A systematic review of multi-slice and multi-frame descriptors in cardiac MRI exams

Computer-Aided Diagnosis systems have been developed to help medical professional in their decision making routines towards a more accurate diagnosis. These systems process medical exams such as Magnetic Resonance (MRI) in order to quantify meaningful features. These can be used with similarity-measuring techniques in a Content-Based Image Retrieval context, or inputted into a machine learning classifier in order to support early disease detection. For cardiac MRIs, single slice descriptors have been proposed in the two-dimensional domain, shape descriptors have been proposed in the three-dimensional domain, and previous reviews have mapped these two descriptor categories. Nonetheless, no systematic review on these descriptors have looked at full cardiac MRI images sets. We have reviewed the literature by searching for descriptors that consider the whole slice set (multi-slice) or frames (multi-frame) in cardiac MRI exams. We discuss descriptors and techniques, the datasets that were used, and the different evaluation metrics. Finally, we highlight literature gaps and research opportunities.

An Overview on Image Registration Techniques for Cardiac Diagnosis and Treatment

Image registration has been used for a wide variety of tasks within cardiovascular imaging. This study aims to provide an overview of the existing image registration methods to assist researchers and impart valuable resource for studying the existing methods or developing new methods and evaluation strategies for cardiac image registration. For the cardiac diagnosis and treatment strategy, image registration and fusion can provide complementary information to the physician by using the integrated image from these two modalities. This review also contains a description of various imaging techniques to provide an appreciation of the problems associated with implementing image registration, particularly for cardiac pathology intervention and treatments.

A novel skeleton based quantification and 3-D volumetric visualization of left atrium fibrosis using late gadolinium enhancement magnetic resonance imaging

This work presents the results of a new tool for 3-D segmentation, quantification and visualization of cardiac left atrium fibrosis, based on late gadolinium enhancement magnetic resonance imaging (LGE-MRI), for stratifying patients with atrial fibrillation (AF) that are candidates for radio-frequency catheter ablation. In this study 10 consecutive patients suffering AF with different grades of atrial fibrosis were considered. LGE-MRI and magnetic resonance angiography (MRA) images were used to detect and quantify fibrosis of the left atrium using a threshold and 2-D skeleton based approach. Quantification and 3-D volumetric views of atrial fibrosis were compared with quantification and 3-D bipolar voltage maps measured with an electro-anatomical mapping (EAM) system, the clinical reference standard technique for atrial substrate characterization. Segmentation and quantification of fibrosis areas proved to be clinically reliable among all different fibrosis stages. The proposed tool obtains discrepancies in fibrosis quantification less than 4% from EAM results and yields accurate 3-D volumetric views of fibrosis of left atrium. The novel 3-D visualization and quantification tool based on LGE-MRI allows detection of cardiac left atrium fibrosis areas. This noninvasive method provides a clinical alternative to EAM systems for quantification and localization of atrial fibrosis.