Automatic detection of left and right ventricles from CTA enables efficient alignment of anatomy with myocardial perfusion data

• Type of Material: Article
• Authors: Marina Piccinelli, Emory University; Tracy L. Faber, Emory University; Chesnal D. Arepalli, Emory University; Vikram Appia, Georgia Institute of Technology; Jakob Vinten-Johansen, Emory University; Susan L. Schmarkey, Emory University; Russell Folks, Emory University; Ernest Garcia, Emory University; Anthony Yezzi, Georgia Institute of Technology
• Language: English
• Date: 2014-02-01
• Publisher: Springer Verlag (Germany)
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © American Society of Nuclear Cardiology 2013.
• Final Published Version (URL): http://dx.doi.org/10.1007/s12350-013-9812-1
• Title of Journal or Parent Work: Journal of Nuclear Cardiology
• ISSN: 1071-3581
• Volume: 21
• Issue: 1
• Start Page: 96
• End Page: 108
• Grant/Funding Information: This work was supported in part by NIH grant R01-HL-085417 from NHLBI and by the EMTech Bio Collaborative Grant program.
• Keywords: COMPUTED-TOMOGRAPHY ANGIOGRAPHY; image fusion; CARDIAC CT; PET/CT imaging; Science & Technology; SEGMENTATION; Life Sciences & Biomedicine; coronary artery disease; Radiology, Nuclear Medicine & Medical Imaging; Cardiac & Cardiovascular Systems; SPECT; PORCINE MODEL; Cardiovascular System & Cardiology
• Research Categories: Health Sciences, Radiology; Health Sciences, Medicine and Surgery

Abstract

Background: Accurate alignment between cardiac CT angiographic studies (CTA) and nuclear perfusion images is crucial for improved diagnosis of coronary artery disease. This study evaluated in an animal model the accuracy of a CTA fully automated biventricular segmentation algorithm, a necessary step for automatic and thus efficient PET/CT alignment. Methods and Results: Twelve pigs with acute infarcts were imaged using Rb-82 PET and 64-slice CTA. Post-mortem myocardium mass measurements were obtained. Endocardial and epicardial myocardial boundaries were manually and automatically detected on the CTA and both segmentations used to perform PET/CT alignment. To assess the segmentation performance, image-based myocardial masses were compared to experimental data; the hand-traced profiles were used as a reference standard to assess the global and slice-by-slice robustness of the automated algorithm in extracting myocardium, LV and RV. Mean distances between the automated and the manual 3D segmented surfaces were computed. Finally, differences in rotations and translations between the manual and automatic surfaces were estimated post PET/CT alignment. The largest, smallest, and median distances between interactive and automatic surfaces averaged 1.2±2.1, 0.2±1.6, and 0.7±1.9mm. The average angular and translational differences in CT/PET alignments were 0.4°, −0.6° and −2.3° about x, y and z axes, and 1.8, −2.1, and 2.0 mm in x, y and z directions. Conclusions: Our automatic myocardial boundary detection algorithm creates surfaces from CTA that are similar in accuracy and provide similar alignments with PET as those obtained from interactive tracing. Specific difficulties in a reliable segmentation of the apex and base regions will require further improvements in the automated technique.