ArticlePhysics in medicine and biology2020
Attenuation correction for human PET/MRI studies.
Article in Physics in medicine and biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Joint EANM-SNMMI guideline on the role of 2-[European journal of nuclear medicine and molecular imaging · 2024Guideline
- Pooled it
- A two-stage PET/MRI attenuation-correction workflow for pseudo-CT and AC PET synthesis with GUI-based inference.Medical & biological engineering & computing · 2026Article
- [ 18 F]fluorodeoxyglucose PET/MRI for nononcological musculoskeletal indications: reference uptake values in the asymptomatic lumbar spine and example comparison to a low back pain patient.Nuclear medicine communications · 2026Article
- Sequential scans ofJapanese journal of radiology · 2026Article
- A unified deep learning framework for cross-platform harmonization of multi-tracer PET quantification in neurodegenerative disease.NPJ digital medicine · 2026Article
- Deep learning applications for quantitative and qualitative PET in PET/MR: technical and clinical unmet needs.Magma (New York, N.Y.) · 2024Review
- The pons as an optimal background reference region for spinalEJNMMI research · 2024Article
- Application of PET/MRI in Gynecologic Malignancies.Cancers · 2024Review
- Reduction of [EJNMMI physics · 2024Article
- Supplemental Transmission Aided Attenuation Correction for Quantitative Cardiac PET.IEEE transactions on medical imaging · 2024Article
- New Horizons in Brain PET Instrumentation.PET clinics · 2024Review
- DeepDixon synthetic CT for [Frontiers in neuroscience · 2023Article
- Pelvic PET/MR attenuation correction in the image space using deep learning.Frontiers in oncology · 2023Article
- Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners.European journal of nuclear medicine and molecular imaging · 2022Article
- Multimodal image synthesis based on disentanglement representations of anatomical and modality specific features, learned using uncooperative relativistic GAN.Medical image analysis · 2022Article
- A Path to Qualification of PET/MRI Scanners for Multicenter Brain Imaging Studies: Evaluation of MRI-Based Attenuation Correction Methods Using a Patient Phantom.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2022Article
- Evaluation of Deep Learning-Based Approaches to Segment Bowel Air Pockets and Generate Pelvic Attenuation Maps from CAIPIRINHA-Accelerated Dixon MR Images.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2022Article
- Monte Carlo Characterization of the Trimage Brain PET System.Journal of imaging · 2022Article
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Abstract
Attenuation correction has been one of the main methodological challenges in the integrated positron emission tomography and magnetic resonance imaging (PET/MRI) field. As standard transmission or computed tomography approaches are not available in integrated PET/MRI scanners, MR-based attenuation correction approaches had to be developed. Aspects that have to be considered for implementing accurate methods include the need to account for attenuation in bone tissue, normal and pathological lung and the MR hardware present in the PET field-of-view, to reduce the impact of subject motion, to minimize truncation and susceptibility artifacts, and to address issues related to the data acquisition and processing both on the PET and MRI sides. The standard MR-based attenuation correction techniques implemented by the PET/MRI equipment manufacturers and their impact on clinical and research PET data interpretation and quantification are first discussed. Next, the more advanced methods, including the latest generation deep learning-based approaches that have been proposed for further minimizing the attenuation correction related bias are described. Finally, a future perspective focused on the needed developments in the field is given.
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