessed using visual grading with three criteria from the European guidelines for assessment of image quality and two additional criteria regarding the left main artery and the overall image quality. Also, the HU value and its standard deviation were measured in the ascending and descending aorta. Data were analyzed using Visual Grading Regression and paired t-test. less thanbrgreater than less thanbrgreater thanResult: For all five criteria, there was a significant (P andlt; 0.01 or better) improvement in perceived image quality when comparing postprocessed low-dose images with low-dose images without noise reduction. Comparing full dose images with postprocessed low-dose images resulted in a considerably larger, significant (P andlt; 0.001) difference. Also, there was a significant reduction of the standard deviation of the HU values in the ascending and descending aorta when comparing postprocessed low-dose images with low-dose images without postprocessing. less thanbrgreater than less thanbrgreater thanConclusion: Even with an 80% dose reduction, there was a significant improvement in the perceived image quality when using a 2D noise-reduction filter, though not approaching the quality of full-dose images. This indicates that cardiac CT examinations could benefit from noise-reducing postprocessing with 2D non-linear adaptive filters.

    Place, publisher, year, edition, pages
    Informa Healthcare / Wiley-Blackwell / Royal Society of Medicine Press, 2011
    Keywords
    Cardiac, CT angiography, heart, adults, image manipulation
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-71803 (URN)10.1258/ar.2011.100511 (DOI)000295759600007 ()
    Available from: 2011-11-04 Created: 2011-11-04 Last updated: 2017-12-08
    2. Large variation in blood flow between left ventricular segments, as detected by adenosine stress dynamic CT perfusion.
    Open this publication in new window or tab >>Large variation in blood flow between left ventricular segments, as detected by adenosine stress dynamic CT perfusion.
    Show others...
    2015 (English)In: Clinical Physiology and Functional Imaging, ISSN 1475-0961, E-ISSN 1475-097X, Vol. 35, no 4, p. 291-300Article in journal (Refereed) Published
    Abstract [en]

    BACKGROUND: Dynamic cardiac CT perfusion (CTP) is based on repeated imaging during the first-pass contrast agent inflow. It is a relatively new method that still needs validation.

    PURPOSE: To evaluate the variation in adenosine stress dynamic CTP blood flow as compared to (99m) Tc SPECT. Secondarily, to compare manual and automatic segmentation.

    METHODS: Seventeen patients with manifest coronary artery disease were included. Nine were excluded from evaluation for various reasons. All patients were examined with dynamic stress CTP and stress/rest SPECT. CTP blood flow was compared with SPECT on a per segment basis. Results for manual and automated AHA segmentation were compared.

    RESULTS: CTP showed a positive correlation with SPECT, with correlation coefficients of 0·38 and 0·41 for manual and automatic segmentation, respectively (P<0·0001). There was no significant difference between the correlation coefficients of the manual and automated segmentation procedures (P = 0·75). The average per individual global CTP blood flow value for normal segments varied by a factor of 1·9 (manual and automatic segmentation). For the whole patient group, the CTP blood flow value in normal segments varied by a factor of 2·9/2·7 (manual/automatic segmentation). Within each patient, the average per segment blood flow in normal segments varied by a factor of 1·3-2·0/1·2-2·1 (manual/automatic segmentation).

    CONCLUSION: A positive but rather weak correlation was found between CTP and (99m) Tc SPECT. Large variations in CTP blood flow suggest that a cut-off value for stress myocardial blood flow is inadequate to detect ischaemic segments. Dynamic CTP is hampered by a limited coverage.

    National Category
    Clinical Medicine
    Identifiers
    urn:nbn:se:liu:diva-113400 (URN)10.1111/cpf.12163 (DOI)000356312800007 ()24842265 (PubMedID)
    Available from: 2015-01-17 Created: 2015-01-17 Last updated: 2017-12-05
    3. Software-based on-site estimation of fractional flow reserve using standard coronary CT angiography data.
    Open this publication in new window or tab >>Software-based on-site estimation of fractional flow reserve using standard coronary CT angiography data.
    Show others...
    2016 (English)In: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 57, no 10, p. 1186-1192Article in journal (Refereed) Published
    Abstract [en]

    BACKGROUND: The significance of a coronary stenosis can be determined by measuring the fractional flow reserve (FFR) during invasive coronary angiography. Recently, methods have been developed which claim to be able to estimate FFR using image data from standard coronary computed tomography angiography (CCTA) exams.

    PURPOSE: To evaluate the accuracy of non-invasively computed fractional flow reserve (cFFR) from CCTA.

    MATERIAL AND METHODS: A total of 23 vessels in 21 patients who had undergone both CCTA and invasive angiography with FFR measurement were evaluated using a cFFR software prototype. The cFFR results were compared to the invasively obtained FFR values. Correlation was calculated using Spearman's rank correlation, and agreement using intraclass correlation coefficient (ICC). Sensitivity, specificity, accuracy, negative predictive value, and positive predictive value for significant stenosis (defined as both FFR ≤0.80 and FFR ≤0.75) were calculated.

    RESULTS: The mean cFFR value for the whole group was 0.81 and the corresponding mean invFFR value was 0.84. The cFFR sensitivity for significant stenosis (FFR ≤0.80/0.75) on a per-lesion basis was 0.83/0.80, specificity was 0.76/0.89, and accuracy 0.78/0.87. The positive predictive value was 0.56/0.67 and the negative predictive value was 0.93/0.94. The Spearman rank correlation coefficient was ρ = 0.77 (P < 0.001) and ICC = 0.73 (P < 0.001).

    CONCLUSION: This particular CCTA-based cFFR software prototype allows for a rapid, non-invasive on-site evaluation of cFFR. The results are encouraging and cFFR may in the future be of help in the triage to invasive coronary angiography.

    Place, publisher, year, edition, pages
    Sage Publications, 2016
    Keywords
    Cardiac; computed tomography angiography (CTA); heart; arteries; adults; computer applications – detection/diagnosis
    National Category
    Radiology, Nuclear Medicine and Medical Imaging
    Identifiers
    urn:nbn:se:liu:diva-123579 (URN)10.1177/0284185115622075 (DOI)000382967500007 ()26691914 (PubMedID)
    Note

    Funding agencies: Department of Radiology, Region Ostergotland; Swedish Heart-Lung-foundation [20120449]

    Available from: 2015-12-29 Created: 2015-12-29 Last updated: 2017-12-01Bibliographically approved
  • 3.
    de Geer, Jakob
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Coenen, Adriaan
    Erasmus MC, Netherlands.
    Kim, Young-Hak
    Univ Ulsan, South Korea.
    Kruk, Mariusz
    Inst Cardiol, Poland; Inst Cardiol, Poland.
    Tesche, Christian
    Med Univ South Carolina, SC 29425 USA.
    Schoepf, U. Joseph
    Med Univ South Carolina, SC 29425 USA.
    Kepka, Cezary
    Inst Cardiol, Poland; Inst Cardiol, Poland.
    Yang, Dong Hyun
    Univ Ulsan, South Korea.
    Nieman, Koen
    Erasmus MC, Netherlands; Stanford Univ, CA 94305 USA; Stanford Univ, CA 94305 USA.
    Persson, Anders
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Effect of Tube Voltage on Diagnostic Performance of Fractional Flow Reserve Derived From Coronary CT Angiography With Machine Learning: Results From the MACHINE Registry2019In: American Journal of Roentgenology, ISSN 0361-803X, E-ISSN 1546-3141, Vol. 213, no 2, p. 325-331Article in journal (Refereed)
    Abstract [en]

    OBJECTIVE. Coronary CT angiography (CCTA)-based methods allow noninvasive estimation of fractional flow reserve (cFFR), recently through use of a machine learning (ML) algorithm (cFFR(ML)). However, attenuation values vary according to the tube voltage used, and it has not been shown whether this significantly affects the diagnostic performance of cFFR and cFFR(ML). Therefore, the purpose of this study is to retrospectively evaluate the effect of tube voltage on the diagnostic performance of cFFR(ML). MATERIALS AND METHODS. A total of 525 coronary vessels in 351 patients identified in the MACHINE consortium registry were evaluated in terms of invasively measured FFR and cFFR(ML). CCTA examinations were performed with a tube voltage of 80, 100, or 120 kVp. For each tube voltage value, correlation (assessed by Spearman rank correlation coefficient), agreement (evaluated by intraclass correlation coefficient and Bland-Altman plot analysis), and diagnostic performance (based on ROC AUC value, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy) of the cFFR(ML) in terms of detection of significant stenosis were calculated. RESULTS. For tube voltages of 80, 100, and 120 kVp, the Spearman correlation coefficient for cFFR(ML) in relation to the invasively measured FFR value was rho = 0.684, rho = 0.622, and rho = 0.669, respectively (p amp;lt; 0.001 for all). The corresponding intraclass correlation coefficient was 0.78, 0.76, and 0.77, respectively (p amp;lt; 0.001 for all). Sensitivity was 100.0%, 73.5%, and 85.0%, and specificity was 76.2%, 79.0%, and 72.8% for tube voltages of 80, 100, and 120 kVp, respectively. The ROC AUC value was 0.90, 0.82, and 0.80 for 80, 100, and 120 kVp, respectively (p amp;lt; 0.001 for all). CONCLUSION. CCTA-derived cFFR(ML) is a robust method, and its performance does not vary significantly between examinations performed using tube voltages of 100 kVp and 120 kVp. However, because of rapid advancements in CT and postprocessing technology, further research is needed.

  • 4.
    De Geer, Jakob
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Gjerde, Marcus
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Cardiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Brudin, Lars
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Department of Clinical Physiology in Kalmar, Linköping University, County Council of Kalmar, Kalmar, Sweden.
    Olsson, Eva
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Persson, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Engvall, Jan
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Large variation in blood flow between left ventricular segments, as detected by adenosine stress dynamic CT perfusion.2015In: Clinical Physiology and Functional Imaging, ISSN 1475-0961, E-ISSN 1475-097X, Vol. 35, no 4, p. 291-300Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Dynamic cardiac CT perfusion (CTP) is based on repeated imaging during the first-pass contrast agent inflow. It is a relatively new method that still needs validation.

    PURPOSE: To evaluate the variation in adenosine stress dynamic CTP blood flow as compared to (99m) Tc SPECT. Secondarily, to compare manual and automatic segmentation.

    METHODS: Seventeen patients with manifest coronary artery disease were included. Nine were excluded from evaluation for various reasons. All patients were examined with dynamic stress CTP and stress/rest SPECT. CTP blood flow was compared with SPECT on a per segment basis. Results for manual and automated AHA segmentation were compared.

    RESULTS: CTP showed a positive correlation with SPECT, with correlation coefficients of 0·38 and 0·41 for manual and automatic segmentation, respectively (P<0·0001). There was no significant difference between the correlation coefficients of the manual and automated segmentation procedures (P = 0·75). The average per individual global CTP blood flow value for normal segments varied by a factor of 1·9 (manual and automatic segmentation). For the whole patient group, the CTP blood flow value in normal segments varied by a factor of 2·9/2·7 (manual/automatic segmentation). Within each patient, the average per segment blood flow in normal segments varied by a factor of 1·3-2·0/1·2-2·1 (manual/automatic segmentation).

    CONCLUSION: A positive but rather weak correlation was found between CTP and (99m) Tc SPECT. Large variations in CTP blood flow suggest that a cut-off value for stress myocardial blood flow is inadequate to detect ischaemic segments. Dynamic CTP is hampered by a limited coverage.

  • 5.
    De Geer, Jakob
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping.
    Sandborg, Michael
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping.
    Persson, Anders
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping.
    Post processing noise reduction as a way of reducing the dose in cardiac CT without sacrificing image quality: A Pilot study.2010In: European Congress of Radiology 2010, 2010Conference paper (Refereed)
  • 6.
    de Geer, Jakob
    et al.
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization, CMIV. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping.
    Sandborg, Michael
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics UHL. Linköping University, Center for Medical Image Science and Visualization, CMIV.
    Smedby, Örjan
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization, CMIV.
    Persson, Anders
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization, CMIV.
    The efficacy of 2D, non-linear noise reduction filtering in cardiac imaging: a pilot study2011In: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 52, no 7, p. 716-722Article in journal (Refereed)
    Abstract [en]

    Background: Computed tomography (CT) is becoming increasingly popular as a non-invasive method for visualizing the coronary arteries but patient radiation doses are still an issue. Postprocessing filters such as 2D adaptive non-linear filters might help to reduce the dose without loss of image quality. less thanbrgreater than less thanbrgreater thanPurpose: To investigate whether the use of a 2D, non-linear adaptive noise reduction filter can improve image quality in cardiac computed tomography angiography (CCTA). less thanbrgreater than less thanbrgreater thanMaterial and Methods: CCTA examinations were performed in 36 clinical patients on a dual source CT using two patient dose levels: maximum dose during diastole and reduced dose (20% of maximum dose) during systole. One full-dose and one reduced-dose image were selected from each of the examinations. The reduced-dose image was duplicated and one copy postprocessed using a 2D non-linear adaptive noise reduction filter, resulting in three images per patient. Image quality was assessed using visual grading with three criteria from the European guidelines for assessment of image quality and two additional criteria regarding the left main artery and the overall image quality. Also, the HU value and its standard deviation were measured in the ascending and descending aorta. Data were analyzed using Visual Grading Regression and paired t-test. less thanbrgreater than less thanbrgreater thanResult: For all five criteria, there was a significant (P andlt; 0.01 or better) improvement in perceived image quality when comparing postprocessed low-dose images with low-dose images without noise reduction. Comparing full dose images with postprocessed low-dose images resulted in a considerably larger, significant (P andlt; 0.001) difference. Also, there was a significant reduction of the standard deviation of the HU values in the ascending and descending aorta when comparing postprocessed low-dose images with low-dose images without postprocessing. less thanbrgreater than less thanbrgreater thanConclusion: Even with an 80% dose reduction, there was a significant improvement in the perceived image quality when using a 2D noise-reduction filter, though not approaching the quality of full-dose images. This indicates that cardiac CT examinations could benefit from noise-reducing postprocessing with 2D non-linear adaptive filters.

  • 7.
    De Geer, Jakob
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Sandstedt, Mårten
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Björkholm, Anders
    Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Alfredsson, Joakim
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Cardiology in Linköping.
    Janzon, Magnus
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Cardiology in Linköping.
    Engvall, Jan
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Persson, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Software-based on-site estimation of fractional flow reserve using standard coronary CT angiography data.2016In: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 57, no 10, p. 1186-1192Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: The significance of a coronary stenosis can be determined by measuring the fractional flow reserve (FFR) during invasive coronary angiography. Recently, methods have been developed which claim to be able to estimate FFR using image data from standard coronary computed tomography angiography (CCTA) exams.

    PURPOSE: To evaluate the accuracy of non-invasively computed fractional flow reserve (cFFR) from CCTA.

    MATERIAL AND METHODS: A total of 23 vessels in 21 patients who had undergone both CCTA and invasive angiography with FFR measurement were evaluated using a cFFR software prototype. The cFFR results were compared to the invasively obtained FFR values. Correlation was calculated using Spearman's rank correlation, and agreement using intraclass correlation coefficient (ICC). Sensitivity, specificity, accuracy, negative predictive value, and positive predictive value for significant stenosis (defined as both FFR ≤0.80 and FFR ≤0.75) were calculated.

    RESULTS: The mean cFFR value for the whole group was 0.81 and the corresponding mean invFFR value was 0.84. The cFFR sensitivity for significant stenosis (FFR ≤0.80/0.75) on a per-lesion basis was 0.83/0.80, specificity was 0.76/0.89, and accuracy 0.78/0.87. The positive predictive value was 0.56/0.67 and the negative predictive value was 0.93/0.94. The Spearman rank correlation coefficient was ρ = 0.77 (P < 0.001) and ICC = 0.73 (P < 0.001).

    CONCLUSION: This particular CCTA-based cFFR software prototype allows for a rapid, non-invasive on-site evaluation of cFFR. The results are encouraging and cFFR may in the future be of help in the triage to invasive coronary angiography.

  • 8.
    Engvall, Jan
    et al.
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Gjerde, Marcus
    Linköping University, Department of Medical and Health Sciences, Cardiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Cardiology in Linköping.
    de Geer, Jakob
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medical and Health Sciences, Radiology. Östergötlands Läns Landsting.
    Olsson, E.
    Östergötlands Läns Landsting.
    Quick, Petter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Persson, A.
    Östergötlands Läns Landsting. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences.
    Adenosine stress myocardial perfusion detected with CT compared with attenuation-corrected SPECT2011In: EUROPEAN HEART JOURNAL SUPPLEMENTS, Oxford University Press , 2011, Vol. 13, no A, p. A31-A31Conference paper (Refereed)
    Abstract [en]

    Purpose: To asses adenosine stress myocardial perfusion by cardiac CT and compare with simultaneously performed attenuation corrected SPECT.

    Methods: 11 patients, 9 men and 2 women >2months post primary PCI, with manifest myocardial damage and remaining stenoses in the coronary circulation, were studied with myocardial perfusion CT under vasodilatory stress. The investigation started with a topogram followed by a testbolus of iodine whereafter the coronary artery study was performed in sequence mode. Adenosine was then infused for at least five minutes at the standard rate of 140ug/kg/min. After three minutes, 6 MBq/kg of 99mTc-tetrofosmin was injected immediately followed by 80ml iodine contrast. The wash-in of iodine was monitored by CT scanning of a 7cm long cardiac volume segment every other second for 22s. One hour after the CT scan, myocardial SPECT was performed. Scanning required the patients to tolerate breath holding for 22s, have a heart rate <80/min and body weight <85kg, and their kidney function should allow 140ml 370mg iodine contrast to be given.

    Results: All 11 patients tolerated the full adenosine infusion and scanning was successful. One patient could not be analyzed due to noisy images. In two patients, the limited scanning volume did not cover the entire base of the heart. Three patients had no defect on SPECT. Patients with a defect had on average myocardial blood flow 80ml/100ml tissue/min in the defect area and 142ml in the segments with the highest perfusion, while patients without defect had 98 and 141ml, respectively.

    Conclusion: Peak myocardial perfusion may be determined with CT under adenosine stress and compared with attenuation corrected SPECT. Initial experience shows that the method is sensitive to timing of bolus, to noisy images and results may diverge from those obtained with SPECT.

  • 9.
    Eriksson, Per
    et al.
    Linköping University, Department of Medicine and Health Sciences, Internal Medicine . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medicine, Department of Nephrology UHL.
    Mohammed, Ahmed Abdulilah
    Linköping University, Department of Medicine and Health Sciences, Radiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping.
    De Geer, Jakob
    Linköping University, Department of Medicine and Health Sciences, Radiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization, CMIV.
    Kihlberg, Johan
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Radiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping.
    Persson, Anders
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Radiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping.
    Granerus, Göran
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Nyström, Fredrik
    Linköping University, Department of Medicine and Health Sciences, Internal Medicine . Linköping University, Faculty of Health Sciences.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Radiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping.
    Non-invasive investigations of potential renal artery stenosis in renal insufficiency2010In: Nephrology, Dialysis and Transplantation, ISSN 0931-0509, E-ISSN 1460-2385, Vol. 25, no 11, p. 3607-3614Article in journal (Refereed)
    Abstract [en]

    Background. The diagnostic value of non-invasive methods for diagnosing renal artery stenosis in patients with renal insufficiency is incompletely known.

    Methods. Forty-seven consecutive patients with moderately impaired renal function and a clinical suspicion of renal artery stenosis were investigated with computed tomography angiography (CTA), gadolinium-enhanced magnetic resonance angiography (MRA), contrast-enhanced Doppler ultrasound and captopril renography. The primary reference standard was stenosis reducing the vessel diameter by at least 50% on CTA, and an alternative reference standard (‘morphological and functional stenosis’) was defined as at least 50% diameter reduction on CTA or MRA, combined with a positive finding from ultrasound or captopril renography.

    Results. The frequency of positive findings, calculated on the basis of individual patients, was 70% for CTA, 60% for MRA, 53% for ultrasound and 30% for captopril renography. Counting kidneys rather than patients, corresponding frequencies were 53%, 41%, 29% and 15%, respectively. In relation to the CTA standard, the sensitivity (and specificity) at the patient level was 0.81 (0.79) for MRA, 0.70 (0.89) for ultrasound and 0.42 (1.00) for captopril renography, and at the kidney level 0.76 (0.82), 0.53 (0.81) and 0.30 (0.86), respectively. Relative to the alternative reference standard, corresponding values at the patient level were 1.00 (0.62) for CTA, 0.90 (0.69) for MRA, 0.91 (1.00) for ultrasound and 0.67 (1.00) for captopril renography, and at the kidney level 0.96 (0.76), 0.85 (0.79), 0.71 (0.97) and 0.50 (0.97), respectively.

    Conclusions. CTA and MRA are superior to ultrasound and captopril renography at diagnosing morphological stenosis, but ultrasound may be useful as a screening method and captopril renography for verifying renin-dependent hypertension.

  • 10.
    Kihlberg, Johan
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Faculty of Health Sciences.
    Kalra, Mannudeep
    Massachusetts General Hospital, Boston, USA.
    Dahlström, Nils
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Faculty of Health Sciences.
    De Geer, Jakob
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Faculty of Health Sciences.
    Rönn, Martin
    ContextVision, Linköping.
    Persson, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Faculty of Health Sciences.
    Olofsson, Fredrik
    ContextVision, Linköping.
    Bäck, Anni
    ContextVision, Linköping.
    Applying 2D and 3D Postprocessing Algorithms to MR Images: Does Image Quality Improve? Can MR Imaging Duration Be Reduced?2010In: In Proceedings of RSNA 2010, SSM22, 2010Conference paper (Refereed)
  • 11.
    Nous, Fay M. A.
    et al.
    Erasmus MC, Netherlands; Erasmus MC, Netherlands.
    Coenen, Adriaan
    Erasmus MC, Netherlands; Erasmus MC, Netherlands.
    Boersma, Eric
    Erasmus MC, Netherlands.
    Kim, Young-Hak
    Univ Ulsan, South Korea.
    Kruk, Mariusz B. P.
    Inst Cardiol, Poland.
    Tesche, Christian
    Med Univ South Carolina, SC 29425 USA.
    de Geer, Jakob
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Yang, Dong Hyun
    Univ Ulsan, South Korea; Univ Ulsan, South Korea.
    Kepka, Cezary
    Inst Cardiol, Poland.
    Schoepf, U. Joseph
    Univ Ulsan, South Korea.
    Persson, Anders
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Kurata, Akira
    Erasmus MC, Netherlands; Ehime Univ, Japan.
    Budde, Ricardo P. J.
    Erasmus MC, Netherlands; Erasmus MC, Netherlands.
    Nieman, Koen
    Erasmus MC, Netherlands; Erasmus MC, Netherlands; Stanford Univ, CA 94305 USA.
    Comparison of the Diagnostic Performance of Coronary Computed Tomography Angiography-Derived Fractional Flow Reserve in Patients With Versus Without Diabetes Mellitus (from the MACHINE Consortium)2019In: American Journal of Cardiology, ISSN 0002-9149, E-ISSN 1879-1913, Vol. 123, no 4, p. 537-543Article in journal (Refereed)
    Abstract [en]

    Coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) is a noninvasive application to evaluate the hemodynamic impact of coronary artery disease by simulating invasively measured FFR based on CT data. CT-FFR is based on the assumption of a normal coronary microvascular response. We assessed the diagnostic performance of a machine-learning based application for on-site computation of CT-FFR in patients with and without diabetes mellitus with suspected coronary artery disease. The study population included 75 diabetic and 276 nondiabetic patients who were enrolled in the MACHINE consortium. The overall diagnostic performance of coronary CT angiography alone and in combination with CT-FFR were analyzed with direct invasive FFR comparison in 110 coronary vessels of the diabetic group and in 415 coronary vessels of the nondiabetic group. Per-vessel discrimination of lesion-specific ischemia by CT-FFR was assessed by the area under the receiver operating characteristic curves. The overall diagnostic accuracy of CT-FFR in diabetic patients was 83% and in nondiabetic patients 75% (p = 0.088), showing improvement over the diagnostic accuracy of coronary CT angiography, which was 58% and 65% (p = 0.223), respectively. In addition, the diagnostic accuracy of CT-FFR was similar between diabetic and nondiabetic patients per stratified CT-FFR group (CT-FFR amp;lt; 0.6, 0.6 to 0.69, 0.7 to 0.79, 0.8 to 0.89, amp;gt;= 0.9). The area under the curves for diabetic and nondiabetic patients were also comparable, 0.88 and 0.82 (p = 0.113), respectively. In conclusion, on-site machine-learning CT-FFR analysis improved the diagnostic performance of coronary CT angiography and accurately discriminated lesion-specific ischemia in both diabetic and nondiabetic patients suspected of coronary artery disease. (C) 2018 Elsevier Inc. All rights reserved.

  • 12.
    Sandstedt, Mårten
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    De Geer, Jakob
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Henriksson, Lilian
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Engvall, Jan
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Janzon, Magnus
    Linköping University, Department of Medical and Health Sciences, Division of Health Care Analysis. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Cardiology in Linköping.
    Persson, Anders
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Alfredsson, Joakim
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Cardiology in Linköping.
    Long-term prognostic value of coronary computed tomography angiography in chest pain patients.2019In: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 60, no 1, p. 45-53Article in journal (Refereed)
    Abstract [en]

    Background Coronary computed tomography angiography (CCTA) is increasingly used to detect coronary artery disease (CAD), but long-term follow-up studies are still scarce. Purpose To evaluate the prognostic value of CCTA in patients with suspected CAD. Material and Methods A total of 1205 consecutive CCTA patients with chest pain were classified as normal coronary arteries, non-obstructive CAD, or obstructive CAD. The primary outcome was major adverse cardiac event (MACE), defined as a composite outcome including cardiac death, myocardial infarction, unstable angina pectoris, or late revascularization (after >90 days). Results Over 7.5 years follow-up (median = 3.1 years), Kaplan-Meier estimates demonstrated a MACE in 1.0%, 4.6%, and 20.7% in normal coronary arteries, non-obstructive CAD, and obstructive CAD, respectively. Log rank test for pairwise comparisons showed significant differences between non-obstructive CAD and normal coronary arteries ( P = 0.023) and between obstructive CAD and normal coronary arteries ( P < 0.001). In a multivariable analysis, adjusting for classical risk factors, non-obstructive CAD and obstructive CAD were independent predictors of MACE, with hazard ratios (HR) of 3.22 ( P = 0.041) and 25.18 ( P < 0.001), respectively. Conclusion Patients with normal coronary arteries have excellent long-term prognosis, but the risk for MACE increases with non-obstructive and obstructive CAD. Both non-obstructive and obstructive CAD are independently associated with future ischemic events.

  • 13.
    Smedby, Örjan
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Fredrikson, Mats
    Linköping University, Department of Clinical and Experimental Medicine, Occupational and Environmental Medicine. Linköping University, Faculty of Health Sciences.
    de Geer, Jakob
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences.
    Borgen, L
    Drammen Hospital, Norway .
    Sandborg, Michael
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Quantifying the potential for dose reduction with visual grading regression2013In: British Journal of Radiology, ISSN 0007-1285, E-ISSN 1748-880X, Vol. 86, no 1021Article in journal (Refereed)
    Abstract [en]

    Objectives To propose a method to study the effect of exposure settings on image quality and to estimate the potential for dose reduction when introducing dose-reducing measures.

    Methods Using the framework of visual grading regression (VGR), a log(mAs) term is included in the ordinal logistic regression equation, so that the effect of reducing the dose can be quantitatively related to the effect of adding post-processing. In the ordinal logistic regression, patient and observer identity are treated as random effects using generalised linear latent and mixed models. The potential dose reduction is then estimated from the regression coefficients. The method was applied in a single-image study of coronary CT angiography (CTA) to evaluate two-dimensional (2D) adaptive filters, and in an image-pair study of abdominal CT to evaluate 2D and three-dimensional (3D) adaptive filters.

    Results For five image quality criteria in coronary CTA, dose reductions of 16–26% were predicted when adding 2D filtering. Using five image quality criteria for abdominal CT, it was estimated that 2D filtering permits doses were reduced by 32–41%, and 3D filtering by 42–51%.

    Conclusions VGR including a log(mAs) term can be used for predictions of potential dose reduction that may be useful for guiding researchers in designing subsequent studies evaluating diagnostic value. With appropriate statistical analysis, it is possible to obtain direct numerical estimates of the dose-reducing potential of novel acquisition, reconstruction or post-processing techniques.

  • 14.
    Smedby, Örjan
    et al.
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Fredrikson, Mats
    Linköping University, Department of Clinical and Experimental Medicine, Occupational and Environmental Medicine. Linköping University, Faculty of Health Sciences.
    de Geer, Jakob
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences.
    Sandborg, Michael
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Quantifying effects of post-processing with visual grading regression2012In: Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment / [ed] Craig K. Abbey; Claudia R. Mello-Thoms, SPIE - International Society for Optical Engineering, 2012, Vol. 8318, p. Art. no. 83181N-Conference paper (Refereed)
    Abstract [en]

    For optimization and evaluation of image quality, one can use visual grading experiments, where observers rate some aspect of image quality on an ordinal scale. To take into account the ordinal character of the data, ordinal logistic regression is used in the statistical analysis, an approach known as visual grading regression (VGR). In the VGR model one may include factors such as imaging parameters and post-processing procedures, in addition to patient and observer identity. In a single-image study, 9 radiologists graded 24 cardiac CTA images acquired with ECG-modulated tube current using standard settings (310 mAs), reduced dose (62 mAs) and reduced dose after post-processing. Image quality was assessed using visual grading with five criteria, each with a five-level ordinal scale from 1 (best) to 5 (worst). The VGR model included one term estimating the dose effect (log of mAs setting) and one term estimating the effect of postprocessing. The model predicted that 115 mAs would be required to reach an 80% probability of a score of 1 or 2 for visually sharp reproduction of the heart without the post-processing filter. With the post-processing filter, the corresponding figure would be 86 mAs. Thus, applying the post-processing corresponded to a dose reduction of 25%. For other criteria, the dose-reduction was estimated to 16-26%. Using VGR, it is thus possible to quantify the potential for dose-reduction of post-processing filters.

  • 15.
    Smedby, Örjan
    et al.
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Fredrikson, Mats
    Linköping University, Department of Clinical and Experimental Medicine, Occupational and Environmental Medicine. Linköping University, Faculty of Health Sciences.
    de Geer, Jakob
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences.
    Sandborg, Michael
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Visual grading regression with random effects2012In: MEDICAL IMAGING 2012: IMAGE PERCEPTION, OBSERVER PERFORMANCE, AND TECHNOLOGY ASSESSMENT, SPIE - International Society for Optical Engineering, 2012, Vol. 8318, p. Art. no. 831805-Conference paper (Refereed)
    Abstract [en]

    To analyze visual grading experiments, ordinal logistic regression (here called visual grading regression, VGR) may be used in the statistical analysis. In addition to types of imaging or post-processing, the VGR model may include factors such as patient and observer identity, which should be treated as random effects. Standard software does not allow random factors in ordinal logistic regression, but using Generalized Linear Latent And Mixed Models (GLLAMM) this is possible. In a single-image study, 9 radiologists graded 24 cardiac Computed Tomography Angiography (CTA) images with reduced dose without and after post-processing with a 2D adaptive filter, using five image quality criteria. First, standard ordinal logistic regression was carried out, treating filtering, patient and observer identity as fixed effects. The same analysis was then repeated with GLLAMM, treating filtering as a fixed effect and patient and observer identity as random effects. With both approaches, a significant effect (pless than0.01) of the filtering was found for all five criteria. No dramatic differences in parameter estimates or significance levels were found between the two approaches. It is concluded that random effects can be appropriately handled in VGR using GLLAMM, but no major differences in the results were found in a preliminary evaluation.

  • 16.
    Wang, Chunliang
    et al.
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization, CMIV.
    Persson, Anders
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization, CMIV.
    Engvall, Jan
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Centre, Department of Clinical Physiology UHL.
    de Geer, Jakob
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medical and Health Sciences, Radiology.
    Björkholm, Anders
    Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping.
    Czekierda, Waldemar
    Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping.
    Fransson, Sven Göran
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences.
    Smedby, Örjan
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping.
    Can segmented 3D images be used for stenosis evaluation in coronary CT angiography?2012In: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 53, no 8, p. 845-851Article in journal (Refereed)
    Abstract [en]

    Purpose: To retrospectively evaluate the diagnostic accuracy of coronary CT angiography (CCTA) using segmented 3D data for the detection of significant stenoses with catheter angiography (CA) as the reference standard.

    Method: CCTA data sets from 30 patients were acquired with a 64-slice dual source CT scanner and segmented by an independent observer using the region growing (RG) method and the “virtual contrast injection” (VC) method. For every examination, each of the three types of images was  then reviewed by one of three reviewers in a blinded fashion for the presence of stenoses with diameter reduction of 50% or more. For the original series, the reviewer was allowed to use all the 2D or 3D visualization tools available (mixed method). For the segmented results (from RG and VC), the reviewer only used the 3D maximum intensity projection. Evaluation results were compared with CA for each artery.

    Results: Overall, 34 arteries with significant stenosis were identified by CA. The percentage of evaluable arteries, accuracy and negative predictive value (NPV) for detecting stenosis were, respectively, 86%, 74% and 93% for the mixed method, 83%, 71% and 92% for VC, and 64%, 56% and 93% for RG. Accuracy was significantly lower for the RG method than for the other two methods (p<0.01), whereas there was no significant difference in accuracy between the VC method and the mixed method (p = 0.22). Excluding vessels with heavy calcification, all three methods had similar accuracy.

    Conclusion: Diagnostic accuracy when using segmented 3D data was lower than with access to 2D images. However, the high NPV of the 3D methods suggests a potential of using them as an initial step, with access to 2D reviewing techniques for suspected lesions and cases with heavy calcification. The VC method, which generates more evaluable arteries and has higher accuracy, seems more promising for this purpose than the RG method.

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