Prediction of post-hepatectomy liver failure by preoperative gadoxetate disodium-enhanced magnetic resonance imaging.

BACKGROUND: Approximately 15% of patients experience post-hepatectomy liver failure after major hepatectomy. Poor hepatocyte uptake of gadoxetate disodium, a magnetic resonance imaging contrast agent, may be a predictor of post-hepatectomy liver failure. METHODS: A retrospective cohort study of patients undergoing major hepatectomy (≥3 segments) with a preoperative gadoxetate disodium-enhanced magnetic resonance imaging was conducted. The liver signal intensity (standardized to the spleen) and the functional liver remnant was calculated to determine if this can predict post-hepatectomy liver failure after major hepatectomy. RESULTS: In 134 patients, low signal intensity of the remnant liver standardized by signal intensity of the spleen in post-contrast images was associated with post-hepatectomy liver failure in multiple logistic regression analysis (Odds Ratio 0.112; 95% CI 0.023-0.551). In a subgroup of 33 patients with lower quartile of functional liver remnant, area under the curve analysis demonstrated a diagnostic accuracy of functional liver remnant to predict post-hepatectomy liver failure of 0.857 with a cut-off value for functional liver remnant of 1.4985 with 80.0% sensitivity and 89.3% specificity. CONCLUSION: Functional liver remnant determined by gadoxetate disodium-enhanced magnetic resonance imaging is a predictor of post-hepatectomy liver failure which may help identify patients for resection, reducing morbidity and mortality.

Sharing De-identified Medical Images Electronically for Research: A Survey of Patients' Opinion Regarding Data Management.

PURPOSE: Secondary usage of patient data has recently become of increasing interest for the development and application of computer analytic techniques. Strict oversight of these data is required and the individual patients themselves are integral to providing guidance. We sought to understand patients' attitudes to sharing their imaging data for research purposes. These images could provide a great wealth of information for researchers. METHODS: Patients from the Greater Toronto Area attending Sunnybrook Health Sciences Centre for imaging (magnetic resonance imagining, computed tomography, or ultrasound) examination areas were invited to participate in an electronic survey. RESULTS: Of the 1083 patients who were approached (computed tomography 609, ultrasound 314, and magnetic resonance imaging 160), 798 (74%) agreed to take the survey. Overall median age was 60 (interquartile range = 18, Q1 = 52, Q3 = 70), 52% were women, 42% had a university degree, and 7% had no high school diploma. In terms of willingness to share de-identified medical images for research, 76% were willing (agreed and strongly agreed), while 7% refused. Most participants gave their family physicians (73%) and other physicians (57%) unconditional data access. Participants chose hospitals/research institutions to regulate electronic images databases (70%), 89% wanted safeguards against unauthorized access to their data, and over 70% wanted control over who will be permitted, for how long, and the ability to revoke that permission. CONCLUSIONS: Our study found that people are willing to share their clinically acquired de-identified medical images for research studies provided that they have control over permissions and duration of access.

Haptoglobin 2-2 genotype is associated with presence and progression of MRI depicted atherosclerotic intraplaque hemorrhage.

BACKGROUND: Atherosclerotic intraplaque hemorrhage (IPH) is a source of free hemoglobin that binds the haptoglobin protein and forms a complex cleared by CD163 macrophages. Compared to the other common haptoglobin genotypes, hemoglobin-haptoglobin2-2 complex has the lowest affinity for tissue macrophages resulting in lower rate of hemoglobin uptake and increased oxidative burden. We hypothesized that haptoglobin2-2 patients' failure to clear hemoglobin results in a greater prevalence and progression of IPH. METHODS: Prevalence and volume of IPH were measured in eighty patients with advanced vascular disease using MRI. Haptoglobin was genotyped using PCR. Mixed Models Repeated Measures Analyses were performed to detect any differences in prevalence and volume of IPH between the haptoglobin genotypes. RESULTS: Haptoglobin2-2 patients had a statistically significant higher prevalence of baseline IPH (OR = 4.34, p-value: 0.01, 95% CI: 1.31-14.35). Longitudinal analysis of 48 IPH positive carotids indicated a statistically significant progression of IPH volume over time in haptoglobin2-2 patients (Type 3 test for fixed effect p-value = 0.0106; baseline vs. year 3: ß = 0.11, SE = 0.05, p-value = 0.03; year 2 vs. year 3: ß = 0.05, SE = 0.02, p-value = 0.03). CONCLUSIONS: Patients with the Hp2-2 genotype had a significantly higher prevalence of carotid baseline IPH, which progressed over a two year follow up period. Detection of pre-symptomatic vascular disease using haptoglobin genotyping may allow for better risk stratification of populations at risk of stroke and in need of more targeted imaging investigations.

Detecting left ventricular impaired relaxation in cardiac MRI using moving mesh correspondences.

UNLABELLED: Anatomical cine cardiovascular magnetic resonance (CMR) imaging is widely used to assess the systolic cardiac function because of its high soft tissue contrast. Assessment of diastolic LV function has not regularly been performed due the complex and time consuming procedures. This study presents a semi-automated assessment of the left ventricular (LV) diastolic function using anatomical short-axis cine CMR images. The proposed method is based on three main steps: (1) non-rigid registration, which yields a sequence of endocardial boundary points over the cardiac cycle based on a user-provided contour on the first frame; (2) LV volume and filling rate computations over the cardiac cycle; and (3) automated detection of the peak values of early (E) and late ventricular (A) filling waves. In 47 patients cine CMR imaging and Doppler-echocardiographic imaging were performed. CMR measurements of peak values of the E and A waves as well as the deceleration time were compared with the corresponding values obtained in Doppler-Echocardiography. For the E/A ratio the proposed algorithm for CMR yielded a Cohen's kappa measure of 0.70 and a Gwet's AC1 coefficient of 0.70. CONCLUSION: Semi-automated assessment of the left ventricular (LV) diastolic function using anatomical short-axis cine CMR images provides mitral inflow measurements comparable to Doppler-Echocardiography.

Regional assessment of cardiac left ventricular myocardial function via MRI statistical features.

Automating the detection and localization of segmental (regional) left ventricle (LV) abnormalities in magnetic resonance imaging (MRI) has recently sparked an impressive research effort, with promising performances and a breadth of techniques. However, despite such an effort, the problem is still acknowledged to be challenging, with much room for improvements in regard to accuracy. Furthermore, most of the existing techniques are labor intensive, requiring delineations of the endo- and/or epi-cardial boundaries in all frames of a cardiac sequence. The purpose of this study is to investigate a real-time machine-learning approach which uses some image features that can be easily computed, but that nevertheless correlate well with the segmental cardiac function. Starting from a minimum user input in only one frame in a subject dataset, we build for all the regional segments and all subsequent frames a set of statistical MRI features based on a measure of similarity between distributions. We demonstrate that, over a cardiac cycle, the statistical features are related to the proportion of blood within each segment. Therefore, they can characterize segmental contraction without the need for delineating the LV boundaries in all the frames. We first seek the optimal direction along which the proposed image features are most descriptive via a linear discriminant analysis. Then, using the results as inputs to a linear support vector machine classifier, we obtain an abnormality assessment of each of the standard cardiac segments in real-time. We report a comprehensive experimental evaluation of the proposed algorithm over 928 cardiac segments obtained from 58 subjects. Compared against ground-truth evaluations by experienced radiologists, the proposed algorithm performed competitively, with an overall classification accuracy of 86.09% and a kappa measure of 0.73.

Global assessment of cardiac function using image statistics in MRI.

The cardiac ejection fraction (EF) depends on the volume variation of the left ventricle (LV) cavity during a cardiac cycle, and is an essential measure in the diagnosis of cardiovascular diseases. It is often estimated via manual segmentation of several images in a cardiac sequence, which is prohibitively time consuming, or via automatic segmentation, which is a challenging and computationally expensive task that may result in high estimation errors. In this study, we propose to estimate the EF in real-time directly from image statistics using machine learning technique. From a simple user input in only one image, we build for all the images in a subject dataset (200 images) a statistic based on the Bhattacharyya coefficient of similarity between image distributions. We demonstrate that these statistics are non-linearly related to the LV cavity areas and, therefore, can be used to estimate the EF via an Artificial Neural Network (ANN) directly. A comprehensive evaluation over 20 subjects demonstrated that the estimated EFs correlate very well with those obtained from independent manual segmentations.

Assessment of regional myocardial function via statistical features in MR images.

Early and accurate detection of Left Ventricle (LV) regional wall motion abnormalities significantly helps in the diagnosis and followup of cardiovascular diseases. We present a regional myocardial abnormality detection framework based on image statistics. The proposed framework requires a minimal user interaction, only to specify initial delineation and anatomical landmarks on the first frame. Then, approximations of regional myocardial segments in subsequent frames were systematically obtained by superimposing the initial delineation on the rest of the frames. The proposed method exploits the Bhattacharyya coefficient to measure the similarity between the image distribution within each segment approximation and the distribution of the corresponding user-provided segment. Linear Discriminate Analysis (LDA) is applied to find the optimal direction along which the projected features are the most descriptive. Then a Linear Support Vector Machine (SVM) classifier is employed for each of the regional myocardial segments to automatically detect abnormally contracting regions of the myocardium. Based on a clinical dataset of 30 subjects, the evaluation demonstrates that the proposed method can be used as a promising diagnostic support tool to assist clinicians.