Reconstruction-informed and multidomain deep learning for generalizable CT-free attenuation correction in SPECT...

Deep learning (DL) has shown promise in enabling attenuation correction (AC) for SPECT myocardial perfusion imaging (MPI) without relying on anatomical information or CT-derived attenuation maps (ATMs). We introduce a novel reconstruction-informed and multidomain (RIMD) DL framework utilizing both multi-input reconstruction and dual-domain supervision for AC in SPECT MPI. Our method incorporates multi-input non-AC (NAC) images as input to the DL model and employs a combined loss function that optimizes performance in both the ATM and AC domains.

A dataset of 1058 SPECT/CT MPI scans using ^99mTc-Sestamibi from two centers was used for training (934 cases) and an external test set (124 cases). SPECT projections were reconstructed into AC and NAC images using three reconstruction settings of the Ordered Subset - Expectation Maximization (OSEM) algorithm. SwinUnetR model was trained in a consistent 5-fold cross-validation framework with normalized NAC images as input. Our proposed method, as an indirect strategy, uses multi-input NAC images (incorporating all three images with different reconstruction settings) trained using a combination of ATM loss (between predicted and true ATMs) and AC loss (between AC images reconstructed from predicted ATMs and reference AC images). Evaluation included voxel-wise and region-wise metrics for both ATM and AC domains, 17-segment polar map analysis, and an organ-specific analysis. Clinical validation was also performed on part of the external dataset.

Our proposed approach significantly outperformed direct and indirect methods in ablation comparison. This model yielded mean relative absolute error percentage (MRAE%) values of 25.02 ± 23 (internal) and 26.31 ± 14 (external) for ATMs, and 11.72 ± 6.3 (internal) and 19.31 ± 4.9 (external) for AC SPECT images. Organ-wise analysis showed region-wise MRAE% of 9.29 ± 6.5 (internal) and 17.28 ± 17 (external) in the ATM domain, and 4.51 ± 4.3 (internal) and 9.53 ± 6.6 (external) in the AC domain. Polar map analysis across 17 segments showed MRAE% of 5.04 ± 4.6 (internal) and 10.88 ± 7.3 (external). Clinical validation demonstrated high agreement between DLAC and CTAC images (ICC = 0.98), with physicians unable to distinguish between them (F1 score = 0.40), and no significant difference in diagnostic accuracy (DLAC: 0.63, CTAC: 0.70; p = 0.37).

This study demonstrated that our proposed RIMD method utilizing multiple OSEM reconstruction inputs and jointly optimizing ATM and AC losses substantially improved model performance in the indirect strategy. The indirect method consistently outperformed the direct approach, and our model generalized well on external data, showing strong agreement with SPECT CTAC images in both quantitative and qualitative assessments. Preliminary clinical evaluation suggested comparable interpretability between DLAC and CTAC under controlled validation conditions.