Yechiel Lamash

FDG PET/CT Early Dynamic Blood Flow and Late Standardized Uptake Value Determination in Hepatocellular Carcinoma

PURPOSE To prospectively determine whether fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) early dynamic blood flow estimates could be used to discriminate hepatocellular carcinoma (HCC) from background liver and to characterize HCC in patients with and those without angioinvasion; and to evaluate the association between blood flow measures at FDG PET/CT with metabolism in HCCs. MATERIALS AND METHODS Institutional review board approval and written informed consent were obtained for this prospective study. Twenty-one consecutive patients (mean age, 65 years) with 30 established HCCs (mean size, 5.5 cm; seven lesions in five patients with angioinvasion) underwent a blood flow study with an FDG dynamic scan divided into 18 sequences of 5 seconds each and a standard PET/CT scan. On the dynamic study, three independent operators obtained volumes of interest (VOIs) for which three blood flow estimates were calculated (hepatic perfusion index [HPI], time to peak [TTP], and peak intensity [PI]). On the late study, a VOI was placed on the fused scan for each HCC, and maximum standardized uptake value (SUV(max)) was obtained. By using a mixed-effects model analysis, comparison of blood flow estimates between HCC with and that without angioinvasion and background liver was performed. The association between blood flow estimates and SUV(max) was also assessed. RESULTS HPI and TTP showed better performance than did SUV(max) for discriminating HCC and background liver (areas under receiver operating characteristic curve: 0.96, 0.95, and 0.83, respectively; P < .05). HPI was higher in HCC in patients with angioinvasion (0.91 ± 0.15 [standard deviation]) than in those without angioinvasion (0.80 ± 0.18; P = .03). There was no difference in SUV(max) between HCC in patients with and those without angioinvasion (7.8 ± 2.9 vs 6.3 ± 3.4; P = .85). No clear association was found between HPI, PI, or TTP and SUV(max) (P = .49, .77, and .91, respectively). CONCLUSION Early dynamic blood flow FDG PET/CT may be used to help discriminate and characterize HCC tumors.

Learning Patient-Specific Lumped Models for Interactive Coronary Blood Flow Simulations

We propose a parametric lumped model (LM) for fast patient-specific computational fluid dynamic simulations of blood flow in elongated vessel networks to alleviate the computational burden of 3D finite element (FE) simulations. We learn the coefficients balancing the local nonlinear hydraulic effects from a training set of precomputed FE simulations. Our LM yields pressure predictions accurate up to 2.76mmHg on 35 coronary trees obtained from 32 coronary computed tomography angiograms. We also observe a very good predictive performance on a validation set of 59 physiological measurements suggesting that FE simulations can be replaced by our LM. As LM predictions can be computed extremely fast, our approach paves the way to use a personalised interactive biophysical model with realtime feedback in clinical practice.

Automatic coronary lumen segmentation with partial volume modeling improves lesions' hemodynamic significance assessment.

The determination of hemodynamic significance of coronary artery lesions from cardiac computed tomography angiography (CCTA) based on blood flow simulations has the potential to improve CCTA’s specificity, thus resulting in improved clinical decision making. Accurate coronary lumen segmentation required for flow simulation is challenging due to several factors. Specifically, the partial-volume effect (PVE) in small-diameter lumina may result in overestimation of the lumen diameter that can lead to an erroneous hemodynamic significance assessment. In this work, we present a coronary artery segmentation algorithm tailored specifically for flow simulations by accounting for the PVE. Our algorithm detects lumen regions that may be subject to the PVE by analyzing the intensity values along the coronary centerline and integrates this information into a machine-learning based graph min-cut segmentation framework to obtain accurate coronary lumen segmentations. We demonstrate the improvement in hemodynamic significance assessment achieved by accounting for the PVE in the automatic segmentation of 91 coronary artery lesions from 85 patients. We compare hemodynamic significance assessments by means of fractional flow reserve (FFR) resulting from simulations on 3D models generated by our segmentation algorithm with and without accounting for the PVE. By accounting for the PVE we improved the area under the ROC curve for detecting hemodynamically significant CAD by 29% (N=91, 0.85 vs. 0.66, p<0.05, Delong’s test) with invasive FFR threshold of 0.8 as the reference standard. Our algorithm has the potential to facilitate non-invasive hemodynamic significance assessment of coronary lesions.

An automatic method for the identification and quantification of myocardial perfusion defects or infarction from cardiac CT images

The current study presents an automatic algorithm for detection of myocardial infarction and ischemia using cardiac CT image data. The classification is based on probabilistic tissue modeling, where a pixel is classified according to its maximum a-posteriori probability (MAP) as belonging to a normal or abnormal tissue segment. The pixels are represented in a two-dimensional space, where the first dimension is based on pixel intensity and the second relates to pixel position in the radial (transmural) direction. By means of this method, optimal thresholds for separating abnormal from normal pixels are calculated and clusters of abnormal pixels are identified. The methods performance was evaluated in comparison to an expert analysis of the cardiac CT images and showed good agreement.

Simultaneous Multi-phase Coronary CT Angiography Analysis for Coronary Artery Disease Evaluation

Multi-Detector Computed Tomography (MDCT) is becoming increasingly important in the diagnosis of Coronary Artery Disease (CAD). Cardiac MDCT scan generally allows for reconstruction of several frames/phases in the cardiac cycle. The reconstructed images are then used to create curved multi-planar reformation (MPR) views wherein coronary lesions are best diagnosed. However, the generation of such MPR views for all potentially reconstructed phases is tedious and time consuming. Therefore, only a single phase is commonly used for diagnosis which may reduce the overall diagnostic accuracy. In the current work, we propose a new method that enable diagnosis of lesions from all reconstructed phases on a common MPR view simultaneously. Our method extracts the coronary centerline in one phase only. Next, it performs a fast registration of a region of interest between the multiple phases. Finally, the multiple phases are aligned to the MPR view and the clinician is able to review the multiple phases simultaneously. Our experiments indicate that the analysis time of multi-phase coronary CTA data can be reduced to less than 30 % of the currently required time using our method.