ArticleIEEE transactions on medical imaging2024
Spach Transformer: Spatial and Channel-Wise Transformer Based on Local and Global Self-Attentions for PET Image Denoising.
Article in IEEE transactions on medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 53 citations in OpenAlex.
- Deep learning-based techniques for estimating high-quality full-dose positron emission tomography images from low-dose scans: a systematic review.BMC medical imaging · 2024Pooled it
- PAT-Mamba: Adaptive all-in-one image restoration for photoacoustic tomography via state space model.Photoacoustics · 2026Article
- Scan-wise generalized PET denoising with contrastive adversarial learning.Physics in medicine and biology · 2026Article
- Dose-aware diffusion model for 3D PET image denoising: Multi-institutional validation with reader study and real low-dose data.Medical image analysis · 2026Article
- Low-count whole-body PET denoising with deep learning in a multicenter, multi-tracer and externally validated study.European journal of nuclear medicine and molecular imaging · 2026Article
- LeqMod: Adaptable Lesion-Quantification-Consistent Modulation for Deep Learning Low-Count PET Image Denoising.IEEE transactions on medical imaging · 2026Article
- A Cross-modality Transformer Network for MR-guided Low-dose Tau PET Image Denoising.IEEE transactions on radiation and plasma medical sciences · 2026Article
- Anatomically and metabolically informed diffusion for unified denoising and segmentation in low-count PET imaging.Medical image analysis · 2026Article
- Targeted Nanoboron Delivery Platform for Boron Neutron Capture Therapy in Refractory Solid Tumors.International journal of nanomedicine · 2026Review
- Multi-modal low-dose medical imaging through instruction-guided unified AI.Frontiers in medicine · 2026Article
- DME-RWKV: An Interpretable Multimodal Deep Learning Framework for Predicting Anti-VEGF Response in Diabetic Macular Edema.Bioengineering (Basel, Switzerland) · 2025Article
- DG-DiT: Dual-Branch Gating Diffusion Transformer for Multi-Tracer and Multi-Scanner Brain PET Image Denoising.IEEE transactions on radiation and plasma medical sciences · 2025Article
- PETFormer-SCL: a supervised contrastive learning-guided CNN-transformer hybrid network for Parkinsonism classification from FDG-PET.Annals of nuclear medicine · 2025Article
- Detail-preserving denoising of CT and MRI images via adaptive clustering and non-local means algorithm.Scientific reports · 2025Article
- Robust whole-body PET image denoising using 3D diffusion models: evaluation across various scanners, tracers, and dose levels.European journal of nuclear medicine and molecular imaging · 2025Article
- DepthLux: Employing Depthwise Separable Convolutions for Low-Light Image Enhancement.Sensors (Basel, Switzerland) · 2025Article
- An ensemble deep learning framework for multi-class LncRNA subcellular localization with innovative encoding strategy.BMC biology · 2025Article
- Enhancing Low-Light Images with Kolmogorov-Arnold Networks in Transformer Attention.Sensors (Basel, Switzerland) · 2025Article
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
- Head and neck tumor segmentation from [Physics in medicine and biology · 2024Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
Position emission tomography (PET) is widely used in clinics and research due to its quantitative merits and high sensitivity, but suffers from low signal-to-noise ratio (SNR). Recently convolutional neural networks (CNNs) have been widely used to improve PET image quality. Though successful and efficient in local feature extraction, CNN cannot capture long-range dependencies well due to its limited receptive field. Global multi-head self-attention (MSA) is a popular approach to capture long-range information. However, the calculation of global MSA for 3D images has high computational costs. In this work, we proposed an efficient spatial and channel-wise encoder-decoder transformer, Spach Transformer, that can leverage spatial and channel information based on local and global MSAs. Experiments based on datasets of different PET tracers, i.e., 18F-FDG, 18F-ACBC, 18F-DCFPyL, and 68Ga-DOTATATE, were conducted to evaluate the proposed framework. Quantitative results show that the proposed Spach Transformer framework outperforms state-of-the-art deep learning architectures.
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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.