RoentGen-RFO: Synthetic Chest X-Ray Generator

• RoentGen-RFO is a diffusion-based synthetic chest X-ray generator that creates images with embedded critical retained foreign objects, addressing rare-event detection challenges.
• It uses a zero-shot, prompt-driven DDPM approach adapted from RoentGen, generating whole-image radiographs without explicit object insertion or additional fine-tuning.
• Benchmark evaluations reveal that while RoentGen-RFO produces visually realistic images, its synthetic data augmentation does not consistently improve detector performance compared to physics-based alternatives.

RoentGen-RFO is the diffusion-based synthetic chest X-ray generator introduced for creating radiographs that contain critical retained foreign objects (RFOs), in the setting of rare-event detection on chest X-rays (Wang et al., 9 Jul 2025). It is derived from RoentGen, a chest X-ray text-to-image foundation model built by adapting Stable Diffusion to paired chest radiographs and radiology reports, but the designation “RoentGen-RFO” refers to an RFO-specific application of that generator rather than to a separately named module in the original RoentGen manuscript (Chambon et al., 2022). In this usage, “RFO” denotes retained foreign object; this should not be conflated with unrelated arXiv uses of the same acronym for Reflective Forward Optimization, Reparameterization Flow Policy Optimization, or rational function optimization in GRAPE, none of which involve RoentGen (Shi et al., 2024, Zhong et al., 3 Feb 2026, Goodwin et al., 2015).

1. Terminology, lineage, and scope

A common source of confusion is the relation between RoentGen and RoentGen-RFO. RoentGen itself was introduced as a domain-adapted text-to-image generative model for chest X-rays, built by fine-tuning Stable Diffusion on paired chest radiographs and radiology reports from MIMIC-CXR, with free-form radiology-specific prompting and downstream augmentation experiments (Chambon et al., 2022). The original manuscript does not define, introduce, or use “RFO” as a component or acronym. RoentGen-RFO therefore belongs to the later benchmark literature on retained foreign object detection rather than to the original model definition.

Within that later literature, RoentGen-RFO denotes a diffusion-based synthetic image generator used to create chest radiographs containing critical retained foreign objects for detector training and benchmarking (Wang et al., 9 Jul 2025). It is conceptually adjacent to, but distinct from, RoentMod, which repurposes RoentGen weights inside a Stable Diffusion image-to-image editing pipeline for counterfactual pathology insertion and shortcut analysis rather than RFO synthesis (Cooke et al., 10 Sep 2025).

2. Clinical motivation and benchmark setting

RoentGen-RFO was introduced to address a specific data bottleneck: critical retained foreign objects are clinically consequential but extremely rare, so there are too few real positive chest radiographs to train robust detection systems (Wang et al., 9 Jul 2025). The target objects include clinically dangerous internally retained items rather than the more common non-critical foreign objects that dominate earlier public datasets. The benchmark paper emphasizes that prior datasets mainly contain non-critical foreign objects such as necklaces, zippers, tubes, or external instruments, which limits their value for clinically relevant RFO detection.

The associated benchmark, Hopkins RFOs Bench, contains 144 critical RFO chest X-ray cases from 144 distinct patients, collected over 18 years from the Johns Hopkins Health System, together with 150 No RFOs cases and 150 No-critical RFOs cases (Wang et al., 9 Jul 2025). The split is patient-based at 70% / 10% / 20%, and annotations include image-level labels plus object-level polygonal or bounding-box annotations. The paper also notes that plain radiographs are the gold standard for detection, yet intraoperative radiographs may miss up to one-third of RFOs. RoentGen-RFO is therefore framed not as a generic synthetic imaging experiment, but as a rare-positive data generation strategy for a high-stakes detection problem.

3. Generation mechanism and prompting strategy

RoentGen-RFO is described as a DDPM-based model adapted from RoentGen and used in a zero-shot manner, without additional training or fine-tuning on RFO-specific datasets (Wang et al., 9 Jul 2025). Its core mechanism is prompt-driven whole-image synthesis rather than physics-based insertion or image editing. Unlike DeepDRR-RFO, it does not explicitly model X-ray physics, CT attenuation, segmentation, or 3D geometry; instead it relies on a pretrained generative prior over chest radiographs.

The conditioning scheme is textual and spatially specified through prompt parameters. The appendix gives the prompt template as:

Normal chest X-ray with a retained rfo-type located at coordinates (x: {x, y: {y})} in the quadrant of the image.

The paper explains that rfo-type refers to “wire, suture, needle, sponge, electrode and so on,” and that the coordinate fields refer to a location in the generated image (Wang et al., 9 Jul 2025). Parameters are stored in CSV files, which are used to automate prompt construction. A slightly fuller prompt example in the paper preserves the same intent: a normal chest X-ray is requested, together with a specified retained object type and image coordinates.

Methodologically, the generator synthesizes the entire radiograph conditioned on this prompt. The paper explicitly states that RoentGen-RFO does not use object masks, bounding boxes as conditioning inputs, ControlNet-style external control signals, paired image editing, inpainting from an existing chest radiograph, or unpaired image translation (Wang et al., 9 Jul 2025). The output is therefore a text-conditioned synthetic chest X-ray in which the RFO is generated as part of the whole image, not physically inserted into an existing study.

4. Role in detector benchmarking

RoentGen-RFO is evaluated indirectly through downstream detector training rather than by separate radiologist realism scores or FID-style image-quality metrics (Wang et al., 9 Jul 2025). Four detectors are benchmarked on Hopkins RFOs Bench: Faster R-CNN, FCOS, RetinaNet, and YOLOv5. Images are resized to $600 \times 600$, normalized with ImageNet mean and standard deviation, and trained with SGD using learning rate 0.005, momentum 0.9, weight decay 0.0005, and a step scheduler decaying by 0.1 every 5 epochs. The reported metrics are ACC, FNR, and AUC for image-level classification, and FROC for object localization.

The benchmark first establishes that clinically mismatched public pretraining is insufficient: training only on Object-CXR and testing on Hopkins RFOs Bench yields modest performance, while training directly on Hopkins or pretraining on Object-CXR followed by Hopkins fine-tuning performs better (Wang et al., 9 Jul 2025). Against that baseline, RoentGen-RFO is used to add 1,000, 2,000, or 4,000 DDPM-generated synthetic images to the Hopkins training set.

The central empirical result is negative. The paper states that “DDPM-based synthetic image augmentation did not consistently enhance model performance,” and for most detectors it decreased or only marginally improved results (Wang et al., 9 Jul 2025). For Faster R-CNN, the base Hopkins-trained model reports ACC 74.0, FNR 0.31, AUC 0.62, and FROC 50.5; adding 1,000 DDPM images changes these to 73.5, 0.33, 0.66, and 48.5, while adding 4,000 DDPM images degrades them to 70.0, 0.38, 0.61, and 45.0. Similar patterns are reported for FCOS, RetinaNet, and YOLO. The paper’s interpretation is that visually plausible whole-image synthesis did not translate into reliable positive-signal supervision for critical-RFO detector training.

5. Comparison with physics-based synthesis

The most informative way to understand RoentGen-RFO is in comparison with DeepDRR-RFO, the benchmark’s physics-based alternative (Wang et al., 9 Jul 2025). The benchmark treats the two methods as complementary rather than interchangeable. RoentGen-RFO is more flexible and visually realistic at the whole-image level, whereas DeepDRR-RFO provides more controllable and label-faithful object insertion.

Aspect	RoentGen-RFO	DeepDRR-RFO
Core mechanism	Zero-shot prompt-driven diffusion	Physics-based CT insertion and projection
Main strength	Better visual realism and greater anatomical diversity	Controllability, exact annotation, reliable object presence
Main weakness	Weak control fidelity for rare critical RFOs	Synthetic RFOs can be lower resolution and overly contrasted
Label fidelity	No explicit guarantee from model internals	Labels come from projected 3D placement
Downstream effect	Did not consistently enhance model performance	Improved training across all detectors, generally best at 2,000 synthetic images

The contrast is quantitative as well as conceptual. DeepDRR-RFO improves training across all detectors, with best performance generally at 2,000 synthetic images; for example, Faster R-CNN improves from base 74.0 / 0.31 / 0.62 / 50.5 to 78.5 / 0.22 / 0.75 / 58.7 in ACC / FNR / AUC / FROC (Wang et al., 9 Jul 2025). RoentGen-RFO, by contrast, is said to offer “better visual realism and greater anatomical diversity” but to struggle because “current DDPM methods struggle to generate realistic critical RFOs images due to a lack of sufficient real-world RFOs examples necessary for model fine-tuning.” A plausible implication is that rare-object detection demands not only realistic global anatomy but also highly faithful local rendering of the target object’s shape, opacity, and contextual integration.

The same comparison clarifies RoentGen-RFO’s controllability. It supports prompt-level control over object type and approximate location through text, but the paper does not describe a mechanism guaranteeing that the requested object is realized with exact geometry or exact radiographic appearance (Wang et al., 9 Jul 2025). In that sense, RoentGen-RFO is controllable at the prompt level but not at the object-physics level.

6. Limitations and later RoentGen-based RFO synthesis

The benchmark literature is explicit about RoentGen-RFO’s present limitations. It operates zero-shot, without RFO-specific fine-tuning; it generates rather than inserts the foreign object; it does not expose masks or structured control signals; and its usefulness for training critical-RFO detectors is limited in the reported setting (Wang et al., 9 Jul 2025). The paper’s conclusion is therefore nuanced: RoentGen-RFO is an important proof of concept for prompt-driven rare-object synthesis, but not yet a practical replacement for physics-based augmentation in this task.

Later work moved toward more structured RoentGen-based RFO synthesis. SurgRFO introduces a two-stage framework in which a RoentGen chest X-ray foundation model is fine-tuned on surgical-domain images to generate realistic RFO-free backgrounds, and a lightweight local generator synthesizes RFO patches that are then composed with conditional Poisson fusion (Hu et al., 24 May 2026). This later design explicitly separates global background realism from localized RFO synthesis, and it is evaluated through both clinician review and downstream detection experiments. The architectural shift suggests that zero-shot whole-image prompting was insufficient for critical-RFO fidelity, and that compositional synthesis is a more viable route under extreme positive-data scarcity.

Broader RoentGen research provides parallel context for this interpretation. RoentGen-v2 later expanded controllability to sex, age, and race/ethnicity and showed that synthetic supervised pretraining could improve downstream performance, out-of-distribution robustness, and fairness in chest radiograph classification (Moroianu et al., 22 Aug 2025). At the same time, a separate audit of RoentGen-generated chest X-rays reported subgroup disparities, higher uncertainty in some classes, and approximately 42% COVID-like latent hallucinations under an external classifier probe, underscoring that synthetic chest radiograph realism does not guarantee clinical validity or fairness (Bhardwaj et al., 2023). For RoentGen-RFO, these neighboring results suggest that rare-object synthesis should be assessed not only by visual plausibility, but also by object-level label fidelity, subgroup behavior, and downstream task utility.