CT-3DFlow : Leveraging 3D Normalizing Flows for Unsupervised Detection of Pathological Pulmonary CT scans

Unsupervised pathology detection can be implemented by training a model on healthy data only and measuring the deviation from the training set upon inference, for example with CNN-based feature extraction and one-class classifiers, or reconstruction-score-based methods such as AEs, GANs and Diffusion models. Normalizing Flows (NF) have the ability to directly learn the probability distribution of training examples through an invertible architecture. We leverage this property in a novel 3D NF-based model named CT-3DFlow, specifically tailored for patient-level pulmonary pathology detection in chest CT data. Our model is trained unsupervised on healthy 3D pulmonary CT patches, and detects deviations from its log-likelihood distribution as anomalies. We aggregate patches-level likelihood values from a patient's CT scan to provide a patient-level 'normal'/'abnormal' prediction. Out-of-distribution detection performance is evaluated using expert annotations on a separate chest CT test dataset, outperforming other state-of-the-art methods

Effect of contrast material injection protocol on first-pass myocardial perfusion assessed by dual-energy dual-layer computed tomography

Background: Dual-energy dual-layer computed tomography (CT) scanners can provide useful tools, such as iodine maps and virtual monochromatic images (VMI), for the evaluation of myocardial perfusion defects. Data about the influence of acquisition protocols and normal values are still lacking. Methods: Clinically indicated coronary CT-angiographies performed between January-October 2018 in a single university hospital with dual-energy dual-layer CT (DE-DLCT) and different injection protocols were retrospectively evaluated. The two protocols were: 35 mL in patients <80 kg and 0.5 mL/kg in patients >80 kg at 2.5 mL/sec (group A) or double contrast dose at 5 mL/sec (group B). Patients with coronary stenosis >50% were excluded. Regions of interest were manually drawn on 16 myocardial segments and iodine concentration was measured in mg/mL. Signal-to-noise, contrast-to-noise ratios (CNR) and image noise were measured on conventional images and VMI. Results: A total of 30 patients were included for each protocol. With iodine concentrations of 1.38 +/- 0.41 mg/mL for protocol A and 2.07 +/- 0.73 mg/mL for protocol B, the two groups were significantly different (P<0.001). No significant iodine concentration differences were found between the 16 segments (P=0.47 and P=0.09 for group A and B respectively), between basal, mid and apical segments for group A and B (P=0.28 and P=0.12 for group A and B respectively) and between wall regions for group A (P=0.06 on normalised data). In group B, iodine concentration was significantly different between three wall regions [highest values for the lateral wall, median =2.03 (1.06) mg/mL]. Post-hoc analysis showed highest contrast-to-noise and signal-to-noise in VMI at 40 eV (P<0.05). Conclusions: Iodine concentration in left ventricular myocardium of patients without significant coronary artery stenosis varied depending on the injection protocol and appeared more heterogeneous in different wall regions at faster injection rate and greater iodine load. Signal-to-noise and contrast-to-noise gradually improved when decreasing VMI energy, although at the expenses of higher noise, demonstrating the potential of DE-DLCT to enhance objective image quality

Reduced-iodine-dose dual-energy coronary CT angiography: qualitative and quantitative comparison between virtual monochromatic and polychromatic CT images.

To quantitatively evaluate the impact of virtual monochromatic images (VMI) on reduced-iodine-dose dual-energy coronary computed tomography angiography (CCTA) in terms of coronary lumen segmentation in vitro, and secondly to assess the image quality in vivo, compared with conventional CT obtained with regular iodine dose. A phantom simulating regular and reduced iodine injection was used to determine the accuracy and precision of lumen area segmentation for various VMI energy levels. We retrospectively included 203 patients from December 2017 to August 2018 (mean age, 51.7 ± 16.8 years) who underwent CCTA using either standard (group A, n = 103) or reduced (group B, n = 100) iodine doses. Conventional images (group A) were qualitatively and quantitatively compared with 55-keV VMI (group B). We recorded the location of venous catheters. In vitro, VMI outperformed conventional CT, with a segmentation accuracy of 0.998 vs. 1.684 mm <sup>2</sup> , respectively (p < 0.001), and a precision of 0.982 vs. 1.229 mm <sup>2</sup> , respectively (p < 0.001), in simulated overweight adult subjects. In vivo, the rate of diagnostic CCTA in groups A and B was 88.4% (n = 91/103) vs. 89% (n = 89/100), respectively, and noninferiority of protocol B was inferred. Contrast-to-noise ratios (CNR) of lumen versus fat and muscle were higher in group B (p < 0.001) and comparable for lumen versus calcium (p = 0.423). Venous catheters were more often placed on the forearm or hand in group B (p < 0.001). In vitro, low-keV VMI improve vessel area segmentation. In vivo, low-keV VMI allows for a 40% iodine dose and injection rate reduction while maintaining diagnostic image quality and improves the CNR between lumen versus fat and muscle. • Dual-energy coronary CT angiography is becoming increasingly available and might help improve patient management. • Compared with regular-iodine-dose coronary CT angiography, reduced-iodine-dose dual-energy CT with low-keV monochromatic image reconstructions performed better in phantom-based vessel cross-sectional segmentation and proved to be noninferior in vivo. • Patients receiving reduced-iodine-dose dual-energy coronary CT angiography often had the venous catheter placed on the forearm or wrist without compromising image quality

Determination of the myocardial area at risk after reperfused acute myocardial infarction with different imaging techniques: cardiac magnetic resonance imaging, multidetector computed tomography and histopathological validation

International audiencen.

Ultra-High-Resolution and K-Edge Imaging of Prosthetic Heart Valves With Spectral Photon-Counting CT:A Phantom Study

Background and Purpose: The contribution of cardiac computed tomography (CT) for the detection and characterization of prosthetic heart valve (PHV) complications is still limited due mainly to artifacts. Computed tomography systems equipped with photon-counting detectors (PCDs) have the potential to overcome these limitations. Therefore, the aim of the study was to compare image quality of PHV with PCD-CT and dual-energy dual-layer CT (DEDL-CT). Materials and Methods: Two metallic and 3 biological PHVs were placed in a tube containing diluted iodinated contrast inside a thoracic phantom and scanned repeatedly at different angles on a DEDL-CT and PCD-CT. Two small lesions (∼2 mm thickness; containing muscle and fat, respectively) were attached to the structure of 4 valves, placed inside the thoracic phantom, with and without an extension ring, and scanned again. Acquisition parameters were matched for the 2 CT systems and used for all scans. Metallic valves were scanned again with parameters adapted for tungsten K-edge imaging. For all valves, different metallic parts were measured on conventional images to assess their thickness and blooming artifacts. In addition, 6 parallelepipeds per metallic valve were drawn, and all voxels with density &lt;3 times the standard deviation of the contrast media were recorded as an estimate of streak artifacts. For subjective analysis, 3 expert readers assessed conventional images of the valves, with and without lesions, and tungsten K-edge images. Conspicuity and sharpness of the different parts of the valve, the lesions, metallic, and blooming artifacts were scored on a 4-point scale. Measurements and scores were compared with the paired t test or Wilcoxon test. Results: The objective analysis showed that, with PCD-CT, valvular metallic structures were thinner and presented less blooming artifacts. Metallic artifacts were also reduced with PCD-CT (11 [interquartile (IQ) = 6] vs 40 [IQ = 13] % of voxels). Subjective analysis allowed noticing that some structures were visible or clearly visible only with PCD-CT. In addition, PCD-CT yielded better scores for the conspicuity and for the sharpness of all structures (all Ps &lt; 0.006), except for the conspicuity of the leaflets of the mechanical valves, which were well visible with either technique (4 [IQ = 3] for both). Both blooming and streak artifacts were reduced with PCD-CT (P ≤ 0.01). Overall, the use of PCD-CT resulted in better conspicuity and sharpness of the lesions compared with DEDL-CT (both Ps &lt; 0.02). In addition, only with PCD-CT some differences between the 2 lesions were detectable. Adding the extension ring resulted in reduced conspicuity and sharpness with DEDL-CT (P = 0.04 and P = 0.02, respectively) and only in reduced sharpness with PCD-CT (P = 0.04). Tungsten K-edge imaging allowed for the visualization of the only dense structure containing it, the leaflets, and it resulted in images judged having less blooming and metallic artifacts as compared with conventional PCD-CT images (P &lt; 0.01). Conclusions: With PCD-CT, objective and subjective image quality of metallic and biological PHVs is improved compared with DEDL-CT. Notwithstanding the improvements in image quality, millimetric lesions attached to the structure of the valves remain a challenge for PCD-CT. Tungsten K-edge imaging allows for even further reduction of artifacts.</p

Wall Orientation and Shear Stress in the Lattice Boltzmann Model

The wall shear stress is a quantity of profound importance for clinical diagnosis of artery diseases. The lattice Boltzmann is an easily parallelizable numerical method of solving the flow problems, but it suffers from errors of the velocity field near the boundaries which leads to errors in the wall shear stress and normal vectors computed from the velocity. In this work we present a simple formula to calculate the wall shear stress in the lattice Boltzmann model and propose to compute wall normals, which are necessary to compute the wall shear stress, by taking the weighted mean over boundary facets lying in a vicinity of a wall element. We carry out several tests and observe an increase of accuracy of computed normal vectors over other methods in two and three dimensions. Using the scheme we compute the wall shear stress in an inclined and bent channel fluid flow and show a minor influence of the normal on the numerical error, implying that that the main error arises due to a corrupted velocity field near the staircase boundary. Finally, we calculate the wall shear stress in the human abdominal aorta in steady conditions using our method and compare the results with a standard finite volume solver and experimental data available in the literature. Applications of our ideas in a simplified protocol for data preprocessing in medical applications are discussed.Comment: 9 pages, 11 figure

Prognostic significance of vascular and valvular calcifications in low- and high-gradient aortic stenosis

International audienceAims In low-gradient aortic stenosis (LGAS), the high valvulo-arterial impedance observed despite low valvular gradient suggests a high vascular load. Thoracic aortic calcifications (TACs) and valvular aortic calcifications (VACs) are, respectively, surrogates of aortic load and aortic valvular gradient. The aim of this study was to compare the respective contributions of TAC and VAC on 3-year cardiovascular (CV) mortality following TAVI in LGAS vs. high-gradient aortic stenosis (HGAS) patients. Methods and results A total of 1396 consecutive patients were included. TAC and VAC were measured on the pre-TAVI CT-scan. About 435 (31.2%) patients had LGAS and 961 (68.8%) HGAS. LGAS patients were more prone to have diabetes, coronary artery disease (CAD), atrial fibrillation (AF), and lower left ventricular ejection fraction (LVEF), P&lt;0.05 for all. During the 3 years after TAVI, 245(17.8%) patients experienced CV mortality, 92(21.6%) in LGAS and 153(16.2%) in HGAS patients, P=0.018. Multivariate analysis adjusted for age, gender, diabetes, AF, CAD, LVEF, renal function, vascular access, and aortic regurgitation showed that TAC but not VAC was associated with CV mortality in LGAS, hazard ratio (HR) 1.085 confidence interval (CI) (1.019–1.156), P=0.011, and HR 0.713 CI (0.439–1.8), P=0.235; the opposite was observed in HGAS patients with VAC but not TAC being associated with CV mortality, HR 1.342 CI (1.034–1.742), P=0.027, and HR 1.015 CI (0.955–1.079), P=0.626. Conclusion TAC plays a major prognostic role in LGAS while VAC remains the key in HGAS patients. This confirms that LGAS is a complex vascular and valvular disease

Quantitative fat and R2* mapping in vivo to measure lipid-rich necrotic core and intraplaque hemorrhage in carotid atherosclerosis

Purpose: The aim of this work was to quantify the extent of lipid-rich necrotic core (LRNC) and intraplaque hemorrhage (IPH) in atherosclerotic plaques. Methods: Patients scheduled for carotid endarterectomy underwent four-point Dixon and T1-weighted magnetic resonance imaging (MRI) at 3 Tesla. Fat and R2* maps were generated from the Dixon sequence at the acquired spatial resolution of 0.60 × 0.60 × 0.70 mm voxel size. MRI and three-dimensional (3D) histology volumes of plaques were registered. The registration matrix was applied to segmentations denoting LRNC and IPH in 3D histology to split plaque volumes in regions with and without LRNC and IPH. Results: Five patients were included. Regarding volumes of LRNC identified by 3D histology, the average fat fraction by MRI was significantly higher inside LRNC than outside: 12.64 ± 0.2737% versus 9.294 ± 0.1762% (mean ± standard error of the mean [SEM]; P < 0.001). The same was true for IPH identified by 3D histology, R2* inside versus outside IPH was: 71.81 ± 1.276 s−1 versus 56.94 ± 0.9095 s−1 (mean ± SEM; P < 0.001). There was a strong correlation between the cumulative fat and the volume of LRNC from 3D histology (R2 = 0.92) as well as between cumulative R2* and IPH (R2 = 0.94). Conclusion: Quantitative mapping of fat and R2* from Dixon MRI reliably quantifies the extent of LRNC and IPH