Direct attenuation correction using deep learning for cardiac SPECT: A feasibility study

Jaewon Yang, Luyao Shi, Rui Wang, Edward J. Miller, Albert J. Sinusas, Chi Liu, Grant T. Gullberg, Youngho Seo

Research output: Contribution to journalReview articlepeer-review

10 Scopus citations

Abstract

Purpose: Dedicated cardiac SPECT scanners with cadmium-zinc-telluride (CZT) cameras have shown capabilities of shortened scan times or reduced radiation doses as well as improved image quality. Since most of the dedicated scanners do not have an integrated CT, image quantification with attenuation correction (AC) is challenging and artifacts are routinely encountered in daily clinical practice. In this work, we demonstrate a direct AC technique using deep learning (DL) for myocardial perfusion imaging (MPI). Methods: In an IRB-approved retrospective study, 100 cardiac SPECT/CT datasets with 99mTc-tetrofosmin using a GE Discovery NM/CT 570c scanner were collected at the Yale New Haven Hospital. A U-Net-based network was used for generating attenuation-corrected SPECT (SPECTDL) directly from non-corrected SPECT (SPECTNC) without undergoing an additional image reconstruction step. The accuracy of SPECTDL was evaluated by voxel-wise and segment-wise analyses against the reference CT-based AC (SPECTCTAC) using American Heart Association 17 segments in the myocardium. Polar maps of representative (best/median/worst) cases were visually compared for illustrating potential benefits and pitfalls of the DL approach. Results: The voxel-wise correlations with SPECTCTAC were 92.2% ± 3.7 (slope = 0.87; R2 = 0.81) and 97.7% ± 1.8 (slope = 0.94; R2 = 0.91) for SPECTNC and SPECTDL, respectively. The segmental errors of SPECTNC scattered from -35% up to 21% (p < 0.001); while, the errors of SPECTDL stayed mostly within ±10% (p < 0.001). The average segmental errors (mean±SD) were -6.11 ± 8.06% and 0.49 ± 4.35% for SPECTNC and SPECTDL, respectively. The average absolute segmental errors were 7.96 ± 6.23% and 3.31 ± 2.87% for SPECTNC and SPECTDL, respectively. Review of polar maps revealed successful demonstration of reduced attenuation artifacts; however, the performance of SPECTDL was not consistent for all subjects likely due to different amount of attenuation and uptake patterns. Conclusion: We demonstrated the feasibility of direct AC using DL for SPECT MPI. Overall, our DL approach reduced attenuation artifacts substantially compared to SPECTNC, justifying further studies to establish safety and consistency for clinical applications in stand-alone SPECT systems suffered from attenuation artifacts.

Original languageEnglish (US)
JournalJournal of Nuclear Medicine
Volume62
Issue number11
DOIs
StatePublished - Nov 1 2021
Externally publishedYes

Keywords

  • Attenuation correction
  • Cardiac SPECT
  • Deep learning
  • MPI

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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