Vectors through a cross-sectional image (VCI): A visualization method for four-dimensional motion analysis for cardiac computed tomography

Abstract

Background Cardiac computed tomography (CT) has the potential for fully four-dimensional (4D for 3D plus time) motion analysis of the heart. We aimed at developing a method for assessment and presentation of the 4D motion for multi-phase, contrast-enhanced cardiac CT data sets and demonstrating its clinical applicability. Methods Four patients with normal cardiac function, old myocardial infarction (OMI), takotsubo cardiomyopathy, and hypertrophic cardiomyopathy (HCM) underwent contrast-enhanced cardiac CT for one heartbeat using a 320-row CT scanner with no tube current modulation. CT images for 10 cardiac phases (with a 10%-increment of the R-R interval) were reconstructed with the isotropic effective resolution of (0.5 mm)3. An image-based motion-estimation (iME) algorithm, developed previously, has been used to estimate a time series of 3D cardiac motion, from the end-systole to the other nine phases. The iME uses down-sampled images with a resolution of (1.0 mm)3, deforms the end-systole images non-rigidly to match images at other phases. Once the agreement is maximized, iME outputs a 3D motion vector defined for each voxel for each phase, that smoothly changes over voxels and phases. The proposed visualization method, which is called “vectors through a cross-sectional image (VCI),” presents 3D vectors from the end-diastole to the end-systole as arrows with an end-diastole CT slice. We performed visual assessment of the VCI with calculated the mean vector lengths to evaluate regional left ventricular (LV) contraction. Results The VCI images showed the magnitude and direction of systolic 3D vectors, including the through-plane motion, and successfully visualized the relations of LV wall segments and abnormal regional wall motion. Decreased regional motion and asymmetric motion due to hypokinetic infarct segment, takotsubo cardiomyopathy, and hyper trophic cardiomyopathy was clearly observed. It was easy to appreciate the relation of the abnormal regional wall motion to the affected LV wall segments. The mean vector lengths of the affected segments with pathologies were clearly smaller than the other unaffected segments (1.2–1.7 mm versus 2.5–4.7 mm). Conclusions VCI images could capture the magnitude and direction of through-plane motion and show the relations of LV wall segments and abnormal wall motion.

An ALE Meta-Analysis of Specific Functional MRI Studies on Subcortical Vascular Cognitive Impairment

“…Located at the center of the visual cortex, the LING and the cuneus are in charge of visual function processes ( 48 ). A randomized controlled trial confirmed that visual dysfunction may exist in sVCI and regular ophthalmological exams can help improve the quality of life ( 49 ). Our findings were consistent with the clinical symptoms.…”

“…There was a synergistic effect between the networks. When the DMN and ECN are abnormal, the VN will also be functionally damaged ( 49 ). As for the compensatory increase in the VN in the later period, more experiments are needed to prove this.…”

“…As mentioned in the DMN above, the insula is connected to the anterior PCUN, both of which are reduced. Thus, brain regions and brain networks interact with each other in sVCI patients ( 49 ).…”

A Web-Based Synchronized Architecture for Collaborative Dynamic Diagnosis and Therapy Planning

“…Meanwhile real-time 3D echocardiographic images were employed by Suematsu and his colleagues [37] to guide beating heart atrial septal defect closure. Zhang et al [38] designed a stereoscopic cardiac image dynamic visualization platform to provide cardiologists with real-time feedback regarding heart anatomical information, Kidoh et al [39] reported a rendering method for motion analysis of cardiac computed tomography, and the impact of visualizing 4D flow on diagnosis and surgical planning was demonstrated by Christopher et al [40]. However, these software platforms were limited to local computers without Internet-based synchronization or medical information streaming, so were unsuitable for collaborative analysis and treatment planning [41].…”