ArticleIEEE transactions on radiation and plasma medical sciences2023
Training End-to-End Unrolled Iterative Neural Networks for SPECT Image Reconstruction.
Article in IEEE transactions on radiation and plasma medical sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed.
- Artificial Intelligence Across the Cancer Theranostics Workflow: Critical Appraisal of Current Evidence and Future Clinical Translation.Molecular imaging and biology · 2026Review
- Shorter SPECT scans using self-supervised coordinate learning to synthesize skipped projection views.EJNMMI physics · 2025Article
- AI in SPECT Imaging: Opportunities and Challenges.Seminars in nuclear medicine · 2025Review
- A review of state-of-the-art resolution improvement techniques in SPECT imaging.EJNMMI physics · 2025Review
- DuDoCFNet: Dual-Domain Coarse-to-Fine Progressive Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECT.IEEE transactions on medical imaging · 2024Article
- Super-resolution techniques for biomedical applications and challenges.Biomedical engineering letters · 2024Review
- Deep learning-based PET image denoising and reconstruction: a review.Radiological physics and technology · 2024Review
- 90Y SPECT scatter estimation and voxel dosimetry in radioembolization using a unified deep learning framework.EJNMMI physics · 2023Article
- Physics-Guided Deep Scatter Estimation by Weak Supervision for Quantitative SPECT.IEEE transactions on medical imaging · 2023Article
- AI for PET image reconstruction.The British journal of radiology · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Training end-to-end unrolled iterative neural networks for SPECT image reconstruction requires a memory-efficient forward-backward projector for efficient backpropagation. This paper describes an open-source, high performance Julia implementation of a SPECT forward-backward projector that supports memory-efficient backpropagation with an exact adjoint. Our Julia projector uses only ~5% of the memory of an existing Matlab-based projector. We compare unrolling a CNN-regularized expectation-maximization (EM) algorithm with end-to-end training using our Julia projector with other training methods such as gradient truncation (ignoring gradients involving the projector) and sequential training, using XCAT phantoms and virtual patient (VP) phantoms generated from SIMIND Monte Carlo (MC) simulations. Simulation results with two different radionuclides (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.