Faculty Bibliography

OBJECTIVES/OBJECTIVE:To determine whether use of a stylus user input device (UID) would be superior to a mouse for CMR segmentation. METHODS:Twenty-five consecutive clinical cardiac magnetic resonance (CMR) examinations were selected. Image analysis was independently performed by four observers. Manual tracing of left (LV) and right (RV) ventricular endocardial contours was performed twice in 10 randomly assigned sessions, each session using only one UID. Segmentation time and the ventricular function variables were recorded. The mean segmentation time and time reduction were calculated for each method. Intraclass correlation coefficients (ICC) and Bland-Altman plots of function variables were used to assess intra- and interobserver variability and agreement between methods. Observers completed a Likert-type questionnaire. RESULTS:The mean segmentation time (in seconds) was significantly less with the stylus compared to the mouse, averaging 206±108 versus 308±125 (p<0.001) and 225±140 versus 353±162 (p<0.001) for LV and RV segmentation, respectively. The intra- and interobserver agreement rates were excellent (ICC≥0.75) regardless of the UID. There was an excellent agreement between measurements derived from manual segmentation using different UIDs (ICC≥0.75), with few exceptions. Observers preferred the stylus. CONCLUSION/CONCLUSIONS:The study shows a significant reduction in segmentation time using the stylus, a subjective preference, and excellent agreement between the methods. KEY POINTS/CONCLUSIONS:• Using a stylus for MRI ventricular segmentation is faster compared to mouse • A stylus is easier to use and results in less fatigue • There is excellent agreement between stylus and mouse UIDs.

OBJECTIVE:This paper presents a framework for temporal shape analysis to capture the shape and changes of anatomical structures from three-dimensional+t(ime) medical scans. METHOD/METHODS:We first encode the shape of a structure at each time point with the spectral signature, i.e., the eigenvalues and eigenfunctions of the Laplace operator. We then expand it to capture morphing shapes by tracking the eigenmodes across time according to the similarity of their eigenfunctions. The similarity metric is motivated by the fact that small-shaped deformations lead to minor changes in the eigenfunctions. Following each eigenmode from the beginning to end results in a set of eigenmode curves representing the shape and its changes over time. RESULTS:We apply our encoding to a cardiac dataset consisting of series of segmentations outlining the right and left ventricles over time. We measure the accuracy of our encoding by training classifiers on discriminating healthy adults from patients that received reconstructive surgery for Tetralogy of Fallot (TOF). The classifiers based on our encoding significantly surpass deformation-based encodings of the right ventricle, the structure most impacted by TOF. CONCLUSION/CONCLUSIONS:The strength of our framework lies in its simplicity: It only assumes pose invariance within a time series but does not assume point-to-point correspondence across time series or a (statistical or physical) model. In addition, it is easy to implement and only depends on a single parameter, i.e., the number of curves.

OBJECTIVES: Residual respiratory motion degrades image quality in conventional cardiac cine MRI (CCMRI). We evaluated whether a free-breathing (FB) radial imaging CCMRI sequence with compressed sensing reconstruction [extradimensional (e.g. cardiac and respiratory phases) golden-angle radial sparse parallel, or XD-GRASP] could provide better image quality than a conventional Cartesian breath-held (BH) sequence in an unselected population of patients undergoing clinical CCMRI. MATERIALS AND METHODS: One hundred one patients who underwent BH and FB imaging in a midventricular short-axis plane at a matching location were included. Visual and quantitative image analysis was performed by two blinded experienced readers, using a five-point qualitative scale to score overall image quality and visual signal-to-noise ratio (SNR) grade, with measures of noise and sharpness. End-diastolic and end-systolic left ventricular areas were also measured and compared for both BH and FB images. RESULTS: Image quality was generally better with the BH cines (overall quality grade for BH vs FB images 4 vs 2.9, p < 0.001; noise 0.06 vs 0.08 p < 0.001; SNR grade 4.1 vs 3, p < 0.001), except for sharpness (p = 0.48). There were no significant differences between BH and FB images regarding end-diastolic or end-systolic areas (p = 0.35 and p = 0.12). Eighteen of the 101 patients had poor BH image quality (grade 1 or 2). In this subgroup, the quality of the FB images was better (p = 0.0032), as was the SNR grade (p = 0.003), but there were no significant differences regarding noise and sharpness (p = 0.45 and p = 0.47). CONCLUSION: Although FB XD-GRASP CCMRI was visually inferior to conventional BH CCMRI in general, it provided improved image quality in the subgroup of patients with respiratory-motion-induced artifacts on BH images.

Contraction of the heart is central to its purpose of pumping blood around the body. While simple global function measures (such as the ejection fraction) are most commonly used in the clinical assessment of cardiac function, MRI also provides a range of approaches for quantitatively characterizing regional cardiac function, including the local deformation (or strain) within the heart wall. While they have been around for some years, these methods are still undergoing further technical development, and they have had relatively little clinical evaluation. However, they can provide potentially useful new ways to assess cardiac function, which may be able to contribute to better classification and treatment of heart disease. This article provides some basic background on the physical and physiological factors that determine the motion of the heart, in health and disease and then reviews some of the ways that MRI methods are being developed to image and quantify strain within the myocardium. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017.