In FLASH radiotherapy, the dose is delivered using ultra-high dose rates (UHDR), which are approximately 100 times higher than those used for conventional (CONV) treatments. This has shown promise in sparing normal tissue while maintaining tumour control. Proton beams, particularly in the spread-out Bragg peak (SOBP), offer a favourable depth-dose profile for sparing healthy tissue. However, quality assurance for FLASH requires time-resolved dosimetry to capture the temporal structure of pencil beam scanning delivery. Fibre-coupled scintillating detectors have been used both in electron and photon UHDR beams, and have been applied in the proton beam entrance plateau. Extending their use to the SOBP requires careful calibration to address quenching and water-inequivalent response near the Braggpeak. To calibrate and validate a fibre-coupled inorganic scintillator detector system for accurate, time-resolved point dosimetry in the SOBP for UHDR proton beams, which will enable preclinical and in-vivo FLASH studies with robust dosimetric and geometricverification. Experiments were conducted using a clinical proton PBS beam line. A 2D range modulator generated a 5cm SOBP from a mono-energetic beam. Four ZnSe:O scintillator probes coupled to optical fibres were read out by silicon photomultipliers at 50kHz. An ionisation chamber provided reference dosemeasurements. The calibration included determining a signal dependent saturation factor of the silicon photomultiplier , measuring the absolute calibration factor , and characterising the correction for the depth-dependent under-response . A calibration validation was performed in the SOBP across a range of UHDR beam currents, evaluating both dosimetric accuracy and probe positional stability. The calibrated system was then used to characterise SOBP beam spot profiles, in terms of full width at half-maximum and dose rate variation withdepth. A saturation multiplier of up to 55% was observed across all four probes. The depth-dependent under-response reached up to 12% at the distal SOBP edge. Both effects were successfully corrected for through fitting simple functions. The validation in the SOBP demonstrated that the calibration achieved positional stability within 0.1mm and agreement between the measured and absolute doses within 0.5% for all probes. Beam characterisation revealed full-width at half-maximum broadening from 8.3mm at shallow depth to21.5mm near the range end, with spot profiles comprising two Gaussian cores and a Lorentzian tail. The maximum instantaneous dose rate in the UHDR beam fell from 800Gy/s in the entrance plateau to 280Gy/s in theSOBP. The developed calibration method enables accurate, time-resolved dosimetry in UHDR proton SOBP beams, allowing for the separation of saturation and quenching corrections. The fibre-coupled scintillator system demonstrated high precision in both dose and geometry, making it suitable for quality assurance in preclinical FLASH studies. This approach streamlines recalibration, reducing beam time requirements, and supports routine monitoring of PBS-delivered proton FLASH treatments in complex depth-dosescenarios.

Partially as a result of hypoxia-induced radioresistance, rates of treatment failure for head-and-neck cancer patients receiving radiotherapy can be considerable. Clinical trials utilizing positron emission tomography (PET) to image tumor hypoxia and escalate the prescription dose in hypoxic sub-volumes are being pursued in response, with current clinical prescription doses of 70 Gy generally escalated to 77-78 Gy. Instead utilizing magnetic resonance imaging (MRI) for hypoxia-based prescription dose escalation would be associated with a variety of advantages, including not requiring an additional imaging-related radiation dose to be delivered to the patient and allowing for a variety of other functional maps to be extracted from the same patient imaging session, in addition to tumor hypoxia information. The purpose of this study is to investigate the benefits of MRI-informed hypoxia-based radiotherapy dose escalation for head-and-neck cancer patients treated with proton radiotherapy. Ten patients with head-and-neck cancer scheduled to undergo photon therapy underwent a multi-parametric MRI protocol based on which tumor hypoxia maps were computed for every patient using a quantitative blood oxygenation level dependent (BOLD) approach. Four proton therapy treatment plans were then created for each patient, consisting of intensity-modulated proton therapy (IMPT) and proton arc therapy (PAT) treatment planning performed according to current clinical standards (IMPTConv and PATConv) or with a 10% prescription dose escalation to the hypoxic sub-volumes of the low- and high-risk target structures (IMPTEsc and PATEsc). The generated treatment plans were then analyzed with respect to target and organ-at-risk (OAR) doses and normal tissue complication probabilities (NTCPs) as well as tumor control probabilities (TCPs) calculated according to conventional models (TCPConv) or with consideration of hypoxia-induced radioresistance (TCPHyp). Statistical significance (p < 0.05) of different TCP or mean OAR dose distributions was determined using the Wilcoxon signed-rank test. During IMPT, radiotherapy prescription dose escalation increased TCPConv in the nominal scenario by (5.9 ± 6.3) percentage points (pp) in the normoxic (p < 0.001) and (5.2 ± 9.0) pp in the hypoxic target volumes (p = 0.006). In the worst-case scenario, TCPConv was increased by (5.6 ± 4.5) pp (p < 0.001) and (5.3 ± 6.4) pp (p = 0.003). Dose escalation during PAT improved TCPConv by (3.1 ± 3.3) pp (p < 0.001) and (2.1 ± 5.4) pp (p = 0.015) in the nominal scenario and (3.3 ± 3.1) pp (p < 0.001) and (3.3 ± 4.4) pp (p < 0.001) in the worst-case scenario. When hypoxia-induced radioresistance was considered, dose escalation elevated TCPHyp in the nominal scenario by (7.6 ± 4.5) pp (p< 0.001) during IMPT and (6.4 ± 4.1) pp (p < 0.001) during PAT and TCPHyp in the worst-case scenario by (6.3 ± 3.8) pp (p < 0.001) during IMPT and (6.3 ± 3.1) pp (p < 0.001) during PAT. Compared to the patients' clinical photon therapy treatment plans in the nominal scenario, mean OAR doses were reduced by (13.5 ± 9.3)Gy RBE by IMPTConv, (14.3 ± 10.5)Gy RBE by PATConv, (9.8 ± 10.5)Gy RBE by IMPTEsc, and (10.4 ± 12.8)Gy RBE by PATEsc (all p = 0.002). MRI-based hypoxia-informed radiotherapy prescription dose escalation during both IMPT and PAT significantly increased calculated TCPs while significantly reducing doses delivered to nearby healthy organs compared to the patients' clinical photon therapy treatment plans. MRI-based hypoxia-informed prescription dose escalation is therefore considered feasible and may help partially address hypoxia-induced radioresistance.

Some patients receive glass Yttrium-90 microsphere radioembolization for treatment of hepatocellular carcinoma. Traditional dosimetry uses a partition model to calculate doses to relevant structures, but this model has serious limitations; it assumes uniform perfusion and no cross-compartmental dose. This work aims to assess several methods for performing dosimetry for these patients and compare them, with special attention paid to the differences of these methods from the partition model for the tumor, non-tumorous liver, and lungs. By performing this comparison, the partition model can be assessed for its shortcomings and dosimetric precision relative to other models. Ten patients receiving 90Y had their procedures simulated in Monte Carlo and doses tallied using three different source models, with two based on pre- and posttreatment imaging, and one meant to mimic the assumptions of the partition model. When comparing mean dose within the prescribed tumor volume, the partition and pretreatment imaging models agreed to within 9.7%. Several patients showed lung doses above predicted doses from current standard practice, demonstrating the importance of cross-compartmental doses. Additionally, patients who received lobectomy often had high differential uptake of microspheres in the tumor, which were missed in prescription. However, the partition model missed high doses (>20Gy) to the stomach in two patients which were noted in simulation. Overall, the partition model is appropriate for calculation of mean tumor doses if the information used in treatment planning is accurate, but caution should be used when calculating doses outside the liver, as cross-compartmental effects are often observed.

Respiratory-correlated four-dimensional (4D) magnetic resonance imaging (4D-MRI) is useful to estimate breathing induced motion for MRI-guided radiotherapy. Based on 4D-MR image sets, a three-dimensional mid-position (MidP) MRI can be generated using deformable image registration (DIR) for radiotherapy planning. However, the desired spatial resolution and image contrast of the MidP MRI may differ from the original 4D-MRI. This retrospective study validates a high-definition (HD)-MidP MRI approach that combines 4D-MRI motion information with a high-resolution MRI to enhance the spatial resolution of the MidP image. Computed tomography (CT) and MR image sets of 25 lung cancer patients were eligible, of whom 17 were complete and suitable for analysis. Standard-definition (SD)-MidP images were derived by applying DIR to warp the ten respiratory phases of a 4D-CT or 4D-MRI, whereas the HD-MidP MRI was derived by warping a high-resolution respiratory-triggered MRI to the MidP. The MidP image quality was assessed with a 4-point Likert scale on tumor and organ at risk (OAR) distinctiveness by three readers. Additionally, the gross tumor volume (GTV) was delineated by the readers, from which a consensus contour was derived for each MidP image. Reader contours were evaluated using the Dice similarity coefficient (DSC) and mean distance to agreement (DTA). Anatomical accuracy was evaluated by comparing MidP tumor locations to manually determined tumor displacements, while DIR precision was analyzed using the distance to discordance metric (DDM). Moreover, deformation vector fields (DVFs) from the DIR were used to automatically calculate MidP-based treatment margins. Eighteen targets were identified in seventeen patients. All HD-MidP MR image sets were delineated, while 98% (53/54) of the SD-MidP CT and 87% (47/54) of the SD-MidP MR image sets were of adequate quality for delineation. The SD-MidP MRI was positively scored in 13 out of 47 assessments for tumor distinctiveness and in 6 out of 47 assessments for OAR distinctiveness. In contrast, the HD-MidP MRI showed a substantial improvement, with positive scores in 45 out of 54 assessments for tumor distinctiveness and 51 out of 54 assessments for OAR distinctiveness. Contour analyses revealed that the HD-MidP MRI achieved the highest average DSC value (0.83) and, simultaneously, the lowest mean DTA value (0.96 mm). Compared to the manually determined tumor displacements, subvoxel differences in MidP tumor location were observed in 96% (52/54) of the registrations. The distribution of DDM values (median: 1.1 mm) for the HD-MidP MRI was found to be significantly higher than the distributions for the SD-MidP CT (median: 0.2 mm) and SD-MidP MRI (median: 0.7 mm), indicating a lower, but still subvoxel, precision for the HD-MidP MRI approach. The DVF variability was higher for the HD-MidP MRI (median: 2.7 mm) than for the SD-MidP MRI (median: 2.3 mm). However, when used to derive treatment margins, these margins were identical. The presented HD-MidP MRI methodology scored highest on both tumor and OAR distinctiveness, with GTV contours demonstrating the best alignment. Combined with its high anatomical accuracy, these findings support its potential for lung radiotherapyplanning.

Transperineal ultrasound (TPUS) is a valuable imaging tool for evaluating patients with a variety of pelvic floor disorders, including pelvic organ prolapse (POP). Currently, calculating measurements of anatomical structures and relationships as well as extracting the mid-sagittal (MS) plane of 2D and 3D ultrasound images are obtained manually, which is a time-consuming process and requires a reviewer with prior training in pelvic floor US interpretation. The need for manual analysis of ultrasound images has limited the broader adoption of TPUS for evaluating pelvic floor disorders in both research and clinical practice. An automated segmentation and plane extraction method would improve the ability to easily quantify pelvic anatomy relevant to pelvic floor disorders and improve the efficiency and reproducibility of POP diagnosis and treatment. To develop a fast, reproducible, and automated method of acquiring the MS plane, plane of minimal hiatal dimensions (PMHD), and segmentations of the pelvic floor organs from 3D TPUS images. Our method used a nnU-Net segmentation model to segment structures of interest in the 3D TPUS images. The model segmented the pubis symphysis (PS), urethra, bladder, rectum, rectal ampulla, and anorectal angle (ANA). The segmented output was then fed into a heuristics-based method to determine the PS and ANA to extract the MS plane and PMHD automatically. We used a dataset consisting of 161 3D TPUS images from 104 patients. 89 of the volumes were acquired in a resting state and 72 during the Valsalva maneuver. The segmentation and plane extraction algorithms were evaluated by comparing the results with manual segmentations and manual plane extraction methods using the dice similarity coefficients (DSC), mean absolute surface distance (MAD), and absolute angle difference (AAD), respectively. The Wilcoxon-signed rank statistical test was used with Bonferroni-correction to p<0.01. Cohen effect size was used for comparing model results. The nnU-Net segmentation model reported an average DSC(%) of 70.4%, 58.5%, 57.1%, 48.9%, 39.0%, and 19.8% for bladder, rectum, PS, urethra, ANA, and rectal ampulla respectively. The nnU-Net segmentation model achieved significantly higher DSC (p<0.01) for the urethra and rectum than all other tested models. Across all metrics, the nnU-Net segmentation model achieved an average effect size of 0.3, 0.5, 0.7, and 0.8 compared to a 3D ResNet34 + U-Net, 3D U-Net, 2D U-Net, and Attention 3D U-Net model, respectively. The average AADs between the automatically calculated plane slices and manually estimated planes dataset for the MS plane and PMHD were 3.8° and 2.4°, respectively. The PS and ANA segmentation centroids were used to calculate the MS plane and PMHD and they had distance errors of 3.6mm and 4.4mm. We developed an automated 3D segmentation and multiple plane extraction method of female pelvic floor 3D US images. Our method extracts the MS plane and PMHD from 3D US images. The proposed algorithm pipeline can improve the efficiency and reproducibility of TPUS analysis for pelvic floor disorder diagnosis and treatment.

Cone beam computed tomography (CBCT) is widely used in clinical practice and small animal research for image guidance. The reconstruction quality will be compromised by severe truncation-related artifacts when the scanned object is not fully covered by the field of view (FOV). This work aims to develop a Dual-Domain Deep learning-based method for CBCT Reconstruction from Truncated projections (D3CRT) through the guidance of non-truncated prior information. The D3CRT comprised sequential procedures in both projection and image domains. First, in projection domain, a Sinogram Generation Network (SG-Net) based on the denoising diffusion probabilistic model (DDPM) was employed to predict the missing projection data outside the FOV. The SG-Net was fine-tuned via transfer learning using non-truncated prior data to achieve object-specific adaptation. FDK reconstruction was subsequently performed using the predicted projections. Second, in image domain, an Image Enhancement Network (IE-Net) was applied to refine the FDK reconstructed images. Compressed sensing (CS) reconstruction was then carried out to enforce data fidelity by incorporating the original projections, followed by a secondary IE-Net for final image quality enhancement. In-vivo small animal experiments were conducted on a micro-CBCT system to validate the D3CRT method, with non-truncated prior data obtained from large-FOV low-resolution scans. Dice similarity coefficient (DSC), structural similarity index measure (SSIM), root mean square error (RMSE) were used for quantitative evaluation. The proposed D3CRT effectively improves the image reconstruction quality under truncated projection conditions. For whole-body and lung regions, D3CRT achieved DSCs of 97.1% and 96.0%, outperforming the low-resolution prior images (DSCs of 96.8% and 89.9%) when compared with the reference region segmentations. Quantitative evaluations within the FOV yielded an average RMSE of 2.95 and an SSIM of 98.1% for D3CRT, demonstrating better performance than the Low Resolution Image Constrained Reconstruction (LRICR) method which directly takes low-resolution prior images as the initial inputs for CS reconstruction (RMSE 3.83 , SSIM 97.4%). By leveraging non-truncated prior information and projection-domain transfer learning, the proposed D3CRT effectively improved the overall quality of CBCT reconstruction from truncated projections.

Contrast enhancement is the most sensitive indicator for detecting breast malignancies. Computed tomography (CT) has had a limited role for the locoregional staging of breast tumors due to low soft tissue contrast. To evaluate and optimize the performance of clinical photon-counting computed tomography (PCCT) for breast cancer imaging using a contrast-enhanced mammography (CEM) phantom and to compare its imaging performance with dual-source dual-energy CT (DS-DECT). A CEM phantom containing simulated breast lesions was positioned on an anthropomorphic thoracic phantom and scanned using a clinical PCCT system at 120kV in multi-energy mode and a DS-DECT system with two kV pairs of 70/Sn150 kV and 90/Sn150 kV. PCCT scanner variables included scan mode [standard resolution (SR) and ultra-high resolution (UHR)], field of view (FOV) size (large and small), matrix size (512 and 1024), and type of image used for analysis [low-energy threshold images, virtual monoenergetic images (VMIs) at 50, 60, and 70keV, and iodine maps]. Quantitative analysis was performed using circular regions of interest (ROIs) placed on iodine-containing lesions and background within the phantom. For each ROI, mean CT numbers or iodine concentrations and standard deviations were measured across the central five slices and three independent scans. Contrast-to-noise ratio (CNR) and circularity were evaluated across all PCCT configurations and image types and compared with those obtained from DS-DECT. Among all PCCT configurations, the UHR mode with a small FOV and either a 512or 1024matrix at 50keV VMI achieved the highest combined CNR across all iodine concentrations. Additionally, the UHR mode with a 512matrix and either small or large FOV yielded the highest combined circularity values. The optimal PCCT configuration achieved higher CNR, and higher or comparable circularity compared with 50keV VMIs derived from DECT scans. This phantom study demonstrated that optimal spectral performance for potential breast cancer imaging with PCCT is achieved using UHR mode, low-keV VMIs, a regular matrix size, and dedicated reconstruction FOVs, outperforming DECT.

Carbon ion radiotherapy (CIRT) offers superior physical and biological advantages over photon and proton radiotherapy (PRT). However, due to a need for accurate modeling of the relative biological effectiveness (RBE), currently available treatment planning systems (TPS) for CIRT remain limited. This has constrained clinical and research applications by compelling reliance on a narrow set of systems and restricting flexibility for broader application. This study aimed to develop and validate a new efficient and accurate CIRT TPS for pencil-beam scanning (PBS)-based CIRT, tailored for the Heavy-Ion Therapy Center (HITC) at Yonsei Cancer Center (YCC), incorporating modified microdosimetric kinetic model (mMKM)-based RBE-weighted dose calculation and spot weight optimization. The proposed TPS was designed to automate the workflow from CT image import to CIRT plan optimization. Key refinements included 3D Siddon's ray tracing for spot trajectory tracking, air-gap modeling for beam profile adaptation, and 3D stopping power ratio (SPR) calculation. Gaussian beam modeling was performed to calibrate in-air measurements and Monte Carlo (MC)-driven 3D dose kernels in water. These procedures produced dose influence matrices (DIMs) and MC-estimated matrices required for spot weight optimization with mMKM-based RBE-weighted dose calculation, which was further accelerated by Adam optimizer. Validation was performed by generating plans for a water phantom (10Gy-RBE), a lung case (15Gy-RBE), and a prostate case (4.3Gy-RBE), using single-field and multi-field optimizations (MFO). The resulting plans were recalculated in RayStation (v.2025) and TOPAS MC, and compared using dose-volume histogram (DVH) and gamma passing rate (GPR) at 2%/2mm. The proposed TPS successfully automated the CIRT plan optimization within approximately 1 min for ∼1000 spots, which produced uniform RBE-weighted dose coverage in virtual water phantom, the lung patient, and the prostate patient cases. When the spot weights optimized by the proposed framework were recalculated using a commercial TPS and TOPAS MC, the resulting dose distributions closely matched those of the proposed TPS. Quantitatively, GPRs exceeding 98% were achieved for both physical and RBE-weighted dose at the 2%/2mm criterion, except for the prostate single-field case, which yielded a GPR of 97.37% for the physical dose, relative to TOPAS MC, and a GPR of 96.28% for the RBE-weighted dose, relative to RayStation. These findings confirmed strong agreement between the proposed TPS, MC simulations, and a commercial TPS. A new, fully automated in-house CIRT TPS was developed and validated, demonstrating high dosimetric accuracy and computational efficiency, even comparable to a commercial TPS and MC simulations.

The Ki-67 proliferation index is a critical prognostic marker in pancreatic ductal adenocarcinoma (PDAC); however, its assessment relies on invasive tissue sampling. Ki-67 expression reflects active tumor cell proliferation and is associated with aggressive tumor behavior. A preoperative, noninvasive method to predict Ki-67 status would therefore be valuable for clinical decision-making. Dual-energy CT (DECT) can provide quantitative parameters related to tumor vascularity and composition, potentially reflecting proliferative activity. Additionally, clinical biomarkers such as CA125 may offer complementary information regarding tumor biology. Therefore, the development of a reliable noninvasive approach to preoperatively determine Ki-67 status is of considerable clinical importance. To develop and validate a noninvasive approach for predicting Ki-67 expression in pancreatic ductal adenocarcinoma by integrating quantitative dual-energy CT parameters and clinical biomarkers. This retrospective study included 148 PDAC patients randomly divided into training (n=89) and validation (n=59) sets (6:4 ratio). All patients underwent preoperative DECT scans, and quantitative parameters including normalized iodine concentration (NIC), effective atomic number (Zeff), spectral attenuation slope (λ), etc. were obtained from three contrast phases. Serum tumor markers (CA19-9, CA125, CA50, CEA) and clinical features were analyzed. Multivariate logistic regression was used to identify predictors of Ki-67 expression. A nomogram and 3-D probability surface were developed to intuitively demonstrate the model's predictive structure and decision-making process. Model performance was validated using ROC analysis, calibration curves, and decision curve analysis. Innovatively, kernel-density ridgeline plots and prediction-error bar plots were employed to comprehensively evaluate risk distribution and prediction accuracy, demonstrating the model's stability. The joint model demonstrated excellent predictive performance, achieving AUCs of 0.803 in the training set and 0.810 in the validation set, outperforming both the clinical-only model (training AUC=0.682, validation AUC=0.751) and the DECT-only model (training AUC=0.712, validation AUC=0.702). Multivariate analysis identified arterial-phase normalized iodine concentration (A-NIC) (p=0.046) and CA125 (p=0.005) as independent predictors of Ki-67 expression. These two parameters formed the basis of the final predictive model, demonstrating consistent diagnostic value across both cohorts. Integration of DECT parameters and clinical biomarkers allows accurate noninvasive prediction of Ki-67 expression in PDAC, offering a potential tool for preoperative assessment of tumor proliferation.

The presence of scatter in computed tomography degrades image quality, and can be caused by the patient and by other components in the beam path, such as the bowtie filter. While conventional energy-integrating detectors do not provide spectral distinction, photon-counting (PC) detectors are energy-selective and provide spectral information about the incoming X-ray photons. Since each energy threshold is affected differently by scatter, this spectral information implicitly encodes the scatter content of aprojection. The purpose of this work is to investigate how the spectral information can be exploited to improve deep learning (DL)-based scatter correction. Furthermore, the performance of joint and separate patient and bowtie scatter correction will be investigated, addressing that bowtie scatter has not been considered in current DL-basedapproaches. We present a DL-based approach that can estimate bowtie and patient scatter jointly and compare it against a separate correction. We also introduce neural network-based methods that incorporate the spectral information inherent in PCCT for scatter correction. We present networks that estimate scatter for up to four energy thresholds simultaneously. Training and validation was performed with Monte Carlo data as well as with real data measured by a clinical PCCTsystem. When comparing joint and separate patient and bowtie scatter estimation, both methods reduce the mean absolute error (MAE) from 8HU to 1HU. All proposed DSE methods effectively reduce scatter artifacts and perform better than the convolution-based reference approach. Incorporating the spectral information further improves the performance, with the DSE variant with four energy thresholds achieving the best overall results for all thresholds. For all energy thresholds tested, the spectral DSE methods reduced scatter errors originating from the patient and the bowtie in PCCT from up to 8HU to below 1HU. In addition to the global MAE, we report a critical MAE (MAE10) restricted to voxels with uncorrected errors 10HU, as such deviations are visually perceptible in soft tissue and exceed the noise level of modern CT systems. In all test cases, the proposed spectral methods reduced the MAE10 from 23.8HU in the uncorrected images to 1.6HU after spectral correction. The affected voxels comprised on average 25% of the image volume, indicating a significant reduction in artifact intensity in the most affected areas. In virtual monoenergetic images (VMI), the application of spectral neural networks resulted in a significant reduction in MAE from 16HU to 2HU at 45keV, from 8HU to 1HU at 70keV, and from 5HU to under 1HU at100keV. This paper presents a combined method for correcting patient and bowtie scatter that delivers results equivalent to separate corrections and thus eliminates the need for multiple networks. Further, we demonstrate that deep scatter estimation can effectively exploit the spectral information available to improve scatter correction, especially for spectral applications, like VMIs. Spectral DSE networks slightly outperformed non-spectral variants, with multiple energy thresholds lead to more accurate estimations. This enables the use of one network for scatter correction and eliminates the need for multiple ones, thereby saving computational cost andcomplexity.