ST-NeRP: Spatial-temporal neural representation learning with prior embedding for patient-specific imaging study.

The roles of repeat CT imaging and re-planning during the course of IMRT for patients with head and neck cancer

Accelerated Patient-specific Non-Cartesian Magnetic Resonance Imaging Reconstruction Using Implicit Neural Representations.

Adaptive Radiation Therapy Replanning for Head-and-Neck Cancers and the Dosimetric Benefit to the Parotid Glands

Effects of Interfractional Motion and Anatomic Changes on Proton Therapy Dose Distribution in Lung Cancer

SP-0016 CAN DEFORMABLE REGISTRATION APPROACHES BE RELIABLE?

Conventional methods for reconstructing cone-beam computed tomography (CBCT) often suffer from artifacts and blurring in the presence of missing data, which ultimately hamper the quality of the resulting images. To address this challenge, we propose a neural implicit representation method (PE-INeR) based on prior embedding for sparse-view CBCT reconstruction. In our proposed method, we leverage prior information to guide the reconstruction process. By employing a neural implicit representation network, we capture the intricate features of the image in an implicit manner. Our experimental results underscore the superiority of our approach, demonstrating remarkable reductions in artifacts and substantial enhancements in image quality compared to traditional techniques. Moreover, it is important to emphasize that the PE-INeR outperforms alternative methods in effectively capturing nuanced yet critical image variations, which play a pivotal role in precisely evaluating the advancement of tumors.

Serial Magnetic Resonance Imaging (MRI) exams are often performed in clinical practice, offering shared anatomical and motion information across imaging sessions. However, existing reconstruction methods process each session independently without leveraging this valuable longitudinal information. In this work, we propose a novel concept of longitudinal dynamic MRI, which incorporates patient-specific prior images to exploit temporal correlations across sessions. This framework enables progressive acceleration of data acquisition and reduction of scan time as more imaging sessions become available. The concept is demonstrated using the 4D Golden-angle RAdial Sparse Parallel (GRASP) MRI, a state-of-the-art dynamic imaging technique. Longitudinal reconstruction is performed by concatenating multi-session time-resolved 4D GRASP datasets into an extended dynamic series, followed by a low-rank subspace-based reconstruction algorithm. A series of experiments were conducted to evaluate the feasibility and performance of the proposed method. Results show that longitudinal 4D GRASP reconstruction consistently outperforms standard single-session reconstruction in image quality, while preserving inter-session variations. The approach demonstrated robustness to changes in anatomy, imaging intervals, and body contour, highlighting its potential for improving imaging efficiency and consistency in longitudinal MRI applications. More generally, this work suggests a new context-aware imaging paradigm in which the more we see a patient, the faster we can image.

To evaluate treatment deviations and the impact of treatment modality in the presence of breathing motion and anatomical changes during the course of lung cancer radiotherapy. Two non-small cell lung cancer patients were enrolled in a randomized clinical trial to compare IMRT and proton therapy. To rigorously evaluate the impact of motion and anatomical changes, we used a '5D' dose accumulation approach to sum dose distributions from phase-to-phase and week-to-week to the reference (end-expiration) phase of the original planning 4DCT data set. Six to eight weekly 4DCT data sets that consisted of 10 breathing phases were acquired during the treatment course. The original plan was re-calculated for each phase and deformably mapped to the reference phase to compare the 'delivered' dose distribution with the planned dose distribution of both the IMRT plan and the proton plan for each patient. DVHs derived from delivered dose distribution were compared to that from the planned dose distribution. The delivered dose showed 3% and 2% increase in the dose to the CTV for IMRT and proton plan respectively. Target coverage remained acceptable despite tumor shrinkage from 29% to 49%. The doses to normal structures, such as lung and heart, increased more in the proton plan than in the IMRT plan. The V20 of the total lung volume increased by 4% and 6% from the delivered dose compared to the planned dose for IMRT and proton plan, respectively. The results showed sufficient target coverage was maintained for both modalities. Increases in lung dose were observed in both modalities, but more in the proton arm, perhaps due to weight loss and tumor shrinkage. Adaptive proton therapy strategy is recommended to minimize normal tissue doses. Supported in part by NCI P01 CA021239-29A1.

Image registration and fusion algorithms exist in almost every software system that creates or uses images in radiotherapy. Most treatment planning systems support some form of image registration and fusion to allow the use of multimodality and time-series image data and even anatomical atlases to assist in target volume and normal tissue delineation. Treatment delivery systems perform registration and fusion between the planning images and the in-room images acquired during the treatment to assist patient positioning. Advanced applications are beginning to support daily dose assessment and enable adaptive radiotherapy using image registration and fusion to propagate contours and accumulate dose between image data taken over the course of therapy to provide up-to-date estimates of anatomical changes and delivered dose. This information aids in the detection of anatomical and functional changes that might elicit changes in the treatment plan or prescription. As the output of the image registration process is always used as the input of another process for planning or delivery, it is important to understand and communicate the uncertainty associated with the software in general and the result of a specific registration. Unfortunately, there is no standard mathematical formalism to perform this for real-world situations where noise, distortion, and complex anatomical variations can occur. Validation of the software systems performance is also complicated by the lack of documentation available from commercial systems leading to use of these systems in undesirable 'black-box' fashion. In view of this situation and the central role that image registration and fusion play in treatment planning and delivery, the Therapy Physics Committee of the American Association of Physicists in Medicine commissioned Task Group 132 to review current approaches and solutions for image registration (both rigid and deformable) in radiotherapy and to provide recommendations for quality assurance and quality control of these clinical processes.

Anatomical changes occurred during the treatment course of radiation therapy for lung cancer patients may introduce clinically unacceptable dosimetric deviations from the planned dose. Adaptive radiotherapy (ART) can compensate these dosimetric deviations in subsequent treatments via plan adaption. Determining whether and when to trigger plan adaption during the treatment course is essential to the effectiveness and efficiency of ART. In this study, we aimed to develop a prediction model as an auxiliary decision-making tool for lung ART to identify the patients with intrathoracic anatomical changes that would potentially benefit from the plan adaptions during the treatment course. Seventy-one pairs of weekly cone-beam computer tomography (CBCT) and planning CT (pCT) from 17 advanced non-small cell lung cancer patients were enrolled in this study. To assess the dosimetric impacts brought by anatomical changes observed on each CBCT, dose distribution of the original treatment plan on the CBCT anatomy was calculated on a virtual CT generated by deforming the corresponding pCT to the CBCT and compared to that of the original plan. A replan was deemed needed for the CBCT anatomy once the recalculated dose distribution violated our dosimetric-based trigger criteria. A three-dimensional region of significant anatomical changes (region of interest, ROI) between each CBCT and the corresponding pCT was identified, and 16 morphological features of the ROI were extracted. Additionally, eight features from the overlapped volume histograms (OVHs) of patient anatomy were extracted for each patient to characterize the patient-specific anatomy. Based on the 24 extracted features and the evaluated replanning needs of the pCT-CBCT pairs, a nonlinear supporting vector machine was used to build a prediction model to identify the anatomical changes on CBCTs that would trigger plan adaptions. The most relevant features were selected using the sequential backward selection (SBS) algorithm and a shuffling-and-splitting validation scheme was used for model evaluation. Fifty-five CBCT-pCT pairs were identified of having an ROI, among which 21 CBCT anatomies required plan adaptions. For these 21 positive cases, statistically significant improvements in the sparing of lung, esophagus and spinal cord were achieved by plan adaptions. A high model performance of 0.929 AUC (area under curve) and 0.851 accuracy was achieved with six selected features, including five ROI shape features and one OVH feature. Without involving the OVH features in the feature selection process, the mean AUC and accuracy of the model significantly decreased to 0.826 and 0.779, respectively. Further investigation showed that poor prediction performance with AUC of 0.76 was achieved by the univariate model in solving this binary classification task. We built a prediction model based on the features of patient anatomy and the anatomical changes captured by on-treatment CBCT imaging to trigger plan adaption for lung cancer patients. This model effectively associated the anatomical changes with the dosimetric impacts for lung ART. This model can be a promising tool to assist the clinicians in making decisions for plan adaptions during the treatment courses.

Quantification of Volumetric Changes Occurring During Radiation Therapy for Esophageal Cancer

Marker seed migration in prostate localization

Background Re-irradiation (reRT) is a promising technique for patients with localized recurrence in a previously irradiated area but presents major challenges. These include how to deal with anatomical change between two courses of radiotherapy and integration of radiobiology when summating original and re-irradiation doses. The Support Tool for Re-Irradiation Decisions guided by Radiobiology (STRIDeR) project aims to develop a software tool for use in a commercial treatment planning system to facilitate more informed reRT by accounting for anatomical changes and incorporating radiobiology. We evaluated three approaches to dose summation, incorporating anatomical change and radiobiology to differing extents. Methods In a cohort of 21 patients who previously received pelvic re-irradiation the following dose summation strategies were compared: (1) Rigid registration (RIR) and physical dose summation, to reflect the current clinical approach, (2) RIR and radiobiological dose summation in equivalent dose in 2 Gy fractions (EQD2), and (3) Patient-specific deformable image registration (DIR) with EQD2 dose summation. Results RIR and physical dose summation (Strategy 1) resulted in high cumulative organ at risk (OAR) doses being ‘missed’ in 14% of cases, which were highlighted by EQD2 dose summation (Strategy 2). DIR (with EQD2 dose summation; Strategy 3) resulted in improved OAR overlap and distance to agreement metrics compared to RIR (with EQD2 dose summation; Strategy 2) and was consistently preferred in terms of clinical utility. DIR was considered to have a clinically important impact on dose summation in 38% of cases. Conclusion Re-irradiation cases require individualized assessment when considering dose summation with the previous treatment plan. Fractionation correction is necessary to meaningfully assess cumulative doses and reduce the risk of unintentional OAR overdose. DIR can add clinically relevant information in selected cases, especially for significant anatomical change. Robust solutions for cumulative dose assessment offer the potential for future improved understanding of cumulative OAR tolerances.

To describe and characterise changes documented on thoracic and abdominal imaging of dogs with confirmed immune-mediated haemolytic anaemia. Medical records from a referral hospital were searched from 2015 to 2018 for all dogs diagnosed with immune-mediated haemolytic anaemia that underwent thoracic and abdominal imaging by radiography, ultrasound or computed tomography. Fifty dogs were included. Thoracic imaging revealed abnormalities in 10 dogs (20%) of which lymphadenopathy and cardiomegaly were documented in four dogs (8%) each, and pleural effusion and pleural thickening in one dog (2%) each. Abdominal imaging revealed abnormalities in 43 dogs (86%), in which hepatomegaly and peritoneal effusion were documented in 20 (40%) and 19 dogs (38%), gallbladder wall thickening and sludge in 16 (32%) and 14 dogs (28%) and splenic nodules and splenomegaly in 13 (26%) and seven dogs (14%), respectively. Hepatic and splenic abnormalities were further investigated via fine needle aspirates in 18 dogs and revealed extramedullary haematopoiesis in 12 hepatic (66.7%) and 14 splenic (77.8%) fine needle aspirate samples. Cholecystocentesis was performed in nine dogs with gallbladder abnormalities and revealed bactibilia in three samples (33.3%). In this population of dogs with immune-mediated haemolytic anaemia, thoracic imaging abnormalities were uncommon. Hepatomegaly, peritoneal effusion and gallbladder wall thickening were the most common abdominal imaging findings with bactibilia confirmed in one third of collected bile samples. Hepatosplenomegaly and abdominal lymphadenopathy were not associated with neoplasia in any of the dogs included in this study.

ABSTRACTThe aim of this research was to explore men’s experiences of engaging in psychological therapy to develop a better understanding of the barriers and facilitators to engagement, as well as the role of the therapeutic relationship. Nine men residing in medium-secure forensic mental health hospitals were interviewed. Interpretative Phenomenological Analysis (IPA) was used to obtain accounts of the men’s lived experiences to gain an insight into the subjective meanings they attached to their experiences of psychological therapy. An analysis of the transcripts highlighted the men’s journey over the course of therapy, marked by an internal struggle against the external pressures. Three superordinate themes were identified: ‘shifting self’, ‘relationship with other’ and ‘therapeutic journey’. Agradual, non-linear process of change was evident in the men’s narratives, who at the various phases of psychological therapy were faced with the challenge of questioning and redefining their identity. This involved lowering their guard, learning to become comfortable with vulnerability and face their past in the presence of asupportive ‘other’, in order to move towards building anew or better future for themselves.