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Updated: May 3, 2026
MRI and PET in Mouse Models of Myocardial Infarction
Published on: December 19, 2013
Deep learning approach for automated segmentation of myocardium using bone scintigraphy single-photon emission
Abhijit Bhattaru1, Chaitanya Rojulpote2, Mahesh Vidula3
1Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Cardiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, Rutgers New Jersey Medical School, Newark, NJ, USA.
Background:
We employed deep learning to automatically detect myocardial bone-seeking uptake as a marker of transthyretin cardiac amyloid cardiomyopathy (ATTR-CM) in patients undergoing 99mTc-pyrophosphate (PYP) or hydroxydiphosphonate (HDP) single-photon emission computed tomography (SPECT)/computed tomography (CT).
Methods:
We identified a primary cohort of 77 subjects at Brigham and Women's Hospital and a validation cohort of 93 consecutive patients imaged at the University of Pennsylvania who underwent SPECT/CT with PYP and HDP, respectively, for evaluation of ATTR-CM. Global heart regions of interest (ROIs) were traced on CT axial slices from the apex of the ventricle to the carina. Myocardial images were visually scored as grade 0 (no uptake), 1 (uptake
Results:
Deep learning and ground truth segmentations were comparable (p=0.63). A total of 42 (55%) patients had abnormal myocardial uptake on visual assessment. Automated quantification of the mean HBP ratio in the primary cohort was 3.1±1.4 versus 1.4±0.2 (p<0.01) for patients with positive and negative cardiac uptake, respectively. The model had 100% accuracy in the primary cohort and 98% in the validation cohort.
Conclusion:
We have developed a highly accurate diagnostic tool for automatically segmenting and identifying myocardial uptake suggestive of ATTR-CM.
