4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1263-1280.

The development of magnetic resonance imaging (MRI) revolutionized both the medical and scientific worlds. A large variety of MRI options have generated a huge amount of image data to interpret. The investigation of a specific tissue in 3D or 4D MR images can be facilitated by image processing techniques, such as segmentation and registration. In this work, we provide a brief review of the principles and methods that are commonly applied to achieve superior tissue segmentation results in MRI. The impacts of MR image acquisition on segmentation outcome and the principles of selecting and exploiting segmentation techniques tailored for specific tissue identification tasks are discussed. In the end, two exemplary applications, breast and fibroglandular tissue segmentation in MRI and myocardium segmentation in short-axis cine and real-time MRI, are discussed to explain the typical challenges that can be posed in practical segmentation tasks in MRI data. The corresponding solutions that are adopted to deal with these challenges of the two practical segmentation tasks are thoroughly reviewed.

BackgroundThe purpose of this work is to analyze differences in left ventricular torsion between volunteers and patients with non-ischemic cardiomyopathy based on tissue phase mapping (TPM) cardiovascular magnetic resonance (CMR).MethodsTPM was performed on 27 patients with non-ischemic cardiomyopathy and 14 normal volunteers. Patients underwent a standard CMR including late gadolinium enhancement (LGE) for the assessment of myocardial scar and ECG-gated cine CMR for global cardiac function. TPM was acquired in short-axis orientation at base, mid, and apex for all subjects. After evaluation by experienced observers, the patients were divided in subgroups according to the presence or absence of LGE (LGE+/LGE-), local wall motion abnormalities (WM+/WM-), and having a preserved (≥50 %) or reduced (<50 %) ejection fraction (EF+/EF-). TPM data was semi-automatically segmented and global LV torsion was computed for each cardiac time frame for endocardial and epicardial layers, and for the entire myocardium.ResultsMaximum myocardial torsion was significantly lower for patients with reduced EF compared to controls (0.21 ± 0.15°/mm vs. 0.36 ± 0.11°/mm, p = 0.018), but also for patients with wall motion abnormalities (0.21 ± 0.13°/mm vs. 0.36 ± 0.11°/mm, p = 0.004). Global myocardial torsion showed a positive correlation (r = 0.54, p < 0.001) with EF. Moreover, endocardial torsion was significantly higher than epicardial torsion for EF+ subjects (0.56 ± 0.33°/mm vs. 0.34 ± 0.18°/mm, p = 0.039) and for volunteers (0.46 ± 0.16°/mm vs. 0.30 ± 0.09°/mm, p = 0.004). The difference in maximum torsion between endo- and epicardial layers was positively correlated with EF (r = 0.47, p = 0.002) and age (r = 0.37, p = 0.016) for all subjects.ConclusionsTPM can be used to detect significant differences in LV torsion in patients with reduced EF and in the presence of local wall motion abnormalities. We were able to quantify torsion differences between the endocardium and epicardium, which vary between patient subgroups and are correlated to age and EF.

From detailed characterization of cardiac abnormalities to the assessment of cancer treatment‐related cardiac dysfunction, cardiac MRI is playing a growing role in the evaluation of cardiac pathology in oncology patients. Current guidelines are now incorporating the use of MRI for the comprehensive multidisciplinary approach to cancer management, and innovative applications of MRI in research are expanding its potential to provide a powerful noninvasive tool in the arsenal against cancer. This review focuses on the application of cardiac MRI to diagnose and manage cardiovascular complications related to cancer and its treatment. Following an introduction to current cardiac MRI methods and principles, this review is divided into two sections: functional cardiovascular analysis and anatomical or tissue characterization related to cancer and cancer therapeutics. Level of Evidence: 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;50:1349–1366.

Although computer assistance has become common in medical practice, some of the most challenging tasks that remain unsolved are in the area of automatic detection and recognition. The human visual perception is in general far superior to computer vision algorithms. Object-based image analysis is a relatively new approach that aims to lift image analysis from a pixel-based processing to a semantic region-based processing of images. It allows effective integration of reasoning processes and contextual concepts into the recognition method. In this paper, we present an approach that applies object-based image analysis to the task of detecting the spine in computed tomography images. A spine detection would be of great benefit in several contexts, from the automatic labeling of vertebrae to the assessment of spinal pathologies. We show with our approach how region-based features, contextual information and domain knowledge, especially concerning the typical shape and structure of the spine and its components, can be used effectively in the analysis process. The results of our approach are promising with a detection rate for vertebral bodies of 96% and a precision of 99%. We also gain a good two-dimensional segmentation of the spine along the more central slices and a coarse three-dimensional segmentation.

BackgroundArrhythmia can significantly alter the image quality of cardiovascular magnetic resonance (CMR); automatic detection and sorting of the most frequent types of arrhythmias during the CMR acquisition could potentially improve image quality. New CMR techniques, such as non-Cartesian CMR, can allow self-gating: from cardiac motion-related signal changes, we can detect cardiac cycles without an electrocardiogram. We can further use this data to obtain a surrogate for RR intervals (valley intervals: VV). Our purpose was to evaluate the feasibility of an automated method for classification of non-arrhythmic (NA) (regular cycles) and arrhythmic patients (A) (irregular cycles), and for sorting of common arrhythmia patterns between atrial fibrillation (AF) and premature ventricular contraction (PVC), using the cardiac motion-related signal obtained during self-gated free-breathing radial cardiac cine CMR with compressed sensing reconstruction (XD-GRASP).MethodsOne hundred eleven patients underwent cardiac XD-GRASP CMR between October 2015 and February 2016; 33 were included for retrospective analysis with the proposed method (6 AF, 8 PVC, 19 NA; by recent ECG). We analyzed the VV, using pooled statistics (histograms) and sequential analysis (Poincaré plots), including the median (medVV), the weighted mean (meanVV), the total number of VV values (VVval), and the total range (VVTR) and half range (VVHR) of the cumulative frequency distribution of VV, including the median to half range (medVV/VVHR) and the half range to total range (VVHR/VVTR) ratios. We designed a simple algorithm for using the VV results to differentiate A from NA, and AF from PVC.ResultsBetween NA and A, meanVV, VVval, VVTR, VVHR, medVV/VVHR and VVHR/VVTR ratios were significantly different (p values = 0.00014, 0.0027, 0.000028, 5×10−9, 0.002, respectively). Between AF and PVC, meanVV, VVval and medVV/VVHR ratio were significantly different (p values = 0.018, 0.007, 0.044, respectively). Using our algorithm, sensitivity, specificity, and accuracy were 93 %, 95 % and 94 % to discriminate between NA and A, and 83 %, 71 %, and 77 % to discriminate between AF and PVC, respectively; areas under the ROC curve were 0.93 and 0.89.ConclusionsOur study shows we can reliably detect arrhythmias and differentiate AF from PVC, using self-gated cardiac cine XD-GRASP CMR.

The recent development of a real-time magnetic resonance imaging (MRI) technique with 20 to 30 ms temporal resolution allows for imaging multiple consecutive heart cycles, without the need for breath holding or ECG synchronization. Manual analysis of the resulting image series is no longer feasible because of their length. We propose a region-based algorithm for automatically segmenting the myocardium in consecutive heart cycles based on local context and prior knowledge. The method was evaluated on ten real-time MRI series and compared to segmentations by two observers, with promising results. We show that our approach enables a multicycle analysis of the heart function robust to breathing and arrhythmia

Obstructive sleep apnea (OSA) is a public health problem. Volumetric analysis of the upper airways can help us to understand the pathogenesis of OSA. A reliable pharynx segmentation is the first step in identifying the anatomic risk factors for this sleeping disorder. As manual segmentation is a time-consuming and subjective process, a fully automatic segmentation of pharyngeal structures is required when investigating larger data bases such as in cohort studies. We develop a context-based automatic algorithm for segmenting pharynx from magnetic resonance images (MRI). It consists of a pipeline of steps including pre-processing (thresholding, connected component analysis) to extract coarse 3D objects, classification of the objects (involving object-based image analysis (OBIA), visual feature space analysis, and silhouette coefficient computation) to segregate pharynx from other structures automatically, and post-processing to refine the shape of the identified pharynx (including extraction of the oropharynx and propagating results from neighboring slices to slices that are difficult to delineate). Our technique is fast such that we can apply our algorithm to population-based epidemiological studies that provide a high amount of data. Our method needs no user interaction to extract the pharyngeal structure. The approach is quantitatively evaluated on ten datasets resulting in an average of approximately 90% detected volume fraction and a 90% Dice coefficient, which is in the range of the interobserver variation within manual segmentation results.