Image-guided Radiotherapy Research Articles

Feasibility study of structural similarity index for patient-specific quality assurance.

The traditional gamma evaluation method combines dose difference (DD) and distance-to-agreement (DTA) to assess the agreement between two dose distributions. However, while gamma evaluation can identify the location of errors, it does not provide information about the type of errors. The purpose of this study is to optimize and apply the structural similarity (SSIM) index algorithm as a supplementary metric for the quality evaluation of radiation therapy plans alongside gamma evaluation. By addressing the limitations of gamma evaluation, this study aims to establish clinically meaningful SSIM criteria to enhance the accuracy of patient-specific quality assurance (PSQA). We analyzed the relationship between the gamma passing rate (GPR) and the SSIM index with respect to distance and dose errors. For SSIM analysis corresponding to gamma evaluation criteria of 3%/2mm, we introduce the concept of SSIM passing rate (SPR). We determined a valid SSIM index that met the gamma evaluation criteria and applied it. Evaluations performed for 40 fields measured with an electronic portal imaging device (EPID) were analyzed using the GPR and the applied SPR. The study results showed that distance errors significantly affected both the GPR and the SSIM index, whereas dose errors had some influence on the GPR but little impact on the SSIM index. The SPR was 100% for distance error of 2mm but began to decrease for distance errors of 3mm or more. An optimal SSIM index threshold of 0.65 was established, indicating that SPR fell below 100% when distance errors exceeded 2mm. This study demonstrates that the SSIM algorithm can be effectively applied for the quality evaluation of radiation therapy plans. The SPR can serve as a supplementary metric to gamma evaluation, offering a more precise identification of distance errors. Future research should further validate the efficacy of SSIM algorithm across a broader range of clinical cases.

Deep learning based super-resolution for CBCT dose reduction in radiotherapy.

Cone-beam computed tomography (CBCT) is a crucial daily imaging modality in image-guided and adaptive radiotherapy. However, the use of ionizing radiation in CBCT imaging increases the risk of secondary cancers, which is particularly concerning for pediatric patients. Deep learning super-resolution has shown promising results in enhancing the resolution of photographic and medical images but has not yet been explored in the context of CBCT dose reduction. To facilitate CBCT imaging dose reduction, we propose using an enhanced super-resolution generative adversarial network (ESRGAN) in both the projection and image domains to restore the image quality of low-dose CBCT. An extensive projection database, containing 2997 CBCT scans from head and neck cancer patients, was used to train two different ESRGAN models to generate super-resolution CBCTs. One model operated in the projection domain, using pairs of simulated low-resolution (low-dose) and original high-resolution (high-dose) projections and yielded CBCTSRpro. The other model operated in the image domain, using pairs of axial slices from reconstructed low-resolution and high-resolution CBCTs (CBCTLR and CBCTHR) and resulted in CBCTSRimg. Super-resolution CBCTs were evaluated in terms of image similarity (MAE, ME, PSNR, and SSIM), noise characteristics, spatial resolution, and registration accuracy, using the original CBCT as a reference. To test the perceptual difference between the original and super-resolution CBCT, we performed a visual Turing test. Visually, both super-resolution approaches in the projection and image domains improved the image quality of low-dose CBCTs. This was confirmed by the visual Turing test, that showed low accuracy, sensitivity, and specificity, indicating almost no perceptual difference between CBCTHR and the super-resolution CBCTs. CBCTSRimg (accuracy: 0.55, sensitivity: 0.59, specificity: 0.50) performed slightly better than CBCTSRpro (accuracy: 0.59, sensitivity: 0.61, specificity: 0.57). Image similarity metrics were affected by varying noise levels and did not reflect the visual improvements, with MAE/ME/PSNR/SSIM values of 110.4HU/2.9HU/40.4dB/0.82 for CBCTLR, 136.6HU/-0.4HU/38.6dB/0.77 for CBCTSRpro, and 128.2HU/1.9HU/39.0dB/0.80 for CBCTSRimg. In terms of spatial resolution, quantified by calculating 10% levels of the task transfer function, both CBCTSRpro and CBCTSRimg outperformed CBCTLR and nearly matched the reference CBCTHR (CBCTLR: 0.66lp/mm, CBCTSRpro: 0.88lp/mm, CBCTSRimg: 0.95lp/mm, CBCTHR: 1.01lp/mm). Noise characteristics of CBCTSRimg and CBCTSRpro were comparable to the reference CBCTHR. Registration parameters showed negligible differences for all CBCTs (CBCTLR, CBCTSRpro, CBCTSRimg), with average absolute differences in registration parameters being below 0.4° for rotations and below 0.06mm for translations (CBCTHR as reference). This study demonstrates that deep learning can be a valuable tool for CBCT dose reduction in CBCT-guided radiotherapy by acquiring low-dose CBCTs and restoring the image quality using deep learning super-resolution. The results suggest that higher quality images can be generated when super-resolution is performed in the image domain compared to the projection domain.

The Impact of Socioeconomic Status and Comorbidities on Non-Melanoma Skin Cancer Recurrence After Image-Guided Superficial Radiation Therapy

Background: Non-melanoma skin cancers (NMSCs) are the most common cancers in the United States. Image-guided superficial radiation therapy (IGSRT) is an effective treatment for NMSCs. Patient comorbidities and socioeconomic status (SES) are known contributors to health disparities. However, the impact of comorbidities or SES on the outcomes of IGSRT-treated NMSCs has not yet been studied. This study evaluated freedom from recurrence in IGSRT-treated NMSCs stratified by SES and the number of comorbidities. Methods: This large retrospective cohort study evaluated associations between SES (via Area Deprivation Index (ADI)) or comorbidity (via Charlson Comorbidity Index (CCI)) and 2-, 4-, and 6-year year freedom from recurrence in patients with IGSRT-treated NMSC (n = 19,988 lesions). Results: Freedom from recurrence in less (ADI ≤ 50) vs. more (ADI > 50) deprived neighborhoods was 99.47% vs. 99.61% at 6 years, respectively (p = 0.2). Freedom from recurrence in patients with a CCI of 0 (low comorbidity burden) vs. a CCI of ≥7 (high comorbidity burden) was 99.67% vs. 99.27% at 6 years, respectively (p = 0.9). Conclusions: This study demonstrates that there are no significant effects of SES or comorbidity burden on freedom from recurrence in patients with IGSRT-treated NMSC. This supports the expansion of IGSRT in deprived neighborhoods to increase access to care, and IGSRT should be a consideration even in patients with a complex comorbidity status.

First linac-mounted photon counting detector for image guided radiotherapy: Planar image quality characterization.

Image guided radiotherapy (IGRT) with cone-beam computed tomography (CBCT) is limited by the sub-optimal soft-tissue contrast and spatial resolution of energy-integrating flat panel detectors (FPDs) which produce quasi-quantitative CT numbers. Spectral CT with high resolution photon-counting detectors (PCDs) could improve tumor delineation by enhancing the soft-tissue contrast, spatial resolution, dose-efficiency, and CT numberaccuracy. This study presents the first linac-mounted PCD. On the journey to developing spectral cone-beam CT for IGRT, the planar image quality of a linac-mounted PCD is first fundamentally characterized and compared to an FPD in terms of the 2D spatial resolution, noise, andcontrast. A Medipix3RX-based PCD was mounted to the kV FPD of an x-ray volume imaging (XVI) system on an Elekta linac and the PCD acquisition was synchronized with the pulsed kV source. The energy calibration of the Medipix3RX was determined with various radioisotope gamma emissions up to 60 keV. To compare the 2D spatial resolution and noise between the PCD and FPD, the pre-sampling modulation transfer function (MTF) and normalized noise power spectrum (NPS) were measured using an RQA5 spectrum and a fluoroscopy phantom was imaged to determine the limiting resolution of line pairs. Spectral planar images of phantom inserts containing two different concentrations of calcium (60 and 240 mg/cc) and iodine (5 and 15 mg/cc) were optimally energy weighted to maximize the contrast using tube voltages of 60, 80, 100, and 120 kV. To account for drifts in the sensor temperature, the PCD was dynamically translated in and out of the insert shadow during acquisitions to obtain flat field corrections per frame. The raw contrast of the resultant planar images was compared to the energy-integrating FPD. The energy calibration of the Medipix3RX was observed to be linear up to 60 keV. The limiting resolution observed on the fluoroscopy phantom was 2 lp/mm for the FPD and 5 lp/mm for the PCD. The pre-sampling MTF was higher across all frequencies comparing the PCD to the FPD. The normalized NPS of the PCD did not vary with frequency, whereas the spectrum for the FPD decreased monotonically and was lower than the PCD noise power across most of the spatial frequency range studied due to optical light spreading. Optimal energy weights were applied to the dynamically acquired PCD images and the raw contrast of the 60 mg/cc calcium insert increased by factors of and at 60 and 120 kV respectively compared to theFPD. A Medipix3RX-based PCD was successfully integrated with the kilovoltage imaging system on an Elekta linac. The initial planar image quality characterization indicated improvements in the MTF and energy-weighted contrast compared to the FPD. Future work will focus on obtaining linac-mounted spectral CBCT images with a translate-rotate geometry, however this initial study indicates that variations in the PCD sensor response during acquisitions must be addressed to realise the full potential of linac-mounted spectralCBCT.

Impact of belly board immobilization devices and body mass factor on setup displacement using daily cone-beam CT in rectal cancer radiotherapy.

The objective of this study is to evaluate the impact of different belly board and daily changes in patient's body-mass factor (BMF) on setup displacement in radiotherapy for rectal cancer. Twenty-five patients were immobilized using the thermoplastic mask with belly board (TM-BB), and 30 used the vacuum bag cushion with belly board (VBC-BB), performing daily cone-beam computed tomography (CBCT) scans 625 times and 750 times, respectively. Daily pretreatment CBCT scans were registered to the planned CT images for BMF change determination and setup displacement measurement. Independent t-tests compared setup displacement between the two groups in left-right (LR), superior-inferior (SI), and anterior-posterior (AP) directions, as well as the BMF changes. The impact of daily BMF changes on setup displacement was evaluated using multivariate logistic regression and 10-fold cross-validation. The setup displacement for TM-BB in the LR, SI, and AP directions were 0.31±0.25, 0.58±0.40, and 0.19±0.18cm, respectively, while VBC-BB showed 0.19±0.15, 0.26±0.22, and 0.36±0.29cm in the corresponding directions, respectively. Margins of planning target volume (PTV) for TM-BB were 8, 10, and 6mm in LR, SI, and AP directions, while VBC-BB showed margins of 5,7, and 8mm, respectively. The daily BMF changes for both groups were ranked in descending order as follows: sacral rotation angle (RS), hip lateral diameter (HLD), and hip anterior-posterior diameter (HAPD). HAPD was the main factor affecting setup displacement in both the AP and SI directions in TM-BB, while RS was the primary factor for setup displacement in the AP direction in VBC-BB. Compared with TM-BB, VBC-BB had a larger AP displacement but smaller in LR and SI displacement. Daily changes in BMF have distinct effects on setup displacement in different immobilization devices. Image-guided radiation therapy (IGRT) is highly recommended and BMF changes should be given consideration during radiotherapy.

Rapid in vivo EPID image prediction using a combination of analytically calculated attenuation and AI predicted scatter.

The electronic portal imaging device (EPID) can be used in vivo, to detect on-treatment errors by evaluating radiation exiting a patient. To detect deviations from the planning intent, image predictions need to be modeled based on the patient's anatomy and plan information. To date in vivo transit images have been predicted using Monte Carlo (MC) algorithms. A deep learning approach can make predictions faster than MC and only requires patient information for training. To test the feasibility and reliability of creating a deep-learning model with patient data for predicting in vivo EPID images for IMRT treatments. In our approach, the in vivo EPID image was separated into contributions from primary and scattered photons. A primary photon attenuation function was determined by measuring attenuation factors for various thicknesses of solid water. The scatter component ofin vivoEPID images was estimated using a convolutional neural network (CNN). The CNN input was a 3-channel image comprised of the non-transit EPID image and ray tracing projections through a pretreatment CBCT. The predicted scatter component was added to the primary attenuation component to give the full predictedin vivo EPID image. We acquired 193 IMRT fields/images from 93 patients treated on the Varian Halcyon. Model training:validation:test dataset ratios were 133:20:40 images. Additional patient plans were delivered to anthropomorphic phantoms, yielding 75 images for further validation. We assessed model accuracy by comparing model-calculated and measuredin vivoimages with a gamma comparison. Comparing the model-calculated and measured in vivo images gives a mean gamma pass rate for the training:validation:test datasets of 95.4%:94.1%:92.9% for 3%/3 mm and 98.4%:98.4%:96.8% for 5%/3mm. For images delivered to phantom data sets the average gamma pass rate was 96.4% (3%/3 mm criteria). In all data sets, the lower passing rates of some images were due to CBCT artifacts and patient motion that occurred between the time of CBCT and treatment. CONCLUSIONS: The developed deep-learning-based model can generatein vivoEPID images with a mean gamma pass rate greater than 92% (3%/3 mm criteria).This approach provides an alternative to MC prediction algorithms. Image predictions can be made in 30 ms on a standard GPU. In future work, image predictions from this model can be used to detectin vivo treatment errors and on-treatment changes in patient anatomy, providing an additional layer of patient-specific quality assurance.

A novel comprehensive metric balancing imaging dose and setup accuracy in image-guided radiotherapy: concept proposal and clinical validation

PurposeTo propose and validate a comprehensive novel metric balancing the registration accuracy and imaging dose for image-guided-radiotherapy based on real patient data.Materials and methodsWith written informed consent and ethical approval, 56 patients were scanned using 6MV CBCT, 140 kV CBCT, and 100 kV CBCT on Halcyon system for three consecutive treatment fractions. Online registration was performed by various on-duty therapists under routine clinical pressure and time limitation. Offline registration was carried out by an experienced physicist without pressure. The consistency between the online and offline results was used as a surrogate of the missing ground-truth of registration accuracy, which was usually developed by introducing ‘known’ setup errors and rescan the phantoms, yet is ethnically not applicable to real patients. The registration differences (ΔD) between various imaging methods and observers were analyzed. The weighted CT dose index (CTDIw) for kV and MV CBCT was acquired using the PTW CTDI head phantom. The weighted-Dose-Accuracy-Product (DAPw) index was defined as DAPw =ΔD(mm) w1* CTDIw(mGy) w2, where w1 and w2 are the weighting factors of accuracy and dose respectively (w1+w2 = 1).ResultsThe mean and interquartile range (IQR) of ΔD decreased monotonically for MV CBCT, 100 kV CBCT, and 140 kV CBCT, supporting the registration consistency as a surrogate metric of image quality. Significant differences of ΔD were observed between the online and offline registration across three imaging methods (P<0.05). The 140 kV CBCT provides superior positioning accuracy, less dependency on observer subjectivity and time pressure of clinical workflow. Using w1=w2 = 0.5 as an example, the smallest mean, standard deviation, and IQR of DAPw were observed on the 100 kV CBCT, indicating optimal balance between dose and accuracy than the other two methods. Analysis of variance (ANOVA) showed statistically significant differences in DAPw among the different imaging methods (P<0.01, F=50.57).ConclusionUsing registration consistency as a surrogate indicator of image quality, this study proposed and validated a novel “DAPw” parameter based on real patient data, providing a purpose-specific tool for balancing setup accuracy and radiation dose in clinic.

Assessment of Setup-errors in 3Dd-Conformal Radiotherapy for Head and Neck Cancer Patients Using an Electronic Portal Imaging Device

Patient setup is crucial in radiotherapy since treatment is delivered as fractionated treatment over a period of time. Using own institutional margins by considering the setup errors will provide better radiotherapy outcome. Therefore, this study aims to assess set up errors for head and neck cancer patients using an electronic portal imaging device at Apeksha Hospital, Maharagama, Sri Lanka. A total of 101 head and neck cancer patients who were immobilized with thermoplastic mask were selected in this study. Stored data from July 2021 to July 2022 were obtained from ARIA patient management system in the Varian 2300CD Unit at Apeksha Hospital. In order to calculate systematic and random errors, translational errors in all directions were collected utilizing 303 pairs of orthogonal portal images. Moreover, three different algorithms were used to obtain the margin of clinical target volume (CTV) to planning target volume (PTV). The estimated systematic and random errors in the directions of antero-posterior, superior-inferior and medio-lateral are 0.13 cm, 0.10 cm and 0.08 cm, and 0.22 cm, 0.21 cm, and 0.19 cm respectively. Less than 0.5 cm margin were obtained by applying three different algorithms. This study indicates that using a 0.5 cm margin for head and neck cancer patients treating in 2300CD Varian Unit at Apeksha Hospital is safe. Further, this study recommends to developing institutional CTV to PTV margin for all sites of cancer to reduce unnecessary radiation to the surrounding normal healthy tissues.

The TROG 15.01 stereotactic prostate adaptive radiotherapy utilizing kilovoltage intrafraction monitoring (SPARK) clinical trial database.

The US National Institutes of Health state that Sharing of clinical trial data has great potential to accelerate scientific progress and ultimately improve public health by generating better evidence on the safety and effectiveness of therapies for patients (https://www.ncbi.nlm.nih.gov/books/NBK285999/ accessed 2024-01-24.). Aligned with this initiative, the Trial Management Committee of the Trans-Tasman Radiation Oncology Group (TROG) 15.01 Stereotactic Prostate Adaptive Radiotherapy utilizing Kilovoltage intrafraction monitoring (KIM) (SPARK) clinical trial supported the public sharing of the clinical trial data. The data originate from the TROG 15.01 SPARK clinical trial. The SPARK trial was a phase II prospective multi-institutional clinical trial (NCT02397317). The aim of the SPARK clinical trial was to measure the geometric and dosimetric cancer targeting accuracy achieved with a real-time image-guided radiotherapy technology named KIM for 48 prostate cancer patients treated in 5 treatment sessions. During treatment, real-time tumor translational and rotational motion were determined from x-ray images using the KIM technology. A dose reconstruction method was used to evaluate the dose delivered to the target and organs-at-risk. Patient-reported outcomes and toxicity data were monitored up to 2 years after the completion of the treatment. The dataset contains planning CT images, treatment plans, structure sets, planned and motion-included dose-volume histograms, intrafraction kilovoltage, and megavoltage projection images, tumor translational and rotational motion determined by KIM, tumor motion ground truth data, the linear accelerator trajectory traces, and patient treatment outcomes. The dataset is publicly hosted by the University of Sydney eScholarship Repository at https://doi.org/10.25910/qg5d-6058. The 3.6Tb dataset, with approximately 1 million patient images, could be used for a variety of applications, including the development of real-time image-guided methods, adaptation strategies, tumor, and normal tissue control modeling, and prostate-specific antigen kinetics.

Image-Guided Radiation Therapy Is Equally Effective for Basal and Squamous Cell Carcinoma.

Non-melanoma skin cancers (NMSCs), including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), are highly prevalent and a significant cause of morbidity. Image-guided superficial radiation therapy (IGSRT) uses integrated high-resolution dermal ultrasound to improve lesion visualization, but it is unknown whether efficacy varies by histology. This large retrospective cohort study was conducted to determine the effect of tumor histology on freedom from recurrence in 20,069 biopsy-proven NMSC lesions treated with IGSRT, including 9928 BCCs (49.5%), 5294 SCCs (26.4%), 4648 SCCIS cases (23.2%), and 199 lesions with ≥2 NMSCs (1.0%). Freedom from recurrence at 2, 4, and 6 years was 99.60%, 99.45%, and 99.45% in BCC; 99.58%, 99.49%, and 99.49% in SCC; and 99.96%, 99.80%, and 99.80% in SCCIS. Freedom from recurrence at 2, 4, and 6 years following IGSRT did not differ significantly comparing BCC vs. non-BCC or SCC vs. non-SCC but were slightly lower among SCCIS vs. non-SCCIS (p = 0.002). There were no significant differences in freedom from recurrence when stratifying lesions by histologic subtype. This study demonstrates that there is no significant effect of histology on freedom from recurrence in IGSRT-treated NMSC except in SCCIS. These findings support IGSRT as a first-line therapeutic option for NMSC regardless of histology.

A radiation therapy platform to enable upright cone beam computed tomography and future upright treatment on existing photon therapy machines

AbstractBackgroundThe conventional lying down position for radiation therapy can be challenging for patients due to pain, swallowing or breathing issues. To provide an alternative upright treatment position for these patients, we have developed a portable rotating radiation therapy platform which integrates with conventional photon treatment machines. The device enables cone‐beam computed tomography (CBCT) imaging of patients in an upright position, and the future delivery of therapeutic radiation.PurposeTo design, manufacture, and test a device for upright radiation therapy. A collaborative partnership between physicists, engineers, radiation therapists, radiation oncologists, implementation researchers and consumers was established, to create a device that meets both the clinical and technical requirements of upright radiation therapy. The device is central to a clinical trial (ACTRN12623000498695) which will evaluate upright image quality in the context of future image guided radiation therapy for patients with lung cancer or head and neck cancer.MethodsThe weight and physical constraints of the device were assessed with respect to the American civilian population. The final design was evaluated with a series of tests to characterize the angular accuracy of the platform rotation and the reproducibility of the platform setup position in a radiation treatment room. To acquire an upright CBCT, the platform movement system was synchronized to the kilo‐voltage fluoroscopic imaging on an existing treatment machine. The accuracy of the synchronization was evaluated by assessing the positional reproducibility of upright CBCT imaging of a chest phantom.ResultsThe platform has a weight limit of up to 125 kg which is suitable for approximately 90% of males and 95% of females. The platform has physical constraints that accommodate approximately 95.6% of males and 99.6% of females: a maximum seated height of 97.5 cm, a maximum hip breadth of 63.0 cm, and maximum elbow to knuckle length of 46.5 cm. The angular accuracy of the motion system is within ±0.15° over a full rotation, which is within the guidelines for machine movement accuracy in radiation therapy (1 mm/1°). The platform is a portable device and can be reproducibly positioned in a radiation therapy treatment room with a translational range within ±0.04 mm and a rotational range within ±0.025°. The CBCT imaging can reproducibly detect the position of a chest phantom with a translational uncertainty of ±0.07 mm and a rotational uncertainly of ±0.22°, when imaging is acquired following a strict procedure.ConclusionThe upright radiation therapy platform is suitable for the evaluation of CBCT imaging in the context of image guided radiation therapy. The platform will allow the investigation of open questions in upright radiation therapy in the areas of patient experience, positional stability, anatomical changes, and treatment delivery. Improvements to the materials in the radiation beam line, synchronization with the existing treatment machine, and increasing the device weight limit are suggested prior to delivery of future upright treatments.

Offline Adaptive Radiotherapy for Large Non-small Cell Lung Cancer With Volumetric Modulated Arc Therapy Replanning After Creation of Dose Distribution Using Cone-Beam Computed Tomographic Images of Image-Guided Radiotherapy

Offline Adaptive Radiotherapy for Large Non-small Cell Lung Cancer With Volumetric Modulated Arc Therapy Replanning After Creation of Dose Distribution Using Cone-Beam Computed Tomographic Images of Image-Guided Radiotherapy

Deep learning based ultra-low dose fan-beam computed tomography image enhancement algorithm: Feasibility study in image quality for radiotherapy.

We investigated the feasibility of deep learning-based ultra-low dose kV-fan-beam computed tomography (kV-FBCT) image enhancement algorithm for clinical application in abdominal and pelvic tumor radiotherapy. A total of 76 patients of abdominal and pelvic tumors were prospectively selected. The Catphan504 was acquired with the same conditions as the standard phantom test set. We used a CycleGAN-based model for image enhancement. Normal dose CT (NDCT), ultra-low dose CT (LDCT) and deep learning enhanced CT (DLR) were evaluated by subjective and objective analyses in terms of imaging quality, HU accuracy, and image signal-to-noise ratio (SNR). The image noise of DLR was significantly reduced, and the contrast-to-noise ratio (CNR) was significantly improved compared to the LDCT. The most significant improvement was the acrylic which represented soft tissue in CNR from 1.89 to 3.37, improving by 76%, nearly approaching the NDCT, and in low-density resolution from 7.64 to 12.6, improving by 64%. The spatial frequencies of MTF10 and MTF50 in DLR were 4.28 and 2.35 cycles/mm in DLR, respectively, which are higher than LDCT 3.87 and 2.12 cycles/mm, and even slightly higher than NDCT 4.15 and 2.28 cycles/mm. The accuracy and stability of HU values of DLR were similar to NDCT. The image quality evaluation of the two doctors agreed well with DLR and NDCT. A two-by-two comparison between groups showed that the differences in image scores of LDCT compared with NDCT and DLR were all statistically significant (p<0.05), and the subjective scores of DLR were close to NDCT. The image quality of DLR was close to NDCT with reduced radiation dose, which can fully meet the needs of conventional image-guided adaptive radiotherapy (ART) and achieve the quality requirements of clinical radiotherapy. The proposed method provided a technical basis for LDCT-guided ART.

Quantification of uncertainties in reference and relative dose measurements, dose calculations, and patient setup in modern external beam radiotherapy.

Uncertainties in the steps of external beam radiotherapy (EBRT) affect patient outcomes. However, few studies have investigated major contributors to these uncertainties. This study investigated factors contributing to reducing uncertainty in delivering a dose to a target volume. The EBRT process was classified into four steps: reference dosimetry, relative dosimetry [percentage depth doses (PDDs) and off-center ratios (OCRs)], dose calculations (PDDs and OCRs in a virtual water phantom), and patient setup using an image-guided radiation therapy system. We evaluated the uncertainties for these steps in conventionally fractionated EBRT for intracranial disease using 4-, 6-, and 10-MV flattened photon beams generated from clinical linear accelerators following the Guide to the Expression of Uncertainty in Measurement and an uncertainty evaluation method with uncorrected deflection. The following were the major contributors to these uncertainties: beam quality conversion factors for reference dosimetry; charge measurements, chamber depth, source-to-surface distance, water evaporation, and field size for relative dosimetry; dose calculation accuracy for the dose calculations; image registration, radiation-imaging isocenter coincidence, variation in radiation isocenter due to gantry and couch rotation, and intrafractional motion for the patient setup. Among the four steps, the relative dosimetry and dose calculation (namely, both penumbral OCRs) steps involved an uncertainty of more than 5% with a coverage factor of 1. In the EBRT process evaluated herein, the uncertainties in the relative dosimetry and dose calculations must be reduced.

OGC SO06 - Novel Non-metallic Fiducial marker for Radiotherapy in Oesophageal Cancer

Abstract Background Early oesophageal tumours are technically difficult to delineate on the planning computed tomography (pCT) for Image Guided Radiotherapy (IGRT). We describe the use of a novel non-metallic fiducial marker for this purpose. Method Non-metallic fiducial marker (NFM) (BioXmark) was used in 17 patients from April 2021 to November 2022 for marking tumour site prior to radical or palliative radiotherapy. NFM was injected in the sub-mucosal plane using fluoroscopy under general anaesthesia, with the patient supine and using regular injection catheter and syringes. After the initial series, further modifications to the technique improved the injection and shortened operative time. These include left lateral positioning, an ultra-fine injection catheters and micro-dosing syringes. Feedback from the treating radiation oncologists was sought for all the patients. Results Injection was successful in all the patients. In the initial series before the modifications to the technique, often the marker was a larger than anticipated due to a combination of poor visibility when the patients were supine and use of larger bore injection catheters. Once the patient position was changed along with the changes in the injection catheters and the syringes, marking was very precise. Conclusion Previously used fiducial markers are gold based. While these were visible on pCTs, they are known to cause distortion along with being expensive and cumbersome to inject. In comparison, NFM is easy to inject without the artefacts seen with metallic markers. We refined the technique further by changing the patient’s position to left lateral which afforded a improved fluoroscopic visibility. Introducing finer injection catheters and micro-dosing syringes further led to usage of lesser volume and precise marking respectively. In summary we present a series of patients whose early oesophageal tumours were marked with a NFM aiding precise delivery of radiotherapy.

Cone-Beam CT to CT Image Translation Using a Transformer-Based Deep Learning Model for Prostate Cancer Adaptive Radiotherapy.

Cone-beam computed tomography (CBCT) is widely utilized in image-guided radiation therapy; however, its image quality is poor compared to planning CT (pCT), thus restricting its utility for adaptive radiotherapy (ART). Our objective was to enhance CBCT image quality utilizing a transformer-based deep learning model, SwinUNETR, which we compared with a conventional convolutional neural network (CNN) model, U-net. This retrospective study involved 260 patients undergoing prostate radiotherapy, with 245 patients used for training and 15 patients reserved as an independent hold-out test dataset. Employing a CycleGAN framework, we generated synthetic CT (sCT) images from CBCT images, employing SwinUNETR and U-net as generators. We evaluated sCT image quality and assessed its dosimetric impact for photon therapy through gamma analysis and dose-volume histogram (DVH) comparisons. The mean absolute error values for the CT numbers, calculated using all voxels within the patient's body contour and taking the pCT images as a reference, were 84.07, 73.49, and 64.69 Hounsfield units for CBCT, U-net, and SwinUNETR images, respectively. Gamma analysis revealed superior agreement between the dose on the pCT images and on the SwinUNETR-based sCT plans compared to those based on U-net. DVH parameters calculated on the SwinUNETR-based sCT deviated by < 1% from those in pCT plans. Our study showed that, compared to the U-net model, SwinUNETR could proficiently generate more precise sCT images from CBCT images, facilitating more accurate dose calculations. This study demonstrates the superiority of transformer-based models over conventional CNN-based approaches for CBCT-to-CT translation, contributing to the advancement of image synthesis techniques in ART.

A Review of Contemporary Image Guidance Techniques in Head and Neck Cancer.

Traditional head and neck cancer treatment involves open surgery, cytotoxic chemotherapy, and conventional radiotherapy planning. Emerging techniques aim to improve precision and reduce associated toxicity and functional impairment in current practice. This review article describes four such adaptations in image guidance, tailored to next generation therapies. This is a review of current literature, including feasibility studies from our cancer center, relating to: saline-aided intra-oral ultrasound-guided retropharyngeal biopsy; intra-oral ultrasound guided trans-oral robotic surgery (TORS); ultrasound-guided injection of "directly injectedtherapies"; and magnetic resonance imaging-guided radiotherapy. Presented within the context of the wider literature, initial local experience and data indicate good technical outcomes and patient tolerance, and low technical complications in all four image guidance techniques. Initial findings suggest a potentially important future role for these four image guidance techniques, on which next generation therapies are reliant. The broader implications on cross-disciplinary collaboration are also explored herein.

CBCT-based online adaptive radiotherapy of the prostate bed: first clinical experience and comparison to nonadaptive conventional IGRT.

Conventional image-guided radiotherapy (IGRT) of the prostate bed is challenged by the varying anatomy due to dynamic changes of surrounding organs such as the bladder and rectum. This leads to changed dose coverage of target and surrounding tissue. The novel online adaptive radiotherapy (oART) aims to improve target coverage as well as reduce dose exposure to surrounding healthy tissues by daily reoptimization of treatment plans. Here we set out to quantify the resulting changes of this adaptation for patients and treatment team. Atotal of 198 fractions of radiotherapy of the prostate bed (6patients) were treated using oART with the Ethos accelerator (Varian Medical Systems, Palo Alto, CA, USA). For each fraction, volumes and several dose-volume parameters of target volumes and organs at risk were recorded for the scheduled plan (initial plan, recalculated based on daily cone beam computed tomography [CBCT]), the adapted plan, and the verification plan, which is the dose distribution of the applied plan recalculated on the closing CBCT after the adaptation process. Clinical acceptability for all plans was determined using given dose-volume parameters of target volumes. Additionally, the time needed for the adaptation process was registered and compared to the time required for the daily treatment of five conventional IGRT patients. Volumes of target and organs at risk (OAR) exhibited broad variation from day to day. The differences in dose coverage D98% of the clinical target volume (CTV) were significant through adaptation (p < 0.0001; median D98% 97.1-98.0%) and further after verification CBCT (p < 0.001; median D98% 98.1%). Similarly, differences in D98% of the planning target volume (PTV) were significant with adaptation (p < 0.0001; median D98% 91.8-96.5%) and after verification CBCT (p < 0.001; median D98% 96.4%) with decreasing interquartile ranges (IQR). Dose to OAR varied extensively and did not show aconsistent benefit from oART but decreased in IQR. Clinical acceptability increased significantly from 19.2% for scheduled plans to 76.8% for adapted plans and decreased to 70.7% for verification plans. The scheduled plan was never chosen for treatment. The median time needed for oART was 25 min compared to 8 min for IGRT. Target dose coverage was significantly improved using oART. IQR decreased for target coverage as well as OAR doses indicating higher repeatability of dose delivery using oART. Differences in doses after verification CBCT for targets as well as OAR were significant compared to adapted plans but did not offset the overall dosimetric gain of oART. The median time required is three times higher for oART compared to IGRT.

Quantifying thermoplastic mask quality for precision radiotherapy in head and neck cancer: A 3D stress test and 6D-axis error analysis.

Patients with head and neck cancers often require radiotherapy, where immobilization devices like thermoplastic masks ensure precise radiation delivery by minimizing movement. However, the quality of these masks lacks standard reference data. This study aimed to establish institutional acceptance criteria for thermoplastic mask quality and quantify their effectiveness using a 3 dimensional stress test and verified the setup errors using daily megavoltage computed tomography (MVCT). Between April and June 2022, 30 patients underwent radiotherapy with thermoplastic masks. Four key facial points (forehead, bilateral cheekbones, and chin) were tested for supporting force. Mean forces ranged from 3.97 N to 8.8 N. MVCT was used to assess 6 dimensional-axis errors, with mean translational errors (x, y, z) of 0.32 mm, -1.09 mm, and 2.24 mm, respectively, and rotational errors (yaw, pitch, roll) of -0.12°, 0.22°, and 0.35°, respectively. The results demonstrated that the thermoplastic masks provided precise immobilization, minimizing setup errors in 6 dimensions. Our findings offer a quantifiable method to ensure high-quality immobilization during radiotherapy for patients with head and neck cancers.

Review of real time 2D dosimetry in external radiotherapy: Advancements and techniques

The objective of this paper is to provide a comprehensive review of the advancements and techniques in real time two-dimensional (2D) dosimetry for external radiation therapy with emphasis in vivo dosimetry and patient specific quality assurance. External radiation therapy plays a crucial role in cancer treatment, delivering high-energy radiation beams to target tumours while minimizing damage to surrounding healthy tissues. Accurate dosimetry, as both the measurement of the dose and its delivered location, is paramount to ensure effective treatment outcomes and minimize potential side effects.The planned content of this paper encompasses a thorough examination of the advancements made in 2D dosimetry techniques, including solid state and electronic systems. The evolution from traditional passive dosimetry to modern real time detectors, such as portal imaging, has revolutionized the field, offering enhanced precision, efficiency, and convenience. This review will discuss the principles, advantages, and limitations of these systems, along with their practical implementation and calibration procedures.Furthermore, the paper will highlight novel technologies, such as luminescence coatings, for quality assurance (QA) and real-time dose verification during treatment. The use of innovative materials and designs in dosemeters, including high spatial resolution detectors and tissue-equivalent phantoms, will also be explored. Additionally, the incorporation of advanced data analysis techniques, such as machine/deep learning algorithms, for dose reconstruction and QA will be addressed.The review will also explore the application of real time 2D dosimetry in modern clinical and pre-clinical modalities, including intensity-modulated radiation therapy and volumetric modulated arc therapy, stereotactic radiosurgery, image-guided radiation therapy, particle therapy, adaptive radiotherapy, electron and proton ultra-high dose rate therapy and very high energy electrons.By providing an up-to-date overview of the state-of-the-art in real time 2D dosimetry in vivo dosimetry and patient specific quality assurance, this paper aims to inform and guide professionals in the field, facilitating the adoption of cutting-edge techniques and improving the accuracy and safety of external radiotherapy treatments.