Automatic segmentation, detection and quantification of coronary artery stenoses on CTA

Accurate detection and quantification of coronary artery stenoses is an essential requirement for treatment planning of patients with suspected coronary artery disease. We present a method to automatically detect and quantify coronary artery stenoses in computed tomography coronary angiography. First, centerlines are extracted using a two-point minimum cost path approach and a subsequent refinement step. The resulting centerlines are used as an initialization for lumen segmentation, performed using graph cuts. Then, the expected diameter of the healthy lumen is estimated by applying robust kernel regression to the coronary artery lumen diameter profile. Finally, stenoses are detected and quantified by computing the difference between estimated and expected diameter profiles. We evaluated our method using the data provided in the Coronary Artery Stenoses Detection and Quantification Evaluation Framework. Using 30 testing datasets, the method achieved a detection sensitivity of 29 % and a positive predi

Robust motion correction in the frequency domain of cardiac MR stress perfusion sequences

Poster presentation from 16th Annual SCMR Scientific Sessions, San Francisco, CA, USA. 31 January - 3 February 2013.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc

Regression-Based Cardiac Motion Prediction From Single-Phase CTA

Cardiovascular Aspects of Radiolog

Automatic quantification of epicardial fat volume on non-enhanced cardiac CT scans using a multi-atlas segmentation approach

PURPOSE: There is increasing evidence that epicardial fat (i.e., adipose tissue contained within the pericardium) plays an important role in the development of cardiovascular disease. Obtaining the epicardial fat volume from routinely performed non-enhanced cardiac CT scans is therefore of clinical interest. The purpose of this work is to investigate the feasibility of automatic pericardium segmentation and subsequent quantification of epicardial fat on non-enhanced cardiac CT scans. METHODS: Imaging data of 98 randomly selected subjects belonging to a larger cohort of subjects who underwent a cardiac CT scan at our medical center were retrieved. The data were acquired on two different scanners. Automatic multi-atlas based method for segmenting the pericardium and calculating the epicardial fat volume has been developed. The performance of the method was assessed by (1) comparing the automatically segmented pericardium to a manually annotated reference standard, (2) comparing the automatically obtained epicardial fat volumes to those obtained manually, and (3) comparing the accuracy of the automatic results to the inter-observer variability. RESULTS: Automatic segmentation of the pericardium was achieved with a Dice similarity index of 89.1 \ub1 2.6% with respect to Observer 1 and 89.2 \ub1 1.9% with respect to Observer 2. The correlation between the automatic method and the manual observers with respect to the epicardial fat volume computed as the Pearson's correlation coefficient (R) was 0.91 (P < 0.001) for both observers. The inter-observer study resulted in a Dice similarity index of 89.0 \ub1 2.4% for segmenting the pericardium and a Pearson's correlation coefficient of 0.92 (P<0.001) for computation of the epicardial fat volume. CONCLUSIONS: The authors developed a fully automatic method that is capable of segmenting the pericardium and quantifying epicardial fat on non-enhanced cardiac CT scans. The authors demonstrated the feasibility of using this method to replace manual annotations by showing that the automatic method performs as good as manual annotation on a large dataset

The use of imaging for electrophysiological and devices procedures: a report from the first European Heart Rhythm Association Policy Conference, jointly organized with the European Association of Cardiovascular Imaging (EACVI), the Council of Cardiovascular Imaging and the European Society of Cardiac Radiology

mplantations of cardiac devices therapies and ablation procedures frequently depend on accurate and reliable imaging modalities for pre-procedural assessments, intra-procedural guidance, detection of complications, and the follow-up of patients. An understanding of echocardiography, cardiovascular magnetic resonance imaging, nuclear cardiology, X-ray computed tomography, positron emission tomography, and vascular ultrasound is indispensable for cardiologists, electrophysiologists as well as radiologists, and it is currently recommended that physicians should be trained in several imaging modalities. There are, however, no current guidelines or recommendations by electrophysiologists, cardiac imaging specialists, and radiologists, on the appropriate use of cardiovascular imaging for selected patient indications, which needs to be addressed. A Policy Conference on the use of imaging in electrophysiology and device management, with representatives from different expert areas of radiology and electrophysiology and commercial developers of imaging and device technologies, was therefore jointly organized by European Heart Rhythm Association (EHRA), the Council of Cardiovascular Imaging and the European Society of Cardiac Radiology (ESCR). The objectives were to assess the state of the level of evidence and a first step towards a consensus document for currently employed imaging techniques to guide future clinical use, to elucidate the issue of reimbursement structures and health economy, and finally to define the need for appropriate educational programmes to ensure clinical competence for electrophysiologists, imaging specialists, and radiologists

Evaluation of whole-body MR to CT deformable image registration

Multimodality image registration plays a crucial role in various clinical and research applications. The aim of this study is to present an optimized MR to CT whole-body deformable image registration algorithm and its validation using clinical studies. A 3D intermodality registration technique based on B-spline transformation was performed using optimized parameters of the elastix package based on the Insight Toolkit (ITK) framework. Twenty-eight (17 male and 11 female) clinical studies were used in this work. The registration was evaluated using anatomical landmarks and segmented organs. In addition to 16 anatomical landmarks, three key organs (brain, lungs, and kidneys) and the entire body volume were segmented for evaluation. Several parameters--such as the Euclidean distance between anatomical landmarks, target overlap, Dice and Jaccard coefficients, false positives and false negatives, volume similarity, distance error, and Hausdorff distance--were calculated to quantify the quality of the registration algorithm. Dice coefficients for the majority of patients (> 75%) were in the 0.8-1 range for the whole body, brain, and lungs, which satisfies the criteria to achieve excellent alignment. On the other hand, for kidneys, Dice coefficients for volumes of 25% of the patients meet excellent volume agreement requirement, while the majority of patients satisfy good agreement criteria (> 0.6). For all patients, the distance error was in 0-10 mm range for all segmented organs. In summary, we optimized and evaluated the accuracy of an MR to CT deformable registration algorithm. The registered images constitute a useful 3D whole-body MR-CT atlas suitable for the development and evaluation of novel MR-guided attenuation correction procedures on hybrid PET-MR systems