PURPOSE: 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. METHODS: Tc-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. RESULTS: 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). CONCLUSION: 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.

Designing compiler intermediate representations (IRs) is often a manual process that makes exploration and innovation in this space costly. Developers typically use general-purpose programming languages to design IRs. As a result, IR implementations are verbose, manual modifications are expensive, and designing tooling for the inspection or generation of IRs is impractical. While compilers relied historically on a few slowly evolving IRs, domain-specific optimizations and specialized hardware motivate compilers to use and evolve many IRs. We facilitate the implementation of SSA-based IRs by introducing IRDL, a domain-specific language to define IRs. We analyze all 28 domain-specific IRs developed as part of LLVM's MLIR project over the last two years and demonstrate how to express these IRs exclusively in IRDL while only rarely falling back to IRDL's support for generic C++ extensions. By enabling the concise and explicit specification of IRs, we provide foundations for developing effective tooling to automate the compiler construction process.

This work presents MLIR, a novel approach to building reusable and extensible compiler infrastructure. MLIR addresses software fragmentation, compilation for heterogeneous hardware, significantly reducing the cost of building domain specific compilers, and connecting existing compilers together. MLIR facilitates the design and implementation of code generators, translators and optimizers at different levels of abstraction and across application domains, hardware targets and execution environments. The contribution of this work includes (1) discussion of MLIR as a research artifact, built for extension and evolution, while identifying the challenges and opportunities posed by this novel design, semantics, optimization specification, system, and engineering. (2) evaluation of MLIR as a generalized infrastructure that reduces the cost of building compilers-describing diverse use-cases to show research and educational opportunities for future programming languages, compilers, execution environments, and computer architecture. The paper also presents the rationale for MLIR, its original design principles, structures and semantics.