Masked Co-attentional Transformer reconstructs 100x ultra-fast/low-dose whole-body PET from longitudinal images and anatomically guided MRI
Yan-Ran (Joyce) Wang, Liangqiong Qu, Natasha Diba Sheybani, Xiaolong, Luo, Jiangshan Wang, Kristina Elizabeth Hawk, Ashok Joseph Theruvath, Sergios, Gatidis, Xuerong Xiao, Allison Pribnow, Daniel Rubin, and Heike E., Daldrup-Link

TL;DR
This paper introduces Masked-LMCTrans, a novel CNN-Transformer model that reconstructs ultra-low-dose, fast whole-body PET scans from baseline images, enabling safer, quicker, and more frequent longitudinal cancer imaging in children.
Contribution
The study presents a new longitudinal multi-modality co-attentional CNN-Transformer that leverages baseline PET/MR similarity to reconstruct ultra-low-dose PET scans, achieving 100x reduction in radio-exposure.
Findings
Reconstructed 100x ultra-low-dose PET images with high quality.
Validated model on pediatric lymphoma PET/MRI data from Stanford and Tübingen.
Enabled safer, faster, and more frequent longitudinal PET imaging.
Abstract
Despite its tremendous value for the diagnosis, treatment monitoring and surveillance of children with cancer, whole body staging with positron emission tomography (PET) is time consuming and associated with considerable radiation exposure. 100x (1% of the standard clinical dosage) ultra-low-dose/ultra-fast whole-body PET reconstruction has the potential for cancer imaging with unprecedented speed and improved safety, but it cannot be achieved by the naive use of machine learning techniques. In this study, we utilize the global similarity between baseline and follow-up PET and magnetic resonance (MR) images to develop Masked-LMCTrans, a longitudinal multi-modality co-attentional CNN-Transformer that provides interaction and joint reasoning between serial PET/MRs of the same patient. We mask the tumor area in the referenced baseline PET and reconstruct the follow-up PET scans. In this…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMedical Imaging Techniques and Applications · Radiation Detection and Scintillator Technologies · Advanced Radiotherapy Techniques
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
