Non-linear properties of iterative reconstruction (IR) algorithms can alter image texture. We evaluated the effect of a model-basedIR algorithm (advanced modelled iterative reconstruction; ADMIRE) and dose on computed tomography thorax image quality.Dual-source scanner data were acquired at 20, 45 and 65 reference mAs in 20 patients. Images reconstructed with filteredback projection (FBP) and ADMIRE Strengths 3–5 were assessed independently by six radiologists and analysed using an ordinallogistic regression model. For all image criteria studied, the effects of tube load 20 mAs and all ADMIRE strengths were significant(p < 0.001) when compared to reference categories 65 mAs and FBP. Increase in tube load from 45 to 65 mAs showed imagequality improvement in three of six criteria. Replacing FBP with ADMIRE significantly improves perceived image quality for allcriteria studied, potentially permitting a dose reduction of almost 70% without loss in image quality

Objectives: Images reconstructed with higher strengths of iterative reconstruction algorithms may impair radiologists’ subjective perception and diagnostic performance due to changes in the amplitude of different spatial frequencies of noise. The aim of the present study was to ascertain if radiologists can learn to adapt to the unusual appearance of images produced by higher strengths of Advanced modeled iterative reconstruction algorithm (ADMIRE).

Methods:Two previously published studies evaluated the performance of ADMIRE in non-contrast and contrast-enhanced abdominal CT. Images from 25 (first material) and 50 (second material) patients, were reconstructed with ADMIRE strengths 3, 5 (AD3, AD5) and filtered back projection (FBP). Radiologists assessed the images using image criteria from the European guidelines for quality criteria in CT. To ascertain if there was a learning effect, new analyses of data from the two studies was performed by introducing a time variable in the mixed-effects ordinal logistic regression model.

Results: In both materials, a significant negative attitude to ADMIRE 5 at the beginning of the viewing was strengthened during the progress of the reviews for both liver parenchyma (first material: −0.70, p < 0.01, second material: −0.96, p < 0.001) and overall image quality (first material:−0.59, p < 0.05, second material::−1.26, p < 0.001). For ADMIRE 3, an early positive attitude for the algorithm was noted, with no significant change over time for all criteria except one (overall image quality), where a significant negative trend over time (−1.08, p < 0.001) was seen in the second material.

Conclusions: With progression of reviews in both materials, an increasing dislike for ADMIRE 5 images was apparent for two image criteria. In this time perspective (weeks or months), no learning effect towards accepting the algorithm could be demonstrated.

Traditional filtered back projection (FBP) reconstruction methods have served the computed tomography (CT) community wellfor over 40 years. With the increased use of CT during the last decades, efforts to minimise patient exposure, while maintainingsufficient or improved image quality, have led to the development of model-based iterative reconstruction (MBIR) algorithms fromseveral vendors. The usefulness of the advanced modeled iterative reconstruction (ADMIRE) (Siemens Healthineers) MBIR inabdominal CT is reviewed and its noise suppression and/or dose reduction possibilities explored. Quantitative and qualitativemethods with phantom and human subjects were used. Assessment of the quality of phantom images will not always correlatepositively with those of patient images, particularly at the higher strength of the ADMIRE algorithm. With few exceptions,ADMIRE Strength 3 typically allows for substantial noise reduction compared to FBP and hence to significant (≈30%) patientdose reductions. The size of the dose reductions depends on the diagnostic task.

Malignant gliomas are primary brain tumours with an infiltrative growth pattern, often with contrast enhancement on magnetic resonance imaging (MRI). However, it is well known that tumour infiltration extends beyond the visible contrast enhancement. The aim of this study was to investigate if there is contrast enhancement not detected visually in the peritumoral oedema of malignant gliomas by using relaxometry with synthetic MRI. 25 patients who had brain tumours with a radiological appearance of malignant glioma were prospectively included. A quantitative MR-sequence measuring longitudinal relaxation (R₁), transverse relaxation (R₂) and proton density (PD), was added to the standard MRI protocol before surgery. Five patients were excluded, and in 20 patients, synthetic MR images were created from the quantitative scans. Manual regions of interest (ROIs) outlined the visibly contrast-enhancing border of the tumours and the peritumoral area. Contrast enhancement was quantified by subtraction of native images from post GD-images, creating an R₁-difference-map. The quantitative R₁-difference-maps showed significant contrast enhancement in the peritumoral area (0.047) compared to normal appearing white matter (0.032), p = 0.048. Relaxometry detects contrast enhancement in the peritumoral area of malignant gliomas. This could represent infiltrative tumour growth.

Background

Our aim was to compare CT images from native, nephrographic and excretory phases using image quality criteria as well as the detection of positive pathological findings in CT Urography, to explore if the radiation burden to the younger group of patients or patients with negative outcomes can be reduced.

Methods

This is a retrospective study of 40 patients who underwent a CT Urography examination on a 192-slice dual source scanner. Image quality was assessed for four specific renal image criteria from the European guidelines, together with pathological assessment in three categories: renal, other abdominal, and incidental findings without clinical significance. Each phase was assessed individually by three radiologists with varying experience using a graded scale. Certainty scores were derived based on the graded assessments. Statistical analysis was performed using visual grading regression (VGR). The limit for significance was set at p = 0.05.

Results

For visual reproduction of the renal parenchyma and renal arteries, the image quality was judged better for the nephrogram phase (p < 0.001), whereas renal pelvis/calyces and proximal ureters were better reproduced in the excretory phase compared to the native phase (p < 0.001). Similarly, significantly higher certainty scores were obtained in the nephrogram phase for renal parenchyma and renal arteries, but in the excretory phase for renal pelvis/calyxes and proximal ureters. Assessment of pathology in the three categories showed no statistically significant differences between the three phases. Certainty scores for assessment of pathology, however, showed a significantly higher certainty for renal pathology when comparing the native phase to nephrogram and excretory phase and a significantly higher score for nephrographic phase but only for incidental findings.

Conclusion

Visualisation of renal anatomy was as expected with each post-contrast phase showing favourable scores compared to the native phase. No statistically significant differences in the assessment of pathology were found between the three phases. The low-dose CT (LDCT) seems to be sufficient in differentiating between normal and pathological examinations. To reduce the radiation burden in certain patient groups, the LDCT could be considered a suitable alternative as a first line imaging method. However, radiologists should be aware of its limitations.

Purpose To estimate potential dose reduction in abdominal CT by visually comparing images reconstructed with filtered back projection (FBP) and strengths of 3 and 5 of a specific MBIR.

Material and methods A dual-source scanner was used to obtain three data sets each for 50 recruited patients with 30, 70 and 100% tube loads (mean CTDIvol 1.9, 3.4 and 6.2 mGy). Six image criteria were assessed independently by five radiologists. Potential dose reduction was estimated with Visual Grading Regression (VGR).

Results Comparing 30 and 70% tube load, improved image quality was observed as a significant strong effect of log tube load and reconstruction method with potential dose reduction relative to FBP of 22–47% for MBIR strength 3 (p < 0.001). For MBIR strength 5 no dose reduction was possible for image criteria 1 (liver parenchyma), but dose reduction between 34 and 74% was achieved for other criteria. Interobserver reliability showed agreement of 71–76% (κw 0.201–0.286) and intra-observer reliability of 82–96% (κw 0.525–0.783).

Conclusion MBIR showed improved image quality compared to FBP with positive correlation between MBIR strength and increasing potential dose reduction for all but one image criterion.

Trabecular bone structure has been shown to impact bone strength and fracture risk. In vitro, this structure can be measured by micro-computed tomography (micro-CT). For clinical use, it would be valuable if multi-slice computed tomography (MSCT) could be used to analyse trabecular bone structure. One important step in the analysis is image volume segmentation. Previous segmentation techniques have either been computer resource intensive or produced suboptimal results when used on MSCT data. This paper proposes a new segmentation method that tries to balance good results against computational complexity.

The accuracy of the analyses for studying the three dimensionaltrabecular bone microstructure rely on the quality of the segmentationbetween trabecular bone and bone marrow. Such segmentationis challenging for images from computed tomography modalities thatcan be used in vivo due to their low contrast and resolution. For thispurpose, we propose in this paper a granulometry-based segmentationmethod. In a first step, the trabecular thickness is estimated by usingthe granulometry in gray scale, which is generated by applying the openingmorphological operation with ball-shaped structuring elements ofdifferent diameters. This process mimics the traditional sphere-fittingmethod used for estimating trabecular thickness in segmented images.The residual obtained after computing the granulometry is comparedto the original gray scale value in order to obtain a measurement ofhow likely a voxel belongs to trabecular bone. A threshold is applied toobtain the final segmentation. Six histomorphometric parameters werecomputed on 14 segmented bone specimens imaged with cone-beam computedtomography (CBCT), considering micro-computed tomography(micro-CT) as the ground truth. Otsu’s thresholding and AutomatedRegion Growing (ARG) segmentation methods were used for comparison.For three parameters (Tb.N, Tb.Th and BV/TV), the proposedsegmentation algorithm yielded the highest correlations with micro-CT,while for the remaining three (Tb.Nd, Tb.Tm and Tb.Sp), its performancewas comparable to ARG. The method also yielded the strongestaverage correlation (0.89). When Tb.Th was computed directly fromthe gray scale images, the correlation was superior to the binary-basedmethods. The results suggest that the proposed algorithm can be usedfor studying trabecular bone in vivo through CBCT.

Vascular diseases are a common cause of death, particularly in developed countries. Computerized image analysis tools play a potentially important role in diagnosing and quantifying vascular pathologies. Given the size and complexity of modern angiographic data acquisition, fast, automatic and accurate vascular segmentation is a challenging task. In this paper we introduce a fully automatic high-speed vascular skeleton extraction algorithm that is intended as a first step in a complete vascular tree segmentation program. The method takes a 3D unprocessed Computed Tomography Angiography (CTA) scan as input and produces a graph in which the nodes are centrally located artery voxels and the edges represent connections between them. The algorithm works in two passes where the first pass is designed to extract the skeleton of large arteries and the second pass focuses on smaller vascular structures. Each pass consists of three main steps. The first step sets proper parameters automatically using Gaussian curve fitting. In the second step different filters are applied to detect voxels nodes - that are part of arteries. In the last step the nodes are connected in order to obtain a continuous centerline tree for the entire vasculature. Structures found, that do not belong to the arteries, are removed in a final anatomy-based analysis. The proposed method is computationally efficient with an average execution time of 29 s and has been tested on a set of CTA scans of the lower limbs achieving an average overlap rate of 97% and an average detection rate of 71%. (C) 2015 Elsevier B.V. All rights reserved.

In this study we present a non-rigid point set registration for 3D curves (composed by 3D set of points). Themethod was evaluated in the task of registration of 3D superficial vessels of the brain where it was used to matchvessel centerline points. It consists of a combination of the Coherent Point Drift (CPD) and the Thin-PlateSpline (TPS) semilandmarks. The CPD is used to perform the initial matching of centerline 3D points, whilethe semilandmark method iteratively relaxes/slides the points.

For the evaluation, a Magnetic Resonance Angiography (MRA) dataset was used. Deformations were appliedto the extracted vessels centerlines to simulate brain bulging and sinking, using a TPS deformation where afew control points were manipulated to obtain the desired transformation (T1). Once the correspondences areknown, the corresponding points are used to define a new TPS deformation(T2). The errors are measured in thedeformed space, by transforming the original points using T1 and T2 and measuring the distance between them.To simulate cases where the deformed vessel data is incomplete, parts of the reference vessels were cut and thendeformed. Furthermore, anisotropic normally distributed noise was added.

The results show that the error estimates (root mean square error and mean error) are below 1 mm, even inthe presence of noise and incomplete data.