CT arterial enhancement fraction (AEF) for hepatocellular carcinoma (HCC) screening in patients with end-stage liver cirrhosis

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Stochastic Segmentation with Conditional Categorical Diffusion Models

Semantic segmentation has made significant progress in recent years thanks to deep neural networks, but the common objective of generating a single segmentation output that accurately matches the image's content may not be suitable for safety-critical domains such as medical diagnostics and autonomous driving. Instead, multiple possible correct segmentation maps may be required to reflect the true distribution of annotation maps. In this context, stochastic semantic segmentation methods must learn to predict conditional distributions of labels given the image, but this is challenging due to the typically multimodal distributions, high-dimensional output spaces, and limited annotation data. To address these challenges, we propose a conditional categorical diffusion model (CCDM) for semantic segmentation based on Denoising Diffusion Probabilistic Models. Our model is conditioned to the input image, enabling it to generate multiple segmentation label maps that account for the aleatoric uncertainty arising from divergent ground truth annotations. Our experimental results show that CCDM achieves state-of-the-art performance on LIDC, a stochastic semantic segmentation dataset, and outperforms established baselines on the classical segmentation dataset Cityscapes.Comment: Code available at https://github.com/LarsDoorenbos/ccdm-stochastic-segmentatio

T1 reduction rate with Gd-EOB-DTPA determines liver function on both 1.5 T and 3 T MRI.

Magnetic resonance T1 mapping before and after Gd-EOB-DTPA administration allows quantification of the T1 reduction rate as a non-invasive surrogate marker of liver function. A major limitation of T1 relaxation time measurement is its dependency on MRI field strengths. Since T1 reduction rate is calculated as the relative shortening of T1 relaxation time before and after contrast administration, we hypothesized that the T1 reduction rate is comparable between 1.5 and 3 T. We thus compared liver T1 relaxation times between 1.5 and 3 T in a total of 243 consecutive patients (124, 1.5 T and 119, 3 T) between 09/2018 and 07/2019. T1 reduction rates were compared between patients with no cirrhosis and patients with cirrhosis Child-Pugh A-C. There was no significant difference of T1 reduction rate between 1.5 and 3 T in any patient group (p-value 0.126-0.861). On both 1.5 T and 3 T, T1 reduction rate allowed to differentiate between patients with no cirrhosis and patients with liver cirrhosis Child A-C (p < 0.001). T1 reduction rate showed a good performance to predict liver cirrhosis Child A (AUC = 0.83, p < 0.001), Child B (AUC = 0.83, p < 0.001) and Child C (AUC = 0.92, p < 0.001). In conclusion, T1 reduction rate allows to determine liver function on Gd-EOB-DTPA MRI with comparable values on 1.5 T and 3 T

Convolutional neural network for automated segmentation of the liver and its vessels on non-contrast T1 vibe Dixon acquisitions

We evaluated the effectiveness of automated segmentation of the liver and its vessels with a convolutional neural network on non-contrast T1 vibe Dixon acquisitions. A dataset of non-contrast T1 vibe Dixon liver magnetic resonance images was labelled slice-by-slice for the outer liver border, portal, and hepatic veins by an expert. A 3D U-Net convolutional neural network was trained with different combinations of Dixon in-phase, opposed-phase, water, and fat reconstructions. The neural network trained with the single-modal in-phase reconstructions achieved a high performance for liver parenchyma (Dice 0.936 ± 0.02), portal veins (0.634 ± 0.09), and hepatic veins (0.532 ± 0.12) segmentation. No benefit of using multi -modal input was observed (p=1.0 for all experiments), combining in-phase, opposed-phase, fat, and water reconstruction. Accuracy for differentiation between portal and hepatic veins was 99% for portal veins and 97% for hepatic veins in the central region and slightly lower in the peripheral region (91% for portal veins, 80% for hepatic veins). In conclusion, deep learning-based automated segmentation of the liver and its vessels on non-contrast T1 vibe Dixon was highly effective. The single-modal in-phase input achieved the best performance in segmentation and differentiation between portal and hepatic veins

Correlation of gastrointestinal perforation location and amount of free air and ascites on CT imaging.

PURPOSE To analyze the amount of free abdominal gas and ascites on computed tomography (CT) images relative to the location of a perforation. METHODS We retrospectively included 172 consecutive patients (93:79 = m:f) with GIT perforation, who underwent abdominal surgery (ground truth for perforation location). The volume of free air and ascites were quantified on CT images by 4 radiologists and a semiautomated software. The relation of the perforation location (upper/lower GIT) and amount of free air and ascites was analyzed by the Mann-Whitney test. Furthermore, best volume cutoff for upper and lower GIT perforation, areas under the curve (AUC), and interreader volume agreement were assessed. RESULTS There was significantly more abdominal ascites with upper GIT perforation (333 ml, range 5 to 2000 ml) than with lower GIT perforation (100 ml, range 5 to 2000 ml, p = 0.022). The highest volume of free air was found with perforations of the stomach, descending colon and sigmoid colon. Significantly less free air was found with perforations of the small bowel and ascending colon compared to the aforementioned. An ascites volume > 333 ml was associated with an upper GIT perforation demonstrating an AUC of 0.63 ± 0.04. CONCLUSION Using a two-step process based on the volumes of free air and free fluid can help localizing the site of perforation to the upper, middle or lower GI tract

The Octopus Sign-A New HRCT Sign in Pulmonary Langerhans Cell Histiocytosis.

Background: Fibrosis in pulmonary Langerhans cell histiocytosis (PLCH) histologically comprises a central scar with septal strands and associated airspace enlargement that produce an octopus-like appearance. The purpose of this study was to identify the octopus sign on high-resolution computed tomography (HRCT) images to determine its frequency and distribution across stages of the disease. Methods: Fifty-seven patients with confirmed PLCH were included. Two experienced chest radiologists assessed disease stages as early, intermediate, or late, as well as the lung parenchyma for nodular, cystic, or fibrotic changes and for the presence of the octopus sign. Statistical analysis included Cohen's kappa for interrater agreement and Fisher's exact test for the frequency of the octopus sign. Results: Interobserver agreement was substantial for the octopus sign (kappa = 0.747). Significant differences in distribution of the octopus sign between stages 2 and 3 were found with more frequent octopus signs in stage 2 and fewer in stage 3. In addition, we only found the octopus sign in cases of nodular und cystic lung disease. Conclusions: The octopus sign in PLCH can be identified not only on histological images, but also on HRCT images. Its radiological presence seems to depend on the stage of PLCH

Automated liver segmental volume ratio quantification on non-contrast T1-Vibe Dixon liver MRI using deep learning.

PURPOSE To evaluate the effectiveness of automated liver segmental volume quantification and calculation of the liver segmental volume ratio (LSVR) on a non-contrast T1-vibe Dixon liver MRI sequence using a deep learning segmentation pipeline. METHOD A dataset of 200 liver MRI with a non-contrast 3 mm T1-vibe Dixon sequence was manually labeledslice-by-sliceby an expert for Couinaud liver segments, while portal and hepatic veins were labeled separately. A convolutional neural networkwas trainedusing 170 liver MRI for training and 30 for evaluation. Liver segmental volumes without liver vessels were retrieved and LSVR was calculated as the liver segmental volumes I-III divided by the liver segmental volumes IV-VIII. LSVR was compared with the expert manual LSVR calculation and the LSVR calculated on CT scans in 30 patients with CT and MRI within 6 months. RESULTS Theconvolutional neural networkclassified the Couinaud segments I-VIII with an average Dice score of 0.770 ± 0.03, ranging between 0.726 ± 0.13 (segment IVb) and 0.810 ± 0.09 (segment V). The calculated mean LSVR with liver MRI unseen by the model was 0.32 ± 0.14, as compared with manually quantified LSVR of 0.33 ± 0.15, resulting in a mean absolute error (MAE) of 0.02. A comparable LSVR of 0.35 ± 0.14 with a MAE of 0.04 resulted with the LSRV retrieved from the CT scans. The automated LSVR showed significant correlation with the manual MRI LSVR (Spearman r = 0.97, p < 0.001) and CT LSVR (Spearman r = 0.95, p < 0.001). CONCLUSIONS A convolutional neural network allowed for accurate automated liver segmental volume quantification and calculation of LSVR based on a non-contrast T1-vibe Dixon sequence

Detection of Pulmonary Embolism on CT-Angiography Using Contrast Attenuation of Pulmonary Veins

Background: In areas of pulmonary embolism (PE), the enhancement of pulmonary veins on computed tomography pulmonary angiography (CTPA) should be decreased due to reduced arterial perfusion. Purpose: The purpose of this study was to investigate the accuracy of contrast density measurements (differences) in all pulmonary veins and the left atrium for the prediction of PE. Materials and Methods: 75 patients with PE and 22 patients without PE on CTPA were included. 4 readers measured the enhancement of the blood in the pulmonary vein immediately before the entrance to the left atrium, right after the aperture, in the center of the left atrium, in the pulmonary trunk and in the aorta. Enhancement of the pulmonary veins with and without upstream PE, and ROC curves with HU thresholds for optimal sensitivity and specificity for PE were calculated. Results: More PEs were found in the right and lower lobes. PE-affected lobes demonstrated 13.8+/-45 HU less enhancement in the pulmonary vein, compared to a paired non-affected pulmonary vein of the same patient (P &lt; 0.0001). On average, non-affected pulmonary veins demonstrated no difference in enhancement compared to each other: 0.2 +/-21 HU. The optimal cutoff level in the ROC curve analysis for PE affection proved to be decreasing enhancement in the pulmonary vein of more than 10 HU, compared to the atrium. Conclusion: Decreasing enhancement in the pulmonary vein of more than 10 HU compared to the atrium could provide additional information and confidence in the diagnosis of PE

Diagnostic performance of quantitative coronary artery disease assessment using computed tomography in patients with aortic stenosis undergoing transcatheter aortic-valve implantation.

BACKGROUND Computed tomography angiography (CTA) is a cornerstone in the pre- transcatheter aortic valve replacement (TAVI) assessment. We evaluated the diagnostic performance of CTA and coronary artery calcium score (CACS) for CAD evaluation compared to invasive coronary angiography in a cohort of TAVI patients. METHODS In consecutive TAVI patients without prior coronary revascularization and device implants, CAD was assessment by quantitative analysis in CTA. (a) Patients with non-evaluable segments were classified as obstructive CAD. (b) In patients with non-evaluable segments a CACS cut-off of 100 was applied for obstructive CAD. The reference standard was quantitative invasive coronary angiography (QCA, i.e. ≥ 50% stenosis). RESULTS 100 consecutive patients were retrospectively included, age was 82.3 ± 6.5 years and 30% of patients had CAD. In 16% of the patients, adequate visualization of the entire coronary tree (all 16 segments) was possible with CTA, while 84% had at least one segment which was not evaluable for CAD analysis due to impaired image quality. On a per-patient analysis, where patients with low image quality were classified as CAD, CTA showed a sensitivity of 100% (95% CI 88.4-100.0), specificity of 11.4% (95% CI 5.1-21.3), PPV of 32.6% (95% CI 30.8-34.5), NPV of 100% and diagnostic accuracy of 38% (95% CI 28.5-48.3) for obstructive CAD. When applying a combined approach of CTA (in patients with good image quality) and CACS (in patients with low image quality), the sensitivity and NPV remained at 100% and obstructive CAD could be ruled out in 20% of the TAVI patients, versus 8% using CTA alone. CONCLUSION In routinely acquired pre-TAVI CTA, the image quality was insufficient in a high proportion of patients for the assessment of the entire coronary artery tree. However, when adding CACS in patients with low image quality to quantitative CTA assessment in patients with good image quality, obstructive CAD could be ruled-out in 1/5 of the patients and may therefore constitute a strategy to streamline pre-procedural workup, and reduce risk, radiation and costs in selected TAVI patients without prior coronary revascularization or device implants

Reproducibility of 4D cardiac computed tomography feature tracking myocardial strain and comparison against speckle-tracking echocardiography in patients with severe aortic stenosis.

BACKGROUND Myocardial strain is an established parameter for the assessment of cardiac function and routinely derived from speckle tracking echocardiography (STE). Novel post-processing tools allow deformation imaging also by 4D cardiac computed tomography angiography (CCT). This retrospective study aims to analyze the reproducibility of CCT strain and compare it to that of STE. METHODS Left (LV) and right ventricular (RV), and left atrial (LA) ejection fraction (EF), dimensions, global longitudinal (GLS), circumferential (GCS) and radial strain (GRS) were determined by STE and CCT feature tracking in consecutive patients with severe aortic stenosis evaluated for transcatheter aortic valve implantation. RESULTS 106 patients (mean age 79.9 ​± ​7.8, 44.3% females) underwent CCT at a median of 3 days (IQR 0-28 days) after STE. In CCT, strain measures showed good to excellent reproducibility (intra- and inter-reader intraclass correlation coefficient ≥0.75) consistently in the LV, RV and LA. In STE, only LV GLS and LA GLS yielded good reproducibility, whereas LV GCS and LV GRS showed moderate, and RV GLS and free wall longitudinal strain (FWLS) poor reproducibility. Agreement between CCT and STE was strong for LV GLS only, while other strain features displayed moderate (LV GCS, LA GLS) or weak (LV GRS, RV GLS and FWLS) inter-modality correlation. CONCLUSION LV, RV and LA CCT strain assessments were highly reproducible. While a strong agreement to STE was found for LV GLS, inter-modality correlation was moderate or weak for LV GCS, LV GRS, and RV and LA longitudinal strain, possibly related to poor reproducibility of STE measurements