ArticleMagnetic resonance in medicine2020
Deep learning-based MR-to-CT synthesis: The influence of varying gradient echo-based MR images as input channels.
Article in Magnetic resonance in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.
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Who cites it
55 citing papers in PubMed, 1 synthesis or guideline pooled it, 120 citations in OpenAlex.
- A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.Frontiers in radiology · 2024Pooled it
- AI in Proton Therapy Treatment Planning: A Narrative Review.International journal of particle therapy · 2026Article
- Radiation-free assessment of the 3D morphology of the adolescent scoliotic spine: a feasibility study using MRI derived quantitative synthetic CT.Spine deformity · 2026Article
- Bibliometric Analysis of Whole-Body MRI from 2015 to 2025 Across Clinical Applications, Quantitative Imaging, and Artificial Intelligence.Journal of imaging · 2026Article
- Review
- An Adaptive Attention 3D U-Net for High-Fidelity MRI-to-CT Synthesis: Bridging the Anatomical Gap with CBAM.Diagnostics (Basel, Switzerland) · 2026Article
- Current State-of-the-Art 3D MRI Sequences for Assessing Bone Morphology with Emphasis on Cranial and Spinal Imaging: A Narrative Review.RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin · 2026Review
- Dental metal artifacts in magnetic resonance-based synthetic computed tomography for brain radiotherapy: Impact on dose, patient setup, and geometric distortion.Physics and imaging in radiation oncology · 2026Article
- Radiological evaluation and clinical implications of deep learning- and MRI-based synthetic CT for the assessment of cervical spine injuries.European radiology · 2025Article
- Spondyloarthritis endgame: MRI versus BoneMRI in sacroiliitis.Skeletal radiology · 2025Review
- Accuracy and Reliability of Synthetic Computed Tomography for Model-Based Tracking of Biplane Videoradiography Data.Annals of biomedical engineering · 2025Article
- Article
- Article
- MAN-GAN: a mask-adaptive normalization based generative adversarial networks for liver multi-phase CT image generation.Scientific reports · 2025Article
- Evaluation of MRI-based synthetic CT for lumbar degenerative disease: a comparison with CT.Scientific reports · 2025Article
- Deep-learning reconstructed lumbar spine 3D MRI for surgical planning: pedicle screw placement and geometric measurements compared to CT.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2024Article
- Synthetic CT generation based on multi-sequence MR using CycleGAN for head and neck MRI-only planning.Biomedical engineering letters · 2024Article
- Feasibility study on the clinical application of CT-based synthetic brain T1-weighted MRI: comparison with conventional T1-weighted MRI.European radiology · 2024Article
- Clinical feasibility of deep learning based synthetic contrast enhanced abdominal CT in patients undergoing non enhanced CT scans.Scientific reports · 2024Article
- Morphometric and clinical comparison of MRI-based synthetic CT to conventional CT of the hip in children.Pediatric radiology · 2024Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo study the influence of gradient echo-based contrasts as input channels to a 3D patch-based neural network trained for synthetic CT (sCT) generation in canine and human populations.
methodsMagnetic resonance images and CT scans of human and canine pelvic regions were acquired and paired using nonrigid registration. Magnitude MR images and Dixon reconstructed water, fat, in-phase and opposed-phase images were obtained from a single T
resultsSeventeen canines and 23 human subjects were included in the study. Performance and repeatability of single-channel models were dependent on the TE-related water-fat interference with variations of up to 17% in mean absolute error, and variations of up to 28% specifically in bones. Repeatability, Dice similarity coefficient, and mean absolute error were statistically significantly better in multichannel models with mean absolute error ranging from 33 to 40 Hounsfield units in humans and from 35 to 47 Hounsfield units in canines.
conclusionSignificant differences in performance and robustness of deep learning models for synthetic CT generation were observed depending on the input. In-phase images outperformed opposed-phase images, and Dixon reconstructed multichannel inputs outperformed single-channel inputs.
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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.