MADplots: A methodology for visualizing and characterizing energy-dependent attenuation of tissues in spectral computed tomography.

Rationale And Objectives: A method for visualizing and analyzing the complete information contained in spectral CT scans using two-dimensional histograms (i.e. Material Attenuation Decomposition plots - MADplots) of the water-photoelectric attenuation versus water-scatter attenuation at the cohort (combination of multiple studies across patients), examination, series, slice, and organ/ROI levels is described.

Prognostic Value of RCA Pericoronary Adipose Tissue CT-Attenuation Beyond High-Risk Plaques, Plaque Volume, and Ischemia.

Objectives: This study was designed to assess the prognostic value of pericoronary adipose tissue computed tomography attenuation (PCATa) beyond quantitative coronary computed tomography angiography (CCTA)-derived plaque volume and positron emission tomography (PET) determined ischemia.

Background: Inflammation plays a crucial role in atherosclerosis. PCATa has been shown to assess coronary-specific inflammation and is of prognostic value in patients with suspected coronary artery disease (CAD).

Impact of CT reconstruction algorithm on auto-segmentation performance.

Model-based iterative reconstruction (MBIR) reduces CT imaging dose while maintaining image quality. However, MBIR reduces noise while preserving edges which may impact intensity-based tasks such as auto-segmentation. This work evaluates the sensitivity of an auto-contouring prostate atlas across multiple MBIR reconstruction protocols and benchmarks the results against filtered back projection (FBP).

Unsupervised abnormality detection through mixed structure regularization (MSR) in deep sparse autoencoders.

Purpose: The purpose of this study is to introduce and evaluate the mixed structure regularization (MSR) approach for a deep sparse autoencoder aimed at unsupervised abnormality detection in medical images. Unsupervised abnormality detection based on identifying outliers using deep sparse autoencoders is a very appealing approach for computer-aided detection systems as it requires only healthy data for training rather than expert annotated abnormality. However, regularization is required to avoid overfitting of the network to the training data.

A functionally personalized boundary condition model to improve estimates of fractional flow reserve with CT (CT-FFR).

Purpose: The purpose of this study is to develop and evaluate a functionally personalized boundary condition (BC) model for estimating the fractional flow reserve (FFR) from coronary computed tomography angiography (CCTA) using flow simulation (CT-FFR).

Materials And Methods: The CCTA data of 90 subjects with subsequent invasive FFR in 123 lesions within 21 days (range: 0-83) were retrospectively collected. We developed a functionally personalized BC model accounting specifically for the coronary microvascular resistance dependency on the coronary outlets pressure suggested by several physiological studies.

Improving CCTA-based lesions' hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation.

Purpose: The goal of this study was to assess the potential added benefit of accounting for partial volume effects (PVE) in an automatic coronary lumen segmentation algorithm that is used to determine the hemodynamic significance of a coronary artery stenosis from coronary computed tomography angiography (CCTA).

Materials And Methods: Two sets of data were used in our work: (a) multivendor CCTA datasets of 18 subjects from the MICCAI 2012 challenge with automatically generated centerlines and 3 reference segmentations of 78 coronary segments and (b) additional CCTA datasets of 97 subjects with 132 coronary lesions that had invasive reference standard FFR measurements. We extracted the coronary artery centerlines for the 97 datasets by an automated software program followed by manual correction if required.

A method for improving iodine contrast enhancement in abdominal computed tomography: experimental study in a pig model.

Objectives: To investigate the improvement in diagnostic image quality of an iodine contrast enhancement tool in an animal model for computed tomography (CT).

Methods: One pig was examined over several consecutive days with a CT system. The quantity of iodine as contrast medium (0.

Virtual nonenhanced abdominal dual-energy MDCT: Analysis of image characteristics.

Aim: To evaluate abdominal and pelvic image characteristics and artifacts on virtual nonenhanced (VNE) images generated from contrast-enhanced dual-energy multidetector computed tomography (MDCT) studies.

Methods: Hadassah-Hebrew University Medical Institutional Review Board approval was obtained; 22 patients underwent clinically-indicated abdominal and pelvic single-source dual-energy MDCT (Philips Healthcare, Cleveland, OH, USA), pre- and post-IV administration of Omnipaque 300 contrast (100 cc). Various solid and vascular structures were evaluated.

Balanced Exploration and Exploitation Model search for efficient epipolar geometry estimation.

The estimation of the epipolar geometry is especially difficult when the putative correspondences include a low percentage of inlier correspondences and/or a large subset of the inliers is consistent with a degenerate configuration of the epipolar geometry that is totally incorrect. This work presents the Balanced Exploration and Exploitation Model Search (BEEM) algorithm that works very well especially for these difficult scenes. The algorithm handles these two problems in a unified manner.