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Bustamante, Mariana
Publications (3 of 3) Show all publications
Bustamante, M. (2018). Automated Assessment of Blood Flow in the Cardiovascular System Using 4D Flow MRI. (Doctoral dissertation). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Automated Assessment of Blood Flow in the Cardiovascular System Using 4D Flow MRI
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Medical image analysis focuses on the extraction of meaningful information from medical images in order to facilitate clinical assessment, diagnostics and treatment. Image processing techniques have gradually become an essential part of the modern health care system, a consequence of the continuous technological improvements and the availability of a variety of medical imaging techniques.

Magnetic Resonance Imaging (MRI) is an imaging technique that stands out as non-invasive, highly versatile, and capable of generating high quality images without the use of ionizing radiation. MRI is frequently performed in the clinical setting to assess the morphology and function of the heart and vessels. When focusing on the cardiovascular system, blood flow visualization and quantification is essential in order to fully understand and identify related pathologies. Among the variety of MR techniques available for cardiac imaging, 4D Flow MRI allows for full three-dimensional spatial coverage over time, also including three-directional velocity information. It is a very powerful technique that can be used for retrospective analysis of blood flow dynamics at any location in the acquired volume.

In the clinical routine, however, flow analysis is typically done using two-dimensional imaging methods. This can be explained by their shorter acquisition times, higher in-plane spatial resolution and signal-to-noise ratio, and their relatively simpler post-processing requirements when compared to 4D Flow MRI. The extraction of useful knowledge from 4D Flow MR data is especially challenging due to the large amount of information included in these images, and typically requires substantial user interaction.

This thesis aims to develop and evaluate techniques that facilitate the post-processing of thoracic 4D Flow MRI by automating the steps necessary to obtain hemodynamic parameters of interest from the data. The proposed methods require little to no user interaction, are fairly quick, make effective use of the information available in the four-dimensional images, and can easily be applied to sizable groups of data.The addition of the proposed techniques to the current pipeline of 4D Flow MRI analysis simplifies and expedites the assessment of these images, thus bringing them closer to the clinical routine.

Abstract [sv]

Medicinsk bildanalys fokuserar på extrahering av meningsfull information från medicinska bilder för att underlätta klinisk bedömning, diagnostik, och behandling. Bildbehandlingsteknik har gradvis blivit en viktig del av det moderna sjukvårdsystemet, en följd av de kontinuerliga tekniska förbättringarna och tillgången till en mängd olika medicinska bildtekniker.

Magnetic resonanstomografi (MRT) är en bildteknik som är ickeinvasiv, flexibel och kan generera bilder av hög kvalitet utan joniserande strålning. MRT utförs ofta i klinisk miljö för att bedöma anatomi och funktion av hjärtat och blodkärlen. När man fokuserar på hjärt-kärlsystemet är bedömning av blodflödet viktigt för att kunna förstå och identifiera sjukdomar fullt ut. Bland de olika MRT-teknikerna som är tillgängliga för avbildning av hjärtat möjliggör 4D flödes-MRT komplett täckning av hjärtat i tre dimensioner över tid, och med hastighetsinformation i tre riktningar. 4D flödes-MRT är en mycket effektiv metod som kan användas för retrospektiv analys av blodflödesdynamik på vilken position som helst i den avbildade volymen.

Till vardags görs dock blodflödesanalysen vanligtvis på bilder tagna med tvådimensionella avbildningsmetoder. Detta kan förklaras av deras kortare insamlingstider, högre spatiella upplösning, bättre signal-brusförhållandet, och att de är relativt enklare att efterbehandla jämfört med 4D flödes-MRT. Utvinningen av användbar information från 4D flödes-MRT-data är väldigt utmanande på grund av den stora mängden information som dessa bilder innehåller och kräver vanligtvis väsentlig användarinteraktion.

Denna avhandling syftar till att utveckla och utvärdera metoder som underlättar efterbehandlingen av 4D flödes-MRT genom att automatisera de steg som är nödvändiga för att härleda hemodynamiska parametrarna av intresse från dessa data. De föreslagna metoderna kräver liten eller ingen användarinteraktion, är relativt snabba, använder all information som finns i de fyrdimensionella bilderna, och kan enkelt appliceras på stora datamängder. Tillägget av de i avhandlingen beskrivna metoderna till den nuvarande analysen av 4D flödes-MRT medger en avsevärd förenkling och uppsnabbad utvärdering, vilket gör att den avancerade 4D flödes MRT-tekniken kommer närmare att kunna användas i kliniskt rutinarbete.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2018. p. 77
Linköping University Medical Dissertations, ISSN 0345-0082 ; 1613
MRI, 4D Flow MRI, Image Analysis, Segmentation
National Category
Medical Image Processing
urn:nbn:se:liu:diva-145729 (URN)10.3384/diss.diva-145729 (DOI)9789176853467 (ISBN)
Public defence
2018-05-03, Eken, Building 421, Floor 9, Entrance 65, Campus US, Linköping, 13:00 (English)
EU, European Research Council, 310612EU, European Research Council, 223615Swedish Research Council, 621-2014-6191Swedish Heart Lung Foundation, 20140398Wallenberg Foundations, KAW 2013.0076
Available from: 2018-03-23 Created: 2018-03-22 Last updated: 2019-09-30Bibliographically approved
Bustamante, M., Gupta, V., Forsberg, D., Carlhäll, C., Engvall, J. & Ebbers, T. (2018). Automated multi-atlas segmentation of cardiac 4D flow MRI. Medical Image Analysis, 49, 128-140
Open this publication in new window or tab >>Automated multi-atlas segmentation of cardiac 4D flow MRI
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2018 (English)In: Medical Image Analysis, ISSN 1361-8415, E-ISSN 1361-8423, Vol. 49, p. 128-140Article in journal (Refereed) Published
Abstract [en]

Four-dimensional (4D) flow magnetic resonance imaging (4D Flow MRI) enables acquisition of time-resolved three-directional velocity data in the entire heart and all major thoracic vessels. The segmentation of these tissues is typically performed using semi-automatic methods. Some of which primarily rely on the velocity data and result in a segmentation of the vessels only during the systolic phases. Other methods, mostly applied on the heart, rely on separately acquired balanced Steady State Free Precession (b-SSFP) MR images, after which the segmentations are superimposed on the 4D Flow MRI. While b-SSFP images typically cover the whole cardiac cycle and have good contrast, they suffer from a number of problems, such as large slice thickness, limited coverage of the cardiac anatomy, and being prone to displacement errors caused by respiratory motion. To address these limitations we propose a multi-atlas segmentation method, which relies only on 4D Flow MRI data, to automatically generate four-dimensional segmentations that include the entire thoracic cardiovascular system present in these datasets. The approach was evaluated on 4D Flow MR datasets from a cohort of 27 healthy volunteers and 83 patients with mildly impaired systolic left-ventricular function. Comparison of manual and automatic segmentations of the cardiac chambers at end-systolic and end-diastolic timeframes showed agreements comparable to those previously reported for automatic segmentation methods of b-SSFP MR images. Furthermore, automatic segmentation of the entire thoracic cardiovascular system improves visualization of 4D Flow MRI and facilitates computation of hemodynamic parameters.

Place, publisher, year, edition, pages
Elsevier, 2018
4D Flow MRI, Cardiac segmentation, Multi-atlas segmentation, Heart, Magnetic resonance imaging, Automatic segmentations, Directional velocities, Hemodynamic parameters, Left ventricular function, Segmentation methods, Semiautomatic methods, Steady state free precessions, Image segmentation, adult, anatomy, article, cohort analysis, controlled study, error, female, heart cycle, heart left ventricle function, human, human tissue, major clinical study, male, motion, nuclear magnetic resonance imaging, steady state, thickness, volunteer
National Category
Medical Image Processing
urn:nbn:se:liu:diva-150788 (URN)10.1016/ (DOI)000446286600011 ()30144652 (PubMedID)2-s2.0-85051830661 (Scopus ID)

Funding details: 310612; Funding details: FP7, Seventh Framework Programme; Funding details: 621-2014-6191, VR, Vetenskapsrådet; Funding details: 223615; Funding details: 20140398; Funding text: This work was partially funded by the FP7-funded project DOPPLER-CIP [grant number 223615]; the European Union’s Seventh Framework Programme ( FP7/2007-2013 ) [grant number 310612 ]; the Swedish Research Council [grant number 621-2014-6191 ]; and the Swedish Heart and Lung Foundation [grant number 20140398 ]. 

Available from: 2018-08-31 Created: 2018-08-31 Last updated: 2018-10-17Bibliographically approved
Bustamante, M., Petersson, S., Eriksson, J., Alehagen, U., Dyverfeldt, P., Carlhäll, C. & Ebbers, T. (2015). Atlas-based analysis of 4D flow CMR: Automated vessel segmentation and flow quantification. Journal of Cardiovascular Magnetic Resonance, 17(87)
Open this publication in new window or tab >>Atlas-based analysis of 4D flow CMR: Automated vessel segmentation and flow quantification
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2015 (English)In: Journal of Cardiovascular Magnetic Resonance, ISSN 1097-6647, E-ISSN 1532-429X, Vol. 17, no 87Article in journal (Refereed) Published
Abstract [en]

Background: Flow volume quantification in the great thoracic vessels is used in the assessment of several cardiovascular diseases. Clinically, it is often based on semi-automatic segmentation of a vessel throughout the cardiac cycle in 2D cine phase-contrast Cardiovascular Magnetic Resonance (CMR) images. Three-dimensional (3D), time-resolved phase-contrast CMR with three-directional velocity encoding (4D flow CMR) permits assessment of net flow volumes and flow patterns retrospectively at any location in a time-resolved 3D volume. However, analysis of these datasets can be demanding. The aim of this study is to develop and evaluate a fully automatic method for segmentation and analysis of 4D flow CMR data of the great thoracic vessels. Methods: The proposed method utilizes atlas-based segmentation to segment the great thoracic vessels in systole, and registration between different time frames of the cardiac cycle in order to segment these vessels over time. Additionally, net flow volumes are calculated automatically at locations of interest. The method was applied on 4D flow CMR datasets obtained from 11 healthy volunteers and 10 patients with heart failure. Evaluation of the method was performed visually, and by comparison of net flow volumes in the ascending aorta obtained automatically (using the proposed method), and semi-automatically. Further evaluation was done by comparison of net flow volumes obtained automatically at different locations in the aorta, pulmonary artery, and caval veins. Results: Visual evaluation of the generated segmentations resulted in good outcomes for all the major vessels in all but one dataset. The comparison between automatically and semi-automatically obtained net flow volumes in the ascending aorta resulted in very high correlation (r(2) = 0.926). Moreover, comparison of the net flow volumes obtained automatically in other vessel locations also produced high correlations where expected: pulmonary trunk vs. proximal ascending aorta (r(2) = 0.955), pulmonary trunk vs. pulmonary branches (r(2) = 0.808), and pulmonary trunk vs. caval veins (r(2) = 0.906). Conclusions: The proposed method allows for automatic analysis of 4D flow CMR data, including vessel segmentation, assessment of flow volumes at locations of interest, and 4D flow visualization. This constitutes an important step towards facilitating the clinical utility of 4D flow CMR.

Place, publisher, year, edition, pages
4D flow cardiovascular magnetic resonance (4D flow CMR); Flow volume; Image segmentation; Image registration; Phase contrast
National Category
Clinical Medicine
urn:nbn:se:liu:diva-122197 (URN)10.1186/s12968-015-0190-5 (DOI)000362164100002 ()26438074 (PubMedID)

Funding Agencies|Swedish Heart and Lung foundation; Swedish Research Council; European Research Council (HEART4FLOW) [310612]

Available from: 2015-10-26 Created: 2015-10-23 Last updated: 2018-03-22

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