Evidence map›Paper›PMID 42142396›Full record

ArticleJournal of applied clinical medical physics2026

Synthetic CT imaging for pelvic and abdominal MR-only radiotherapy: Clinical validation on a 1.5T MR-Linac.

Colin Gaban, Olivier Pisaturo, Raphael Moeckli, Marc Pachoud, Sarah Ghandour

Abstract readValidation Study
In one paragraph

Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Colin GabanDepartment of Radiotherapy, Riviera-Chablais Hospital (HRC), Rennaz, Switzerland.
Olivier PisaturoDepartment of Radiotherapy, Riviera-Chablais Hospital (HRC), Rennaz, Switzerland.
Raphael MoeckliInstitute of Radiation Physics, University Hospital and Lausanne University (CHUV), Lausanne, Switzerland.ORCID https://orcid.org/0000-0002-5885-934X
Marc PachoudDepartment of Radiotherapy, Riviera-Chablais Hospital (HRC), Rennaz, Switzerland.ORCID https://orcid.org/0000-0001-9036-9626
Sarah GhandourDepartment of Radiotherapy, Riviera-Chablais Hospital (HRC), Rennaz, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMagnetic Resonance (MR)- only workflows are fundamentally limited by the absence of electron density information required for accurate photon dose calculations, necessitating supplementary CT imaging. Synthetic CT (sCT) generation algorithms offer a promising solution to eliminate this dependency. While several commercially available sCT solutions exist, their clinical scope remains highly restricted to specific MRI sequences and anatomical regions. PURPOSE: To evaluate two deep learning-based sCT generation models for pelvic and abdominal anatomies using both T1 and T2-weighted MR sequences. This study aims to validate their potential for enabling streamlined MR-only radiotherapy workflows eliminating the need for supplementary CT imaging while reducing patient burden and workflow complexity.

methods31 patients with multiple MRI sequences underwent sCT generation using two deep learning models (Image+, MVision AI). Geometric accuracy and Hounsfield Unit (HU) fidelity were assessed through quantitative comparison with deformed CTs (dCT) registered to MRI geometry. Dosimetric validation was performed by comparing dose distributions using γ-index pass rates between sCT-based plans and two reference standards: deformed CT (dCT) and clinical bulk-density CT (bCT).

resultsMean Dice similarity coefficients demonstrated good geometric agreement: 0.73 ± 0.18 (air cavities), 0.86 ± 0.06 (adipose tissue), 0.85 ± 0.04 (soft tissue), and 0.75 ± 0.07 (bone). Dosimetric evaluation revealed averaged relative dose differences of 0.31% ± 0.98% (bCT reference) and 0.16% ± 1.13% (dCT reference) across anatomical structures. Clinical dose agreement within 2% was achieved in 98.8% of pelvic cases and 93.3% of abdominal cases.

conclusionMRI-based sCTs from typical T1 or T2-weighted MR linac sequences demonstrated strong geometric and dosimetric agreement with dCT, particularly in the pelvic region, with performance appearing independent of MR sequence type. These results support the clinical viability of sCTs as an alternative to conventional CT, establishing the feasibility of comprehensive MR-only RT workflows from simulation through treatment delivery.

Indexed as

Abdominal NeoplasmsAlgorithmsImage Processing, Computer-AssistedMagnetic Resonance ImagingParticle AcceleratorsPelvic NeoplasmsPelvisRadiotherapy Planning, Computer-AssistedTomography, X-Ray ComputedFemaleHumansMaleOrgans at RiskRadiotherapy DosageRadiotherapy, Intensity-ModulatedMR‐LinacMR‐only workflowsynthetic CT

Identifiers

PMID42142396
PMCPMC13179839

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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.