Narrative review finds synthetic CT clinical validation remains limited

Deep-learning systems that convert cone-beam computed tomography into synthetic CT images can improve image quality and produce encouraging dosimetric results, but a major narrative review concludes that none has yet demonstrated reliable performance across anatomical sites, scanners, treatment modalities and real clinical workflows. The evidence is strongest for retrospective, single-center development studies and remains limited for independent validation, prospective operation, failure detection and quality assurance. Researchers say the technology is not yet supported for routine adaptive radiotherapy without evidence tailored to a clearly defined clinical purpose.

The appeal of synthetic CT, or sCT, lies in a fundamental limitation of cone-beam CT acquired with medical linear accelerators. CBCT can show the anatomy of a patient during a treatment fraction, making it useful for positioning and adaptive radiotherapy, but scatter, beam hardening, noise, metal artifacts and weak soft-tissue contrast can distort the Hounsfield unit values used to infer tissue density. An sCT model attempts to correct those defects while retaining the fraction-specific anatomy visible in CBCT. The resulting image is intended not merely to look like conventional CT, but to provide quantitative information that can support electron-density estimation, dose recalculation, treatment-plan evaluation and adaptation decisions.

The review organizes the field into four overlapping methodological families. Supervised deterministic regression, commonly based on convolutional neural networks and U-Net architectures, learns a direct mapping from CBCT to CT-equivalent images. Adversarial systems such as Pix2Pix and CycleGAN use discriminator-based training to generate images resembling the CT domain. Registration-aware approaches explicitly model spatial mismatch between CBCT and a reference CT, while probabilistic diffusion models learn to reconstruct an image through an iterative, CBCT-conditioned denoising process. Hybrid systems combine these elements, and their defining feature determines where the review places them. No method is consistently superior across every anatomical region, imaging device and treatment modality.

Supervised regression generally provides stable optimization and relatively rapid inference when well-aligned CBCT–CT pairs are available. CNNs and U-Nets can reduce intensity bias and improve Hounsfield-unit consistency, while additional contextual or feature-based losses may help preserve tissue boundaries. Their weakness is that voxel-wise objectives can average several plausible solutions and produce overly smooth images, and skip connections can carry input artifacts into the output. Transformers and vision transformers can capture long-range spatial relationships, including in a three-dimensional head-and-neck proton therapy study, but require greater data, memory and computing resources. Registration errors remain a fundamental problem because anatomical change, positioning differences and organ filling can be learned as false intensity relationships.

Adversarial methods may improve texture and local detail beyond direct regression, but they introduce different risks. Pix2Pix combines adversarial learning with a paired reconstruction loss and therefore depends on relatively close spatial correspondence. CycleGAN can learn from unpaired CBCT and CT collections, easing data collection but weakening direct geometric supervision. Such unpaired translation can produce CT-like appearance while altering local structure or inventing features that are not present in the patient. Registration-aware systems such as RegGAN treat residual misalignment as part of the optimization rather than leaving it as an unexplained disturbance. Head-and-neck evaluations have reported improvements in Hounsfield-unit, structural and dosimetric endpoints relative to corresponding generators without explicit registration modeling, although performance still depends on the accuracy of the deformation model and the breadth of the training data.

Diffusion models form one of the four major methodological families examined in the review. They learn a forward noising process and a reverse denoising process, progressively generating an sCT under CBCT guidance without relying on the generator–discriminator min–max optimization used in adversarial systems. A conditional diffusion probabilistic model reportedly outperformed a supervised CycleGAN comparator on image metrics and visual results for paired brain and head-and-neck data under the same registration and evaluation conditions, but that study did not report independent external validation or dosimetric validation. Other work has added frequency, boundary, energy and texture guidance, as well as three-dimensional and hybrid U-Net designs. The main deployment drawback is iterative sampling, which raises inference time and computing cost, although reported durations are difficult to compare because studies measure different units, hardware, image dimensions and numbers of sampling steps.

Image quality alone cannot establish clinical reliability. Common measures such as mean absolute error, peak signal-to-noise ratio, structural similarity, normalized cross-correlation and spatial nonuniformity summarize agreement with a reference CT, but they can conceal clinically important errors in bone, air cavities, soft tissue, tumors or organs at risk. The review therefore recommends region-specific Hounsfield-unit analysis, boundary metrics such as the Dice coefficient and 95th-percentile Hausdorff distance, expert review and patient-level failure analysis. Reference-image quality is also crucial: planning CT may be temporally separated from treatment CBCT, while same-day repeat CT or quality-assurance CT provides a shorter interval. A deformed planning CT is itself a registration-derived reference. These standards are not interchangeable, and a difference between synthetic and reference images may reflect model error, genuine anatomical change, registration failure or a mismatch in acquisition and physiology.

Dosimetric testing asks a different and more demanding question: can the generated image support the treatment task for which it is intended? Photon studies commonly compare dose distributions with the gamma index and dose–volume histograms, along with target and organ-at-risk endpoints. Gamma criteria such as 3%/3 mm, 2%/2 mm and 3%/2 mm are study-specific comparison settings, not universal clinical acceptance thresholds for synthetic CT. A cohort-average gamma pass rate can also conceal an individual patient with unacceptable disagreement. Proton therapy requires additional measures because range is sensitive to tissue composition, air cavities and beam-path composition. Relevant endpoints include relative stopping power, water-equivalent thickness and range error, interpreted alongside plan-dose outcomes. Evidence from photon and proton treatments should therefore remain separate rather than being transferred on the assumption that one modality represents the other.

Translation to adaptive radiotherapy is constrained by more than model inference time. A complete workflow includes data transfer, preprocessing, registration, image generation, contour handling, dose calculation, plan evaluation, quality assurance and recovery when an abnormal result occurs. Selected studies have tested dynamic phantoms or complete retrospective workflows, and one lung workflow reported approximately 11 minutes per treatment fraction on two graphics processors, while another dynamic-thoracic-phantom study reported about 15 seconds for sCT generation but six to eight minutes end to end. Such figures are not directly comparable and remain laboratory or preclinical evidence. The review found little prospective documentation of failures, retries, human overrides, fallback procedures or post-deployment monitoring. It recommends shadow-mode testing, in which outputs are recorded and reviewed without influencing treatment decisions, to determine whether a locked workflow can be monitored and safely controlled.

The review used a structured PubMed search last updated on 30 July 2026, supplemented by reference-list screening and targeted searches, but it was a narrative synthesis rather than a protocol-based systematic review. It did not perform formal risk-of-bias assessment or quantitative meta-analysis, and the included studies varied widely in datasets, scanners, acquisition protocols, reference images, registration methods and evaluation criteria. That heterogeneity makes broad cross-study comparisons uncertain. The authors say the next generation of research should use locked models in independent centers, devices and acquisition protocols; report patient-level structural and Hounsfield-unit errors; preserve modality-specific dosimetric endpoints; and link uncertainty or anomaly detection to predefined quality-assurance triggers, human review and recovery pathways. Until those elements are demonstrated, technically impressive synthetic images should not be confused with evidence that adaptive treatment decisions are safe and reproducible in routine care.

Subject of Research: Clinical validation of deep-learning synthetic CT generated from cone-beam CT for adaptive radiotherapy

Article Title: Cone-beam CT-based synthetic CT generation for adaptive radiotherapy: methodological paradigms, clinical reliability assessment, and translation

Article References: Zhang, Y., Zhang, W., Huang, J., Zhao, J., Luo, H., He, D., & Yang, Z. (2026). Cone-beam CT-based synthetic CT generation for adaptive radiotherapy: methodological paradigms, clinical reliability assessment, and translation. Frontiers in Oncology, 16, Article 1981244. https://doi.org/10.3389/fonc.2026.1981244

Image Credits: AI Generated

DOI: 10.3389/fonc.2026.1981244

Keywords: synthetic CT, cone-beam CT, adaptive radiotherapy, deep learning, medical image synthesis, dosimetry, clinical validation, proton therapy, radiotherapy, uncertainty estimation, Cone-beam, CT-based