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

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.

Validation of Automated Biventricular Myocardial Segmentation from Coronary Computed Tomographic Angiography for Multimodality Image Fusion

“…While the results favorably compare with other techniques and methodologies, an additional validation will be necessary to assess whether the accuracy of the present automated segmentation is sufficient to guarantee the correct alignment of CCTA and cardiac MPI (either SPECT or PET) and consequently the fusion of anatomic structures, namely the patient-specific coronaries, to functional data for diagnosis purposes. A similar study was previously published by our team on our animal model experiments 19 .…”

“…Currently CCTA/nuclear myocardial perfusion imaging (MPI) fusion frameworks exist as research applications 6,7 or in systems that do not rely on the explicit CCTA segmentation but rather on the visualization of the two datasets with volume rendering techniques 8 . In contrast, our team has been concentrating on the creation of frameworks that explicitly obtain a 3D representation of the heart anatomy 9-11 to be displayed in combination with coronary trees and with functional quantitative assessments such as ischemic areas 12 , PET-derived absolute blood flow quantification 7 , and myocardium at risk 6 . Clinical implementation of our quantitative fusion technique requires that our software have access to accurately and quickly segmented nuclear perfusion and CCTA cardiac imagery.…”

Integrated 3D anatomical model for automatic myocardial segmentation in cardiac CT imagery

“…We have presented a novel combination and extension of existing frameworks to automatically segment myocardial boundaries in cardiac CT images. Among their various applications, the proposed methodologies have been developed also in the context of a project for multimodality image fusion (Piccinelli et al 2014). Function and anatomy are equally important in the assessment of Coronary Artery Disease.…”

Vessel-specific quantification of absolute myocardial blood flow, myocardial flow reserve and relative flow reserve by means of fused dynamic 13NH3 PET and CCTA: Ranges in a low-risk population and abnormality criteria

“…Our team has actively been working in the development of automated techniques. This automated methodology was previously tested on an animal model 18 and is presently being validated in a large database of CCTAs. As only vessel centerlines are required, there are number of approaches available to automate this step 19 .…”