Recently, the diffusion model has emerged as a superior generative model that can produce high quality and realistic images. However, for medical image translation, the existing diffusion models are deficient in accurately retaining structural information since the structure details of source domain images are lost during the forward diffusion process and cannot be fully recovered through learned reverse diffusion, while the integrity of anatomical structures is extremely important in medical images. For instance, errors in image translation may distort, shift, or even remove structures and tumors, leading to incorrect diagnosis and inadequate treatments. Training and conditioning diffusion models using paired source and target images with matching anatomy can help. However, such paired data are very difficult and costly to obtain, and may also reduce the robustness of the developed model to out-of-distribution testing data. We propose a frequency-guided diffusion model (FGDM) that employs frequency-domain filters to guide the diffusion model for structure-preserving image translation. Based on its design, FGDM allows zero-shot learning, as it can be trained solely on the data from the target domain, and used directly for source-to-target domain translation without any exposure to the source-domain data during training. We trained FGDM solely on the head-and-neck CT data, and evaluated it on both head-and-neck and lung cone-beam CT (CBCT)-to-CT translation tasks. FGDM outperformed the state-of-the-art methods (GAN-based, VAE-based, and diffusion-based) in metrics of Fréchet Inception Distance (FID), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM), showing its significant advantages in zero-shot medical image translation.

Purpose or Objective: To evaluate the feasibility and clinical utility of integrating sequential PSMA-PET imaging into an offline–online adaptive workflow for response-based dominant intraprostatic lesion (DIL)-boosting high-risk prostate cancer treated with stereotactic ablative radiotherapy (SABR). Materials and Methods: As part of a prospective trial, patients were treated on MR- or CBCT-guided adaptive radiotherapy (ART) systems with prostate/pelvic node 5-fraction SABR (36.25 Gy/25 Gy) with DIL boost (50 Gy). Whereas traditional DIL boost volumes delineate full pre-therapy imaging-defined disease (GTVinitial), this study serially refined DIL boost volumes based on treatment response defined by PSMA-PET scans after neoadjuvant androgen deprivation therapy (nADT, GTVmb1) and fraction 3 SABR (GTVmb2). DIL delineation employed PET-PSMA fusion to CT/MR simulation and was guided by a rule-based %SUVmax threshold approach. Comparisons of GTV volumes and OAR dosimetry were performed between plans using GTVinitial versus GTVmb1/GTVmb2 for DIL boost, for each of the initial cohorts of five patients from the initially treated cohorts. Results: Five patients treated on MR-Linac (n = 3) or CBCT-based ART (n = 2) were analyzed. Three patients exhibited complete imaging response after nADT, omitting GTVmb boosts. Offline GTVmb refinements based on PSMA-PET were seamlessly integrated into ART workflows without introducing additional treatment time. DIL GTV volumes significantly decreased (p = 0.03) from an initial mean of 11.4 cc (GTVinitial) to 4.1 cc (GTVmb1) and 3.0 cc (GTVmb2). Dosimetric analysis showed meaningful reductions in OAR doses: rectal wall D0.035 cc decreased by up to 12 Gy, while bladder wall D0.035 cc and V18.3 Gy reduced from 52.3 Gy and 52.3 cc (Plan_initial) to 42.9 Gy and 24.9 cc (Plan_mb2), respectively. Urethra doses remained stable, with minor reductions. Sigmoid and femoral head doses remained within acceptable limits. Online adaptation efficiently addressed daily anatomical variations, enabling simulation-free plan re-optimization. Conclusion: PSMA-PET-guided adaptive microboosting for HRPCa SABR is feasible and effective. Standard MR-Linac and CBCT systems offer practical alternatives to BgRT platforms, enabling biology-driven dose personalization and potentially reducing toxicity.

Background/Objectives: Patients with early-stage non-small cell lung cancer (NSCLC) or limited lung metastases and compromised lung function, such as those with interstitial lung disease (ILD) or chronic obstructive pulmonary disease (COPD), or other factors rendering them high-risk for surgery or medically inoperable, face increased risks of treatment-related toxicity from stereotactic ablative radiation therapy (SABR). This study evaluated a novel treatment approach to mitigate these risks. Methods: We investigated Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR), delivered as pulsed radiation every three weeks, in patients with <5 cm lung tumors and ILD, COPD, or prior therapy. Treatment occurred between 2022 and 2024. Online adaptive radiotherapy (o-ART) was employed in 20 patients (80%) to modify treatment plans when anatomical changes warranted replanning. Primary outcomes included volumetric tumor response, changes in dose to organs at risk (OARs) and acute events, while secondary outcomes included local and tumor control, and overall survival. Results: Twenty-three patients received PULSAR treatment at doses between 40 Gy and 60 Gy in 5 fractions and one patient received 54 Gy in 3 fractions, with a median follow-up time of 16.2 months. Approximately half of treated patients demonstrated volumetric tumor response, with median residual volume of 70% (range 36–100%) at maximal response. Among the 20 patients (80%) who underwent online adaptive replanning, significant reductions in OAR dosimetry were observed for all organs assessed including the Dmax for heart (p = 0.0053), bronchus (p = 0.0003), esophagus (p = 0.0005), spinal cord (p = 0.025), and the lung V20 Gy and V12.5 Gy (p < 0.0001). Treatment-related toxicity included two grade 1–2 adverse events and six grade 3 events consisting of pneumonitis, dyspnea or lung infection, with no grade 4 or 5 events. Median progression-free survival was 21.1 months, with 1-year overall survival of 74% and 1-year local control of 100%. Conclusions: PULSAR shows promise as a feasible treatment option for high-risk patients with NSCLC or lung metastases, demonstrating no grade 5 events and complete tumor control. Additional research is needed to fully evaluate the safety profile of PULSAR in the high-risk subgroups and whether PULSAR’s treatment intervals and adaptive planning advantages lead to improved long-term outcomes compared to conventional, uninterrupted SABR regimens.

Recently, the diffusion model has emerged as a superior generative model that can produce high quality and realistic images. However, for medical image translation, the existing diffusion models are deficient in accurately retaining structural information since the structure details of source domain images are lost during the forward diffusion process and cannot be fully recovered through learned reverse diffusion, while the integrity of anatomical structures is extremely important in medical images. For instance, errors in image translation may distort, shift, or even remove structures and tumors, leading to incorrect diagnosis and inadequate treatments. Training and conditioning diffusion models using paired source and target images with matching anatomy can help. However, such paired data are very difficult and costly to obtain, and may also reduce the robustness of the developed model to out-of-distribution testing data. We propose a frequency-guided diffusion model (FGDM) that employs frequency-domain filters to guide the diffusion model for structure-preserving image translation. Based on its design, FGDM allows zero-shot learning, as it can be trained solely on the data from the target domain, and used directly for source-to-target domain translation without any exposure to the source-domain data during training. We evaluated it on three cone-beam CT (CBCT)-to-CT translation tasks for different anatomical sites, and a cross-institutional MR imaging translation task. FGDM outperformed the state-of-the-art methods (GAN-based, VAE-based, and diffusion-based) in metrics of Frechet Inception Distance (FID), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM), showing its significant advantages in zero-shot medical image translation.

Purpose: CT‐guided adaptive radiotherapy (CTgART) offers significant clinical benefits, but its utilization is often limited by resource demands and rigid workflows. This study presents a flexible and scalable “Adapt‐on‐Demand” (AOD) framework that integrates the adaptive capabilities of the Varian Ethos platform with the high‐throughput IGRT delivery of the Varian Halcyon, supported by automated scripting. Methods: We developed an end‐to‐end dual‐platform workflow that dynamically transitions between ART and IGRT delivery based on patient needs. Key components include Eclipse Scripting API (ESAPI) automation to streamline adapted plan ingestion from the Ethos Treatment Planning and Management System (TPMS) export directory, compatibility conversion for Halcyon delivery, and ARIA chart setup. The framework was evaluated through time‐efficiency analysis and longitudinal tracking of ART utilization and machine throughput. Multi‐institutional deployment was also conducted to validate generalizability. Results: Automation reduced plan setup time from 45 to 8 min ( p < 0.001, unpaired t ‐test), and direct comparison of MLC leaf positions confirmed exact agreement between Ethos‐native and converted plans across all control points. ART utilization on the Ethos platform increased from 17% to 77% following IGRT offloading to Halcyon. The workflow was successfully implemented at two external sites without code modification, demonstrating portability. Conclusion: This AOD framework enables flexible ART delivery without compromising workflow efficiency. By automating inter‐platform transitions and alleviating adaptive system burden, it improves clinical throughput and broadens patient access to ART. This work also underscores the importance of system interoperability and provides a scalable model adaptable across diverse practice settings.