Image quality evaluation of a new high-performance ring-gantry cone-beam computed tomography imager.

Objective. Newer cone-beam computed tomography (CBCT) imaging systems offer reconstruction algorithms including metal artifact reduction (MAR) and extended field-of-view (eFoV) techniques to improve image quality. In this study a new CBCT imager, the new Varian HyperSight CBCT, is compared to fan-beam CT and two CBCT imagers installed in a ring-gantry and C-arm linear accelerator, respectively.Approach. The image quality was assessed for HyperSight CBCT which uses new hardware, including a large-size flat panel detector, and improved image reconstruction algorithms. The decrease of metal artifacts was quantified (structural similarity index measure (SSIM) and root-mean-squared error (RMSE)) when applying MAR reconstruction and iterative reconstruction for a dental and spine region using a head-and-neck phantom. The geometry and CT number accuracy of the eFoV reconstruction was evaluated outside the standard field-of-view (sFoV) on a large 3D-printed chest phantom. Phantom size dependency of CT numbers was evaluated on three cylindrical phantoms of increasing diameter. Signal-to-noise and contrast-to-noise were quantified on an abdominal phantom.Main results. In phantoms with streak artifacts, MAR showed comparable results for HyperSight CBCT and CT, with MAR increasing the SSIM (0.97-0.99) and decreasing the RMSE (62-55 HU) compared to iterative reconstruction without MAR. In addition, HyperSight CBCT showed better geometrical accuracy in the eFoV than CT (Jaccard Conformity Index increase of 0.02-0.03). However, the CT number accuracy outside the sFoV was lower than for CT. The maximum CT number variation between different phantom sizes was lower for the HyperSight CBCT imager (∼100 HU) compared to the two other CBCT imagers (∼200 HU), but not fully comparable to CT (∼50 HU).Significance. This study demonstrated the imaging performance of the new HyperSight CBCT imager and the potential of applying this CBCT system in more advanced scenarios by comparing the quality against fan-beam CT.

Dosimetric validation of SmART-RAD Monte Carlo modelling for x-ray cabinet radiobiology irradiators.

Objective. Accuracy and reproducibility in the measurement of radiation dose and associated reporting are critically important for the validity of basic and preclinical radiobiological studies performed with kilovolt x-ray radiation cabinets. This is essential to enable results of radiobiological studies to be repeated, as well as enable valid comparisons between laboratories. In addition, the commonly used single point dose value hides the 3D dose heterogeneity across the irradiated sample. This is particularly true for preclinical rodent models, and is generally difficult to measure directly. Radiation transport simulations integrated in an easy to use application could help researchers improve quality of dosimetry and reporting.Approach. This paper describes the use and dosimetric validation of a newly-developed Monte Carlo (MC) tool, SmART-RAD, to simulate the x-ray field in a range of standard commercial x-ray cabinet irradiators used for preclinical irradiations. Comparisons are made between simulated and experimentally determined dose distributions for a range of configurations to assess the potential use of this tool in determining dose distributions through samples, based on more readily available air-kerma calibration point measurements.Main results. Simulations gave very good dosimetric agreement with measured depth dose distributions in phantoms containing both water and bone equivalent materials. Good spatial and dosimetric agreement between simulated and measured dose distributions was obtained when using beam-shaping shielding.Significance. The MC simulations provided by SmART-RAD provide a useful tool to go from a limited number of dosimetry measurements to detailed 3D dose distributions through a non-homogeneous irradiated sample. This is particularly important when trying to determine the dose distribution in more complex geometries. The use of such a tool can improve reproducibility and dosimetry reporting in preclinical radiobiological research.

Evaluation of a cone-beam computed tomography system calibrated for accurate radiotherapy dose calculation.

Background and purpose: Dose calculation on cone-beam computed tomography (CBCT) images has been less accurate than on computed tomography (CT) images due to lower image quality and discrepancies in CT numbers for CBCT. As increasing interest arises in offline and online re-planning, dose calculation accuracy was evaluated for a novel CBCT imager integrated into a ring gantry treatment machine. Materials and methods: The new CBCT system allowed fast image acquisition (5.9 s) by using new hardware, including a large-size flat panel detector, and incorporated image-processing algorithms with iterative reconstruction techniques, leading to accurate CT numbers allowing dose calculation. In this study, CBCT- and CT-based dose calculations were compared based on three anthropomorphic phantoms, after CBCT-to-mass-density calibration was performed. Six plans were created on the CT scans covering various target locations and complexities, followed by CBCT to CT registrations, copying of contours, and re-calculation of the plans on the CBCT scans. Dose-volume histogram metrics for target volumes and organs-at-risk (OARs) were evaluated, and global gamma analyses were performed. Results: Target coverage differences were consistently below 1.2 %, demonstrating the agreement between CT and re-calculated CBCT dose distributions. Differences in Dmean for OARs were below 0.5 Gy for all plans, except for three OARs, which were below 0.8 Gy (<1.1 %). All plans had a 3 %/1mm gamma pass rate > 97 %. Conclusions: This study demonstrated comparable results between dose calculations performed on CBCT and CT acquisitions. The new CBCT system with enhanced image quality and CT number accuracy opens possibilities for off-line and on-line re-planning.

A 3D transfer learning approach for identifying multiple simultaneous errors during radiotherapy.

Objective. Deep learning models, such as convolutional neural networks (CNNs), can take full dose comparison images as input and have shown promising results for error identification during treatment. Clinically, complex scenarios should be considered, with the risk of multiple anatomical and/or mechanical errors occurring simultaneously during treatment. The purpose of this study was to evaluate the capability of CNN-based error identification in this more complex scenario.Approach. For 40 lung cancer patients, clinically realistic ranges of combinations of various treatment errors within treatment plans and/or computed tomography (CT) images were simulated. Modified CT images and treatment plans were used to predict 2580 3D dose distributions, which were compared to dose distributions without errors using various gamma analysis criteria and relative dose difference as dose comparison methods. A 3D CNN capable of multilabel classification was trained to identify treatment errors at two classification levels, using dose comparison volumes as input: Level 1 (main error type, e.g. anatomical change, mechanical error) and Level 2 (error subtype, e.g. tumor regression, patient rotation). For training the CNNs, a transfer learning approach was employed. An ensemble model was also evaluated, which consisted of three separate CNNs each taking a region of interest of the dose comparison volume as input. Model performance was evaluated by calculating sample F1-scores for training and validation sets.Main results. The model had high F1-scores for Level 1 classification, but performance for Level 2 was lower, and overfitting became more apparent. Using relative dose difference instead of gamma volumes as input improved performance for Level 2 classification, whereas using an ensemble model additionally reduced overfitting. The models obtained F1-scores of 0.86 and 0.62 on an independent test set for Level 1 and Level 2, respectively.Significance. This study shows that it is possible to identify multiple errors occurring simultaneously in 3D dose verification data.

Shoot-through proton FLASH irradiation lowers linear energy transfer in organs at risk for neurological tumors and is robust against density variations.

Objective. The goal of the study was to test the hypothesis that shoot-through FLASH proton beams would lead to lower dose-averaged LET (LETD) values in critical organs, while providing at least equal normal tissue sparing as clinical proton therapy plans.Approach. For five neurological tumor patients, pencil beam scanning (PBS) shoot-through plans were made, using the maximum energy of 227 MeV and assuming a hypothetical FLASH protective factor (FPF) of 1.5. The effect of different FPF ranging from 1.2 to 1.8 on the clinical goals were also considered. LETDwas calculated for the clinical plan and the shoot-through plan, applying a 2 Gy total dose threshold (RayStation 8 A/9B and 9A-IonRPG). Robust evaluation was performed considering density uncertainty (±3% throughout entire volume).Main results.Clinical plans showed large LETDvariations compared to shoot-through plans and the maximum LETDin OAR is 1.2-8 times lower for the latter. Although less conformal, shoot-through plans met the same clinical goals as the clinical plans, for FLASH protection factors above 1.4. The FLASH shoot-through plans were more robust to density uncertainties with a maximum OAR D2%increase of 0.6 Gy versus 5.7 Gy in the clinical plans.Significance.Shoot-through proton FLASH beams avoid uncertainties in LETDdistributions and proton range, provide adequate target coverage, meet planning constraints and are robust to density variations.

AAPM WGDCAB Report 372: A joint AAPM, ESTRO, ABG, and ABS report on commissioning of model-based dose calculation algorithms in brachytherapy.

The introduction of model-based dose calculation algorithms (MBDCAs) in brachytherapy provides an opportunity for a more accurate dose calculation and opens the possibility for novel, innovative treatment modalities. The joint AAPM, ESTRO, and ABG Task Group 186 (TG-186) report provided guidance to early adopters. However, the commissioning aspect of these algorithms was described only in general terms with no quantitative goals. This report, from the Working Group on Model-Based Dose Calculation Algorithms in Brachytherapy, introduced a field-tested approach to MBDCA commissioning. It is based on a set of well-characterized test cases for which reference Monte Carlo (MC) and vendor-specific MBDCA dose distributions are available in a Digital Imaging and Communications in Medicine-Radiotherapy (DICOM-RT) format to the clinical users. The key elements of the TG-186 commissioning workflow are now described in detail, and quantitative goals are provided. This approach leverages the well-known Brachytherapy Source Registry jointly managed by the AAPM and the Imaging and Radiation Oncology Core (IROC) Houston Quality Assurance Center (with associated links at ESTRO) to provide open access to test cases as well as step-by-step user guides. While the current report is limited to the two most widely commercially available MBDCAs and only for 192 Ir-based afterloading brachytherapy at this time, this report establishes a general framework that can easily be extended to other brachytherapy MBDCAs and brachytherapy sources. The AAPM, ESTRO, ABG, and ABS recommend that clinical medical physicists implement the workflow presented in this report to validate both the basic and the advanced dose calculation features of their commercial MBDCAs. Recommendations are also given to vendors to integrate advanced analysis tools into their brachytherapy treatment planning system to facilitate extensive dose comparisons. The use of the test cases for research and educational purposes is further encouraged.

A deep-learning assisted bioluminescence tomography method to enable radiation targeting in rat glioblastoma.

Objective. A novel solution is required for accurate 3D bioluminescence tomography (BLT) based glioblastoma (GBM) targeting. The provided solution should be computationally efficient to support real-time treatment planning, thus reducing the x-ray imaging dose imposed by high-resolution micro cone-beam CT.Approach. A novel deep-learning approach is developed to enable BLT-based tumor targeting and treatment planning for orthotopic rat GBM models. The proposed framework is trained and validated on a set of realistic Monte Carlo simulations. Finally, the trained deep learning model is tested on a limited set of BLI measurements of real rat GBM models.Significance. Bioluminescence imaging (BLI) is a 2D non-invasive optical imaging modality geared toward preclinical cancer research. It can be used to monitor tumor growth in small animal tumor models effectively and without radiation burden. However, the current state-of-the-art does not allow accurate radiation treatment planning using BLI, hence limiting BLI's value in preclinical radiobiology research.Results. The proposed solution can achieve sub-millimeter targeting accuracy on the simulated dataset, with a median dice similarity coefficient (DSC) of 61%. The provided BLT-based planning volume achieves a median encapsulation of more than 97% of the tumor while keeping the median geometrical brain coverage below 4.2%. For the real BLI measurements, the proposed solution provided median geometrical tumor coverage of 95% and a median DSC of 42%. Dose planning using a dedicated small animal treatment planning system indicated good BLT-based treatment planning accuracy compared to ground-truth CT-based planning, where dose-volume metrics for the tumor fall within the limit of agreement for more than 95% of cases.Conclusion. The combination of flexibility, accuracy, and speed of the deep learning solutions make them a viable option for the BLT reconstruction problem and can provide BLT-based tumor targeting for the rat GBM models.

A novel orthotopic mouse model replicates human lung cancer cachexia.

INTRODUCTION: Cancer cachexia, highly prevalent in lung cancer, is a debilitating syndrome characterized by involuntary loss of skeletal muscle mass and is associated with poor clinical outcome, decreased survival and negative impact on tumour therapy. Various lung tumour-bearing animal models have been used to explore underlying mechanisms of cancer cachexia. However, these models do not simulate anatomical and immunological features key to lung cancer and associated muscle wasting. Overcoming these shortcomings is essential to translate experimental findings into the clinic. We therefore evaluated whether a syngeneic, orthotopic lung cancer mouse model replicates systemic and muscle-specific alterations associated with human lung cancer cachexia. METHODS: Immune competent, 11 weeks old male 129S2/Sv mice, were randomly allocated to either (1) sham control group or (2) tumour-bearing group. Syngeneic lung epithelium-derived adenocarcinoma cells (K-rasG12D ; p53R172HΔG ) were inoculated intrapulmonary into the left lung lobe of the mice. Body weight and food intake were measured daily. At baseline and weekly after surgery, grip strength was measured and tumour growth and muscle volume were assessed using micro cone beam CT imaging. After reaching predefined surrogate survival endpoint, animals were euthanized, and skeletal muscles of the lower hind limbs were collected for biochemical analysis. RESULTS: Two-third of the tumour-bearing mice developed cachexia based on predefined criteria. Final body weight (-13.7 ± 5.7%; P < 0.01), muscle mass (-13.8 ± 8.1%; P < 0.01) and muscle strength (-25.5 ± 10.5%; P < 0.001) were reduced in cachectic mice compared with sham controls and median survival time post-surgery was 33.5 days until humane endpoint. Markers for proteolysis, both ubiquitin proteasome system (Fbxo32 and Trim63) and autophagy-lysosomal pathway (Gabarapl1 and Bnip3), were significantly upregulated, whereas markers for protein synthesis (relative phosphorylation of Akt, S6 and 4E-BP1) were significantly decreased in the skeletal muscle of cachectic mice compared with control. The cachectic mice exhibited increased pentraxin-2 (P < 0.001) and CXCL1/KC (P < 0.01) expression levels in blood plasma and increased mRNA expression of IκBα (P < 0.05) in skeletal muscle, indicative for the presence of systemic inflammation. Strikingly, RNA sequencing, pathway enrichment and miRNA expression analyses of mouse skeletal muscle strongly mirrored alterations observed in muscle biopsies of patients with lung cancer cachexia. CONCLUSIONS: We developed an orthotopic model of lung cancer cachexia in immune competent mice. Because this model simulates key aspects specific to cachexia in lung cancer patients, it is highly suitable to further investigate the underlying mechanisms of lung cancer cachexia and to test the efficacy of novel intervention strategies.

11ß-HSD1 determines the extent of muscle atrophy in a model of acute exacerbation of COPD.

Muscle atrophy is an extrapulmonary complication of acute exacerbations (AE) in chronic obstructive pulmonary disease (COPD). The endogenous production and therapeutic application of glucocorticoids (GCs) have been implicated as drivers of muscle loss in AE-COPD. The enzyme 11 ß-hydroxysteroid dehydrogenase 1 (11ß-HSD1) activates GCs and contributes toward GC-induced muscle wasting. To explore the potential of 11ßHSD1 inhibition to prevent muscle wasting here, the objective of this study was to ascertain the contribution of endogenous GC activation and amplification by 11ßHSD1 in skeletal muscle wasting during AE-COPD. Emphysema was induced by intratracheal (IT) instillation of elastase to model COPD in WT and 11ßHSD1/KO mice, followed by vehicle or IT-LPS administration to mimic AE. µCT scans were obtained prior and at study endpoint 48 h following IT-LPS, to assess emphysema development and muscle mass changes, respectively. Plasma cytokine and GC profiles were determined by ELISA. In vitro, myonuclear accretion and cellular response to plasma and GCs were determined in C2C12 and human primary myotubes. Muscle wasting was exacerbated in LPS-11ßHSD1/KO animals compared with WT controls. RT-qPCR and western blot analysis showed elevated catabolic and suppressed anabolic pathways in muscle of LPS-11ßHSD1/KO animals relative to WTs. Plasma corticosterone levels were higher in LPS-11ßHSD1/KO animals, whereas C2C12 myotubes treated with LPS-11ßHSD1/KO plasma or exogenous GCs displayed reduced myonuclear accretion relative to WT counterparts. This study reveals that 11ß-HSD1 inhibition aggravates muscle wasting in a model of AE-COPD, suggesting that therapeutic inhibition of 11ß-HSD1 may not be appropriate to prevent muscle wasting in this setting.

Treatment verification in high dose rate brachytherapy using a realistic 3D printed head phantom and an imaging panel.

PURPOSE: Even though High Dose Rate (HDR) brachytherapy has good treatment outcomes in different treatment sites, treatment verification is far from widely implemented because of a lack of easily available solutions. Previously it has been shown that an imaging panel (IP) near the patient can be used to determine treatment parameters such as the dwell time and source positions in a single material pelvic phantom. In this study we will use a heterogeneous head phantom to test this IP approach, and simulate common treatment errors to assess the sensitivity and specificity of the error-detecting capabilities of the IP. METHODS AND MATERIALS: A heterogeneous head-phantom consisting of soft tissue and bone equivalent materials was 3D-printed to simulate a base of tongue treatment. An High Dose Rate treatment plan with 3 different catheters was used to simulate a treatment delivery, using dwell times ranging from 0.3 s to 4 s and inter-dwell distances of 2 mm. The IP was used to measure dwell times, positions and detect simulated errors. Measured dwell times and positions were used to calculate the delivered dose. RESULTS: Dwell times could be determined within 0.1 s. Source positions were measured with submillimeter accuracy in the plane of the IP, and average distance accuracy of 1.7 mm in three dimensions. All simulated treatment errors (catheter swap, catheter shift, afterloader errors) were detected. Dose calculations show slightly different distributions with the measured dwell positions and dwell times (gamma pass rate for 1 mm/1% of 96.5%). CONCLUSIONS: Using an IP, it was possible to verify the treatment in a realistic heterogeneous phantom and detect certain treatment errors.

Dual-energy CT evaluation of 3D printed materials for radiotherapy applications.

Objective. There is a continuous increase in 3D printing applications in several fields including medical imaging and radiotherapy. Although there are numerous advantages of using 3D printing for the development of customized phantoms, bolus, quality assurance devices and other clinical applications, material properties are not well known and printer settings can affect considerably the properties (e.g. density, isotropy and homogeneity) of the printed parts. This study aims to evaluate several materials and printer properties to identify a range of tissue-mimicking materials.Approach. Dual-energy CT was used to obtain the effective atomic number (Zeff) and relative electron density (RED) for thirty-one different materials including different colours of the same filament from the same manufacturer and the same type of filament from different manufacturers. In addition, a custom bone equivalent filament was developed and evaluated since a high-density filament with a composition similar to bone is not commercially available. Printing settings such as infill density, infill pattern, layer height and nozzle size were also evaluated.Main results. Large differences were observed for HU (288), RED (>10%) andZeff(>50%) for different colours of the same filament due to the colour pigment. Results show a wide HU variation (-714 to 1104), RED (0.277 to 1.480) andZeff(5.22 to 12.39) between the printed samples with some materials being comparable to commercial tissue-mimicking materials and good substitutes to a range of materials from lung to bone. Printer settings can result in directional dependency and significantly affect the homogeneity of the samples.Significance. The use of DECT to extract RED, andZeffallows for quantitative imaging and dosimetry using 3D printed materials equivalent to certified tissue-mimicking tissues.

Roadmap for precision preclinical x-ray radiation studies.

This Roadmap paper covers the field of precision preclinical x-ray radiation studies in animal models. It is mostly focused on models for cancer and normal tissue response to radiation, but also discusses other disease models. The recent technological evolution in imaging, irradiation, dosimetry and monitoring that have empowered these kinds of studies is discussed, and many developments in the near future are outlined. Finally, clinical translation and reverse translation are discussed.

BioXmark® liquid fiducials to enable radiotherapy tumor boosting in rectal cancer, a feasibility trial.

Background and purpose: Dose-escalation in rectal cancer (RCa) may result in an increased complete response rate and thereby enable omission of surgery and organ preservation. In order to implement dose-escalation, it is crucial to develop a technique that allows for accurate image-guided radiotherapy. The aim of the current study was to determine the performance of a novel liquid fiducial marker (BioXmark®) in RCa patients during the radiotherapy course by assessing its positional stability on daily cone-beam CT (CBCT), technical feasibility, visibility on different imaging modalities and safety. Materials and methods: Prospective, non-randomized, single-arm feasibility trial with inclusion of twenty patients referred for neoadjuvant chemoradiotherapy for locally advanced RCa. Primary study endpoint was positional stability on CBCT. Furthermore, technical aspects, safety and clinical performance of the marker, such as visibility on different imaging modalities, were evaluated. Results: Seventy-four markers from twenty patients were available for analysis. The marker was stable in 96% of the cases. One marker showed clinically relevant migration, one marker was lost before start of treatment and one marker was lost during treatment. Marker visibility was good on computed tomography (CT) and CBCT, and moderate on electronic portal imaging (EPI). Marker visibility on magnetic resonance imaging (MRI) was poor during response evaluation. Conclusion: The novel liquid fiducial marker demonstrated positional stability. We provide evidence of the feasibility of the novel fiducial marker for image-guided radiotherapy on daily cone beam CT for RCa patients.

Prediction models for brachytherapy-induced rectal toxicity in patients with locally advanced pelvic cancers: a systematic review.

Purpose: Rectal toxicity remains a major threat to quality of life of patients, who receive brachytherapy to the abdominal pelvic area. Estimating the risk of toxicity development is essential to maximize therapeutic benefit without impairing rectal function. This study aimed to abstract and evaluate studies, which have developed prediction models for rectal toxicity after brachytherapy (BT) in patients with pelvic cancers. Material and methods: To identify relevant studies since 1995, MEDLINE database was searched on August 31, 2021, using terms related to "pelvic cancers", "brachytherapy", "prediction models", and "rectal toxicity". Papers were excluded if model specifications were not reported. Risk of bias was assessed using prediction model risk of bias assessment tool. Results: Thirty models (n = 16 cervical cancer, n = 13 prostate cancer, and n = 1 rectal cancer), including 60 distinct predictors were published. Rectal toxicity varied significantly between studies (median, 25.4% for cervix, and median, 8.8% for prostate cancer). High-, low-, and pulsed-dose-rate BT were applied in 15 (50%), 13 (43%), and 1 (3%) studies, respectively. Most common predictors that retained in final models were age (n = 5, 17%), EBRT (n = 5, 17%), V100% rectum (BT) (n = 5, 17%), and dose at rectal point (n = 3, 10%). None of the studies were considered to be at low-risk of bias due to deficiencies in the analysis domain. Conclusions: Existing models have limited clinical application due to poor quality of methodology. The following key issues should be considered in future studies: 1) Measuring patient-reported outcomes to address underestimation of true frequencies of rectal toxicity events; 2) Giving higher priority to reliable dose-volume parameters; 3) Avoiding overfitting by considering an event per candidate predictor rate ≥ 20; 4) Calculating detailed performance measures.

Endorectal contact radiation boosting: Making the case for dose AND volume reporting.

INTRODUCTION: The various rectal endoluminal radiation techniques all have steep, but different, dose gradients. In rectal contact brachytherapy (CXB) doses are typically prescribed and reported to the applicator surface and not to the gross tumor volume (GTV), clinical target volume (CTV) or organs at risk (OAR), which is crucial to understand tumor response and toxicity rates. To quantify the above-described problem, we performed a dose modeling study using a fixed prescription dose at the surface of the applicator and varied tumor response scenarios. METHODS: Endorectal ultrasound-based 3D-volume-models of rectal tumors and the rectal wall were used to simulate the delivered dose to GTV, CTV and the rectal wall layers, assuming treatment with Maastro HDR contact applicator for rectal cancer with a fixed prescription dose to the applicator surface (equivalent to 3 × 30 Gy CXB) and various response scenarios. RESULTS: An identical prescribed dose to the surface of the applicator resulted in a broad range of doses delivered to the GTV, CTV and the uninvolved intestinal wall. For example, the equieffective dose in 2 Gy per fraction (EQD2) D90% of the GTV varied between 63 and 231 Gy, whereas the EQD2 D2cc of the rectal wall varied between 97 and 165 Gy. CONCLUSION: Doses prescribed at the surface are not representative of the dose received by the tumor and the bowel wall. This stresses the relevance of dose reporting and prescription to GTV and CTV volumes and OAR in order to gain insight between delivered dose, local control and toxicity and to optimize treatment protocols.

What is the optimal input information for deep learning-based pre-treatment error identification in radiotherapy?

Background and purpose: Deep learning (DL) provides high sensitivity for detecting and identifying errors in pre-treatment radiotherapy quality assurance (QA). This work's objective was to systematically evaluate the impact of different dose comparison and image preprocessing methods on DL model performance for error identification in pre-treatment QA. Materials and methods: For 53 volumetric modulated arc therapy (VMAT) and 69 stereotactic body radiotherapy (SBRT) treatment plans of lung cancer patients, mechanical errors were simulated (MLC leaf positions, monitor unit scaling, collimator rotation). Two classification levels were assessed: error type (Level 1) and error magnitude (Level 2). Portal dose images with and without errors were compared using standard (gamma analysis), simple (absolute/relative dose difference, ratio) and alternative (distance-to-agreement, structural similarity index, gradient) dose comparison methods. For preprocessing, different normalization methods (min/max and mean/standard deviation) and image resolutions (32 × 32, 64 × 64 and 128 × 128) were evaluated. All possible combinations of classification level, dose comparison, normalization method and image size resulted in 144 input datasets for DL networks for error identification. Results: Average accuracy was highest for simple dose comparison methods (Level 1: 97.7%, Level 2: 78.1%) while alternative methods scored lowest (Level 1: 91.6%, Level 2: 71.2%). Mean/stdev normalization particularly improved Level 2 classification. Higher image resolution improved error identification, although for SBRT lower image resolution was also sufficient. Conclusions: The choice of dose comparison method has the largest impact on error identification for pre-treatment QA using DL, compared to image preprocessing. Model performance can improve by using simple dose comparison methods, mean/stdev normalization and high image resolution.

A novel multichannel deep learning model for fast denoising of Monte Carlo dose calculations: preclinical applications.

Objective.In preclinical radiotherapy with kilovolt (kV) x-ray beams, accurate treatment planning is needed to improve the translation potential to clinical trials. Monte Carlo based radiation transport simulations are the gold standard to calculate the absorbed dose distribution in external beam radiotherapy. However, these simulations are notorious for their long computation time, causing a bottleneck in the workflow. Previous studies have used deep learning models to speed up these simulations for clinical megavolt (MV) beams. For kV beams, dose distributions are more affected by tissue type than for MV beams, leading to steep dose gradients. This study aims to speed up preclinical kV dose simulations by proposing a novel deep learning pipeline.Approach.A deep learning model is proposed that denoises low precision (∼106simulated particles) dose distributions to produce high precision (109simulated particles) dose distributions. To effectively denoise the steep dose gradients in preclinical kV dose distributions, the model uses the novel approach to use the low precision Monte Carlo dose calculation as well as the Monte Carlo uncertainty (MCU) map and the mass density map as additional input channels. The model was trained on a large synthetic dataset and tested on a real dataset with a different data distribution. To keep model inference time to a minimum, a novel method for inference optimization was developed as well.Main results.The proposed model provides dose distributions which achieve a median gamma pass rate (3%/0.3 mm) of 98% with a lower bound of 95% when compared to the high precision Monte Carlo dose distributions from the test set, which represents a different dataset distribution than the training set. Using the proposed model together with the novel inference optimization method, the total computation time was reduced from approximately 45 min to less than six seconds on average.Significance.This study presents the first model that can denoise preclinical kV instead of clinical MV Monte Carlo dose distributions. This was achieved by using the MCU and mass density maps as additional model inputs. Additionally, this study shows that training such a model on a synthetic dataset is not only a viable option, but even increases the generalization of the model compared to training on real data due to the sheer size and variety of the synthetic dataset. The application of this model will enable speeding up treatment plan optimization in the preclinical workflow.

Molecular Detection of Venous Thrombosis in Mouse Models Using SPECT/CT.

The efficacy of thrombolysis is inversely correlated with thrombus age. During early thrombogenesis, activated factor XIII (FXIIIa) cross-links α2-AP to fibrin to protect it from early lysis. This was exploited to develop an α2-AP-based imaging agent to detect early clot formation likely susceptible to thrombolysis treatment. In this study, this imaging probe was improved and validated using 111In SPECT/CT in a mouse thrombosis model. In vitro fluorescent- and 111In-labelled imaging probe-to-fibrin cross-linking assays were performed. Thrombus formation was induced in C57Bl/6 mice by endothelial damage (FeCl3) or by ligation (stenosis) of the infrarenal vena cava (IVC). Two or six hours post-surgery, mice were injected with 111In-DTPA-A16 and ExiTron Nano 12000, and binding of the imaging tracer to thrombi was assessed by SPECT/CT. Subsequently, ex vivo IVCs were subjected to autoradiography and histochemical analysis for platelets and fibrin. Efficient in vitro cross-linking of A16 imaging probe to fibrin was obtained. In vivo IVC thrombosis models yielded stable platelet-rich thrombi with FeCl3 and fibrin and red cell-rich thrombi with stenosis. In the stenosis model, clot formation in the vena cava corresponded with a SPECT hotspot using an A16 imaging probe as a molecular tracer. The fibrin-targeting A16 probe showed specific binding to mouse thrombi in in vitro assays and the in vivo DVT model. The use of specific and covalent fibrin-binding probes might enable the clinical non-invasive imaging of early and active thrombosis.

A deep learning and Monte Carlo based framework for bioluminescence imaging center of mass-guided glioblastoma targeting.

Objective.Bioluminescence imaging (BLI) is a valuable tool for non-invasive monitoring of glioblastoma multiforme (GBM) tumor-bearing small animals without incurring x-ray radiation burden. However, the use of this imaging modality is limited due to photon scattering and lack of spatial information. Attempts at reconstructing bioluminescence tomography (BLT) using mathematical models of light propagation show limited progress.Approach.This paper employed a different approach by using a deep convolutional neural network (CNN) to predict the tumor's center of mass (CoM). Transfer-learning with a sizeable artificial database is employed to facilitate the training process for, the much smaller, target database including Monte Carlo (MC) simulations of real orthotopic glioblastoma models. Predicted CoM was then used to estimate a BLI-based planning target volume (bPTV), by using the CoM as the center of a sphere, encompassing the tumor. The volume of the encompassing target sphere was estimated based on the total number of photons reaching the skin surface.Main results.Results show sub-millimeter accuracy for CoM prediction with a median error of 0.59 mm. The proposed method also provides promising performance for BLI-based tumor targeting with on average 94% of the tumor inside the bPTV while keeping the average healthy tissue coverage below 10%.Significance.This work introduced a framework for developing and using a CNN for targeted radiation studies for GBM based on BLI. The framework will enable biologists to use BLI as their main image-guidance tool to target GBM tumors in rat models, avoiding delivery of high x-ray imaging dose to the animals.

A scoping review of small animal image-guided radiotherapy research: Advances, impact and future opportunities in translational radiobiology.

Background and purpose: To provide a scoping review of published studies using small animal irradiators and highlight the progress in preclinical radiotherapy (RT) studies enabled by these platforms since their development and commercialization in 2007. Materials and methods: PubMed searches and manufacturer records were used to identify 907 studies that were screened with 359 small animal RT studies included in the analyses. These articles were classified as biology or physics contributions and into subgroups based on research aims, experimental models and other parameters to identify trends in the preclinical RT research landscape. Results: From 2007 to 2021, most published articles were biology contributions (62%) whilst physics contributions accounted for 38% of the publications. The main research areas of physics articles were in dosimetry and calibration (24%), treatment planning and simulation (22%), and imaging (22%) and the studies predominantly used phantoms (41%) or in vivo models (34%). The majority of biology contributions were tumor studies (69%) with brain being the most commonly investigated site. The most frequently investigated areas of tumor biology were evaluating radiosensitizers (33%), model development (30%) and imaging (21%) with cell-line derived xenografts the most common model (82%). 31% of studies focused on normal tissue radiobiology and the lung was the most investigated site. Conclusions: This study captures the trends in preclinical RT research using small animal irradiators from 2007 to 2021. Our data show the increased uptake and outputs from preclinical RT studies in important areas of biology and physics research that could inform translation to clinical trials.