ArticlePhysics in medicine and biology2019
MRI-based attenuation correction for brain PET/MRI based on anatomic signature and machine learning.
Article in Physics in medicine and biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
22 citing papers in PubMed, 50 citations in OpenAlex.
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- Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks.Zeitschrift fur medizinische Physik · 2025Article
- Artificial Intelligence Analysis Using MRI and PET Imaging in Gliomas: A Narrative Review.Cancers · 2024Review
- A review of PET attenuation correction methods for PET-MR.EJNMMI physics · 2023Review
- Monte Carlo Characterization of the Trimage Brain PET System.Journal of imaging · 2022Article
- Automated lesion detection of breast cancer in [Frontiers in oncology · 2022Article
- Hybrid total-body pet scanners-current status and future perspectives.European journal of nuclear medicine and molecular imaging · 2022Review
- Imaging of Bone in the Head and Neck Region, is There More Than CT?Current radiology reports · 2022Review
- Prostate and dominant intraprostatic lesion segmentation on PET/CT using cascaded regional-net.Physics in medicine and biology · 2021Article
- MRI classification using semantic random forest with auto-context model.Quantitative imaging in medicine and surgery · 2021Article
- A review of deep learning based methods for medical image multi-organ segmentation.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2021Review
- Feasibility of Total Variation Noise Reduction Algorithm According to Various MR-Based PET Images in a Simultaneous PET/MR System: A Phantom Study.Diagnostics (Basel, Switzerland) · 2021Article
- A review on medical imaging synthesis using deep learning and its clinical applications.Journal of applied clinical medical physics · 2021Review
- Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2020Review
- Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging.Physics in medicine and biology · 2020Article
- Synthetic CT generation from non-attenuation corrected PET images for whole-body PET imaging.Physics in medicine and biology · 2019Article
- Whole-body PET estimation from low count statistics using cycle-consistent generative adversarial networks.Physics in medicine and biology · 2019Article
- Evaluation of a deep learning-based pelvic synthetic CT generation technique for MRI-based prostate proton treatment planning.Physics in medicine and biology · 2019Article
- MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks.Medical physics · 2019Article
- MRI-based treatment planning for proton radiotherapy: dosimetric validation of a deep learning-based liver synthetic CT generation method.Physics in medicine and biology · 2019Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
Deriving accurate attenuation maps for PET/MRI remains a challenging problem because MRI voxel intensities are not related to properties of photon attenuation and bone/air interfaces have similarly low signal. This work presents a learning-based method to derive patient-specific computed tomography (CT) maps from routine T1-weighted MRI in their native space for attenuation correction of brain PET. We developed a machine-learning-based method using a sequence of alternating random forests under the framework of an iterative refinement model. Anatomical feature selection is included in both training and predication stages to achieve optimal performance. To evaluate its accuracy, we retrospectively investigated 17 patients, each of which has been scanned by PET/CT and MR for brain. The PET images were corrected for attenuation on CT images as ground truth, as well as on pseudo CT (PCT) images generated from MR images. The PCT images showed mean average error of 66.1 ± 8.5 HU, average correlation coefficient of 0.974 ± 0.018 and average Dice similarity coefficient (DSC) larger than 0.85 for air, bone and soft tissue. The side-by-side image comparisons and joint histograms demonstrated very good agreement of PET images after correction by PCT and CT. The mean differences of voxel values in selected VOIs were less than 4%, the mean absolute difference of all active area is around 2.5%, and the mean linear correlation coefficient is 0.989 ± 0.017 between PET images corrected by CT and PCT. This work demonstrates a novel learning-based approach to automatically generate CT images from routine T1-weighted MR images based on a random forest regression with patch-based anatomical signatures to effectively capture the relationship between the CT and MR images. Reconstructed PET images using the PCT exhibit errors well below accepted test/retest reliability of PET/CT indicating high quantitative equivalence.
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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 Socratic graph.