ArticleJournal of applied clinical medical physics2020
A technique to generate synthetic CT from MRI for abdominal radiotherapy.
Article in Journal of applied clinical medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 21 citations in OpenAlex.
- Comprehensive evaluation of a deep learning-based synthetic CT model for MR-only radiotherapy across multiple anatomical sites.Physics and imaging in radiation oncology · 2026Article
- Gradient system characterization of a 1.5 T MR-Linac with application to 4D UTE imaging for adaptive MR-guided radiotherapy of lung cancer.Magnetic resonance in medicine · 2025Article
- Proof of concept of fully automated adaptive workflow for head and neck radiotherapy treatments with a conventional linear accelerator.Frontiers in oncology · 2025Article
- The potential role of synthetic computed tomography in spinal surgery: generation, applications, and implications for future clinical practice.Asian spine journal · 2024Article
- Advancements in synthetic CT generation from MRI: A review of techniques, and trends in radiation therapy planning.Journal of applied clinical medical physics · 2024Review
- Feasibility study on the clinical application of CT-based synthetic brain T1-weighted MRI: comparison with conventional T1-weighted MRI.European radiology · 2024Article
- Multi-parametric MRI for radiotherapy simulation.Medical physics · 2023Review
- Multimodal image translation via deep learning inference model trained in video domain.BMC medical imaging · 2022Article
- Synthesis of magnetic resonance images from computed tomography data using convolutional neural network with contextual loss function.Quantitative imaging in medicine and surgery · 2022Article
- Improving the clinical workflow of a MR-Linac by dosimetric evaluation of synthetic CT.Frontiers in oncology · 2022Article
- Synthetic digital reconstructed radiographs for MR-only robotic stereotactic radiation therapy: A proof of concept.Computers in biology and medicine · 2021Article
- Machine learning applications in radiation oncology.Physics and imaging in radiation oncology · 2021Review
- Applying MRI Intensity Normalization on Non-Bone Tissues to Facilitate Pseudo-CT Synthesis from MRI.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo investigate a method to classify tissues types for synthetic CT generation using MRI for treatment planning in abdominal radiotherapy.
methodsAn institutional review board approved volunteer study was performed on a 3T MRI scanner. In-phase, fat and water images were acquired for five volunteers with breath-hold using an mDixon pulse sequence. A method to classify different tissue types for synthetic CT generation in the abdomen was developed. Three tissue clusters (fat, high-density tissue, and spine/air/lungs) were generated using a fuzzy-c means clustering algorithm. The third cluster was further segmented into three sub-clusters that represented spine, air, and lungs. Therefore, five segments were automatically generated. To evaluate segmentation accuracy using the method, the five segments were manually contoured on MRI images as the ground truth, and the volume ratio, Dice coefficient, and Hausdorff distance metric were calculated. The dosimetric effect of segmentation accuracy was evaluated on simulated targets close to air, lungs, and spine using a two-arc volumetric modulated arc therapy (VMAT) technique.
resultsThe volume ratio of auto-segmentation to manual segmentation was 0.88-2.1 for the air segment and 0.72-1.13 for the remaining segments. The range of the Dice coefficient was 0.24-0.83, 0.84-0.93, 0.94-0.98, 0.93-0.96, and 0.76-0.79 for air, fat, lungs, high-density tissue, and spine, respectively. The range of the mean Hausdorff distance was 3-29.1 mm, 0.5-1.3 mm, 0.4-1 mm, 0.7-1.6 mm, and 1.2-1.4 mm for air, fat, lungs, high-density tissue, and spine, respectively. Despite worse segmentation accuracy in air and spine, the dosimetric effect was 0.2% ± 0.2%, with a maximum difference of 0.8% for all target locations.
conclusionA method to generate synthetic CT in the abdomen was developed, and segmentation accuracy and its dosimetric effect were evaluated. Our results demonstrate the potential of using MRI alone for treatment planning in the abdomen.
Indexed as
Identifiers
What OpenQuestion holds
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.