The 50 most-cited articles in Medical Physics, 2023

PURPOSE: Computer-aided diagnosis (CAD) systems on breast ultrasound (BUS) aim to increase the efficiency and effectiveness of breast screening, helping specialists to detect and classify breast lesions. CAD system development requires a set of annotated images, including lesion segmentation, biopsy results to specify benign and malignant cases, and BI-RADS categories to indicate the likelihood of malignancy. Besides, standardized partitions of training, validation, and test sets promote reproducibility and fair comparisons between different approaches. Thus, we present a publicly available BUS dataset whose novelty is the substantial increment of cases with the above-mentioned annotations and the inclusion of standardized partitions to objectively assess and compare CAD systems. ACQUISITION AND VALIDATION METHODS: The BUS dataset comprises 1875 anonymized images from 1064 female patients acquired via four ultrasound scanners during systematic studies at the National Institute of Cancer (Rio de Janeiro, Brazil). The dataset includes biopsy-proven tumors divided into 722 benign and…

BACKGROUND AND PURPOSE: Magnetic resonance imaging (MRI)-based synthetic computed tomography (sCT) simplifies radiation therapy treatment planning by eliminating the need for CT simulation and error-prone image registration, ultimately reducing patient radiation dose and setup uncertainty. In this work, we propose a MRI-to-CT transformer-based improved denoising diffusion probabilistic model (MC-IDDPM) to translate MRI into high-quality sCT to facilitate radiation treatment planning. METHODS: MC-IDDPM implements diffusion processes with a shifted-window transformer network to generate sCT from MRI. The proposed model consists of two processes: a forward process, which involves adding Gaussian noise to real CT scans to create noisy images, and a reverse process, in which a shifted-window transformer V-net (Swin-Vnet) denoises the noisy CT scans conditioned on the MRI from the same patient to produce noise-free CT scans. With an optimally trained Swin-Vnet, the reverse diffusion process was used to generate noise-free sCT scans matching MRI anatomy. We evaluated the proposed method by…

BACKGROUND: Daily or weekly cone-beam computed tomography (CBCT) scans are commonly used for accurate patient positioning during the image-guided radiotherapy (IGRT) process, making it an ideal option for adaptive radiotherapy (ART) replanning. However, the presence of severe artifacts and inaccurate Hounsfield unit (HU) values prevent its use for quantitative applications such as organ segmentation and dose calculation. To enable the clinical practice of online ART, it is crucial to obtain CBCT scans with a quality comparable to that of a CT scan. PURPOSE: This work aims to develop a conditional diffusion model to perform image translation from the CBCT to the CT distribution for the image quality improvement of CBCT. METHODS: The proposed method is a conditional denoising diffusion probabilistic model (DDPM) that utilizes a time-embedded U-net architecture with residual and attention blocks to gradually transform the white Gaussian noise sample to the target CT distribution conditioned on the CBCT. The model was trained on deformed planning CT (dpCT) and CBCT image pairs, and its…

PURPOSE: Medical imaging has become increasingly important in diagnosing and treating oncological patients, particularly in radiotherapy. Recent advances in synthetic computed tomography (sCT) generation have increased interest in public challenges to provide data and evaluation metrics for comparing different approaches openly. This paper describes a dataset of brain and pelvis computed tomography (CT) images with rigidly registered cone-beam CT (CBCT) and magnetic resonance imaging (MRI) images to facilitate the development and evaluation of sCT generation for radiotherapy planning. ACQUISITION AND VALIDATION METHODS: The dataset consists of CT, CBCT, and MRI of 540 brains and 540 pelvic radiotherapy patients from three Dutch university medical centers. Subjects' ages ranged from 3 to 93 years, with a mean age of 60. Various scanner models and acquisition settings were used across patients from the three data-providing centers. Details are available in a comma separated value files provided with the datasets. DATA FORMAT AND USAGE NOTES: The data is available on Zenodo…

PURPOSE: Electronic portal imaging devices (EPIDs) have been widely utilized for patient-specific quality assurance (PSQA) and their use for transit dosimetry applications is emerging. Yet there are no specific guidelines on the potential uses, limitations, and correct utilization of EPIDs for these purposes. The American Association of Physicists in Medicine (AAPM) Task Group 307 (TG-307) provides a comprehensive review of the physics, modeling, algorithms and clinical experience with EPID-based pre-treatment and transit dosimetry techniques. This review also includes the limitations and challenges in the clinical implementation of EPIDs, including recommendations for commissioning, calibration and validation, routine QA, tolerance levels for gamma analysis and risk-based analysis. METHODS: Characteristics of the currently available EPID systems and EPID-based PSQA techniques are reviewed. The details of the physics, modeling, and algorithms for both pre-treatment and transit dosimetry methods are discussed, including clinical experience with different EPID dosimetry systems.…

PURPOSE: For the cancer in the head and neck (HaN), radiotherapy (RT) represents an important treatment modality. Segmentation of organs-at-risk (OARs) is the starting point of RT planning, however, existing approaches are focused on either computed tomography (CT) or magnetic resonance (MR) images, while multimodal segmentation has not been thoroughly explored yet. We present a dataset of CT and MR images of the same patients with curated reference HaN OAR segmentations for an objective evaluation of segmentation methods. ACQUISITION AND VALIDATION METHODS: The cohort consists of HaN images of 56 patients that underwent both CT and T1-weighted MR imaging for image-guided RT. For each patient, reference segmentations of up to 30 OARs were obtained by experts performing manual pixel-wise image annotation. By maintaining the distribution of patient age and gender, and annotation type, the patients were randomly split into training Set 1 (42 cases or 75%) and test Set 2 (14 cases or 25%). Baseline auto-segmentation results are also provided by training the publicly available deep…

BACKGROUND: Positron emission tomography (PET) has had a transformative impact on oncological and neurological applications. However, still much of PET's potential remains untapped with limitations primarily driven by low spatial resolution, which severely hampers accurate quantitative PET imaging via the partial volume effect (PVE). PURPOSE: We present experimental results of a practical and cost-effective ultra-high resolution brain-dedicated PET scanner, using our depth-encoding Prism-PET detectors arranged along a compact and conformal gantry, showing substantial reduction in PVE and accurate radiotracer uptake quantification in small regions. METHODS: lutetium yttrium oxyorthosillicate (LYSO) scintillator crystals coupled 4-to-1 to an 8 × 8 array of silicon photomultiplier (SiPM) pixels on one end and to a prismatoid light guide array on the opposite end. The scanner's performance was evaluated by measuring depth-of-interaction (DOI) resolution, energy resolution, timing resolution, spatial resolution, sensitivity, and image quality of ultra-micro Derenzo and three-dimensional…

BACKGROUND: LATTICE radiation therapy delivers 3D heterogenous dose of high peak-to-valley dose ratio (PVDR) to the tumor target, with peak dose at lattice vertices inside the target and valley dose for the rest of the target. Although the lattice vertex positions can impact PVDR inside the target and sparing of organs-at-risk (OAR), they are fixed as constants and not optimized during treatment planning in current clinical practice. PURPOSE: This work proposes a new LATTICE plan optimization method that can optimize lattice vertex positions during LATTICE treatment planning, which is the first lattice position optimization study to the best of our knowledge. METHODS: The new LATTICE treatment planning method optimizes lattice vertex positions as well as other plan variables (e.g., photon fluences or proton spot weights), with optimization objectives for target PVDR and OAR sparing. To satisfy mathematical differentiability, the lattice vertices are approximated in sigmoid functions. For geometric feasibility, proper geometry constraints are enforced onto lattice vertex positions.…

BACKGROUND: Real-time motion monitoring (RTMM) is necessary for accurate motion management of intrafraction motions during radiation therapy (RT). PURPOSE: Building upon a previous study, this work develops and tests an improved RTMM technique based on real-time orthogonal cine magnetic resonance imaging (MRI) acquired during magnetic resonance-guided adaptive RT (MRgART) for abdominal tumors on MR-Linac. METHODS: A motion monitoring research package (MMRP) was developed and tested for RTMM based on template rigid registration between beam-on real-time orthogonal cine MRI and pre-beam daily reference 3D-MRI (baseline). The MRI data acquired under free-breathing during the routine MRgART on a 1.5T MR-Linac for 18 patients with abdominal malignancies of 8 liver, 4 adrenal glands (renal fossa), and 6 pancreas cases were used to evaluate the MMRP package. For each patient, a 3D mid-position image derived from an in-house daily 4D-MRI was used to define a target mask or a surrogate sub-region encompassing the target. Additionally, an exploratory case reviewed for an MRI dataset of a…

BACKGROUND: Quantitative imaging techniques, such as virtual monochromatic imaging (VMI) and iodine quantification (IQ), have proven valuable diagnostic methods in several specific clinical tasks such as tumor and tissue differentiation. Recently, a new generation of computed tomography (CT) scanners equipped with photon-counting detectors (PCD) has reached clinical status. PURPOSE: This work aimed to investigate the performance of a new photon-counting CT (PC-CT) in low-dose quantitative imaging tasks, comparing it to an earlier generation CT scanner with an energy-integrating detector dual-energy CT (DE-CT). The accuracy and precision of the quantification across size, dose, material types (including low and high iodine concentrations), displacement from iso-center, and solvent (tissue background) composition were explored. METHODS: Quantitative analysis was performed on two clinical scanners, Siemens SOMATOM Force and NAEOTOM Alpha using a multi-energy phantom with plastic inserts mimicking different iodine concentrations and tissue types. The tube configurations in the…

BACKGROUND: The use of computed tomography (CT) for attenuation correction (AC) in whole-body PET/CT can result in a significant contribution to radiation exposure. This can become a limiting factor for reducing considerably the overall radiation exposure of the patient when using the new long axial field of view (LAFOV) PET scanners. However, recent CT technology have introduced features such as the tin (Sn) filter, which can substantially reduce the CT radiation dose. PURPOSE: The purpose of this study was to investigate the ultra-low dose CT for attenuation correction using the Sn filter together with other dose reduction options such as tube current (mAs) reduction. We explore the impact of dose reduction in the context of AC-CT and how it affects PET image quality. METHODS: reported by the scanner. Several voxel-based and volumes of interest (VOI)-based comparisons were performed to compare the ultra-low dose CT images, the generated attenuation maps, and corresponding PET images against those images acquired with the standard low dose CT protocol. Finally, two patient datasets…

BACKGROUND: Low-dose computed tomography (LDCT) can reduce the dose of X-ray radiation, making it increasingly significant for routine clinical diagnosis and treatment planning. However, the noise introduced by low-dose X-ray exposure degrades the quality of CT images, affecting the accuracy of clinical diagnosis. Purpose The noises, artifacts, and high-frequency components are similarly distributed in LDCT images. Transformer can capture global context information in an attentional manner to create distant dependencies on targets and extract more powerful features. In this paper, we reduce the impact of image errors on the ability to retain detailed information and improve the noise suppression performance by fully mining the distribution characteristics of image information. METHODS: This paper proposed an LDCT noise and artifact suppressing network based on Swin Transformer. The network includes a noise extraction sub-network and a noise removal sub-network. The noise extraction and removal capability are improved using a coarse extraction network of high-frequency features based…

BACKGROUND: Gafchromic film's unique properties of tissue-equivalence, dose-rate independence, and high spatial resolution make it an attractive choice for many dosimetric applications. However, complicated calibration processes and film handling limits its routine use. PURPOSE: We evaluated the performance of Gafchromic EBT3 film after irradiation under a variety of measurement conditions to identify aspects of film handling and analysis for simplified but robust film dosimetry. METHODS: The short- (from 5 min to 100 h) and long-term (months) film response was evaluated for clinically relevant doses of up to 50 Gy for accuracy in dose determination and relative dose distributions. The dependence of film response on film-read delay, film batch, scanner type, and beam energy was determined. RESULTS: ), independent of when the film was scanned after irradiation or the type of calibration curve used (batch-specific or time-specific calibration curve) if the same default scanner was used. Analysis of films exposed over a 5-year period showed that using the red channel led to the lowest…

PURPOSE: This work proposes a robot-assisted augmented reality (AR) surgical navigation system for mandibular reconstruction. The system accurately superimposes the preoperative osteotomy plan of the mandible and fibula into a real scene. It assists the doctor in osteotomy quickly and safely under the guidance of the robotic arm. METHODS: The proposed system mainly consists of two modules: the AR guidance module of the mandible and fibula and the robot navigation module. In the AR guidance module, we propose an AR calibration method based on the spatial registration of the image tracking marker to superimpose the virtual models of the mandible and fibula into the real scene. In the robot navigation module, the posture of the robotic arm is first calibrated under the tracking of the optical tracking system. The robotic arm can then be positioned at the planned osteotomy after the registration of the computed tomography image and the patient position. The combined guidance of AR and robotic arm can enhance the safety and precision of the surgery. RESULTS: The effectiveness of the…

PURPOSE: Automated treatment planning strategies are being widely implemented in clinical routines to reduce inter-planner variability, speed up the optimization process, and improve plan quality. This study aims to evaluate the feasibility and quality of intensity-modulated proton therapy (IMPT) plans generated with four different knowledge-based planning (KBP) pipelines fully integrated into a commercial treatment planning system (TPS). MATERIALS/METHODS: A data set containing 60 oropharyngeal cancer patients was split into 11 folds, each containing 47 patients for training, five patients for validation, and five patients for testing. A dose prediction model was trained on each of the folds, resulting in a total of 11 models. Three patients were left out in order to assess if the differences introduced between models were significant. From voxel-based dose predictions, we analyze the two steps that follow the dose prediction: post-processing of the predicted dose and dose mimicking (DM). We focused on the effect of post-processing (PP) or no post-processing (NPP) combined with two…

BACKGROUND: Radiotherapy (RT) combined with cetuximab is the standard treatment for patients with inoperable head and neck cancers. Segmentation of head and neck (H&N) tumors is a prerequisite for radiotherapy planning but a time-consuming process. In recent years, deep convolutional neural networks (DCNN) have become the de facto standard for automated image segmentation. However, due to the expensive computational cost associated with enlarging the field of view in DCNNs, their ability to model long-range dependency is still limited, and this can result in sub-optimal segmentation performance for objects with background context spanning over long distances. On the other hand, Transformer models have demonstrated excellent capabilities in capturing such long-range information in several semantic segmentation tasks performed on medical images. PURPOSE: Despite the impressive representation capacity of vision transformer models, current vision transformer-based segmentation models still suffer from inconsistent and incorrect dense predictions when fed with multi-modal input data. We…

BACKGROUND: Accurate and efficient dose calculation is essential for on-line adaptive planning in proton therapy. Deep learning (DL) has shown promising dose prediction results in photon therapy. However, there is a scarcity of DL-based dose prediction methods specifically designed for proton therapy. Successful dose prediction method for proton therapy should account for more challenging dose prediction problems in pencil beam scanning proton therapy (PBSPT) due to its sensitivity to heterogeneities. PURPOSE: To develop a DL-based PBSPT dose prediction workflow with high accuracy and balanced complexity to support on-line adaptive proton therapy clinical decision and subsequent replanning. METHODS: PBSPT plans of 103 prostate cancer patients (93 for training and the other 10 for independent testing) and 83 lung cancer patients (73 for training and the other 10 for independent testing) previously treated at our institution were included in the study, each with computed tomography scans (CTs), structure sets, and plan doses calculated by the in-house developed Monte-Carlo dose engine…

Abstract Background Standardized patient‐specific pretreatment dosimetry planning is mandatory in the modern era of nuclear molecular radiotherapy, which may eventually lead to improvements in the final therapeutic outcome. Only a comprehensive definition of a dosage therapeutic window encompassing the range of absorbed doses, that is, helpful without being detrimental can lead to therapy individualization and improved outcomes. As a result, setting absorbed dose safety limits for organs at risk (OARs) requires knowledge of the absorbed dose–effect relationship. Data sets of consistent and reliable inter‐center dosimetry findings are required to characterize this relationship. Purpose We developed and standardized a new pretreatment planning model consisting of a predictive dosimetry procedure for OARs in patients with neuroendocrine tumors (NETs) treated with 177Lu‐DOTATATE (Lutathera). In the retrospective study described herein, we used machine learning (ML) regression algorithms to predict absorbed doses in OARs by exploiting a combination of radiomic and dosiomic features…

BACKGROUND: FLASH proton therapy has the potential to reduce side effects of conventional proton therapy by delivering a high dose of radiation in a very short period of time. However, significant progress is needed in the development of FLASH proton therapy. Increasing the dose rate while maintaining dose conformality may involve the use of advanced beam-shaping technologies and specialized equipment such as 3D patient-specific range modulators, to take advantage of the higher transmission efficiency at the highest energy available. The dose rate is an important factor in FLASH proton therapy, but its definition can vary because of the uneven distribution of the dose over time in pencil-beam scanning (PBS). PURPOSE: Highlight the distinctions, both in terms of concept and numerical values, of the various definitions that can be established for the dose rate in PBS proton therapy. METHODS: In an in silico study, five definitions of the dose rate, namely the PBS dose rate, the percentile dose rate, the maximum percentile dose rate, the average dose rate, and the dose averaged dose…

Currently, there are multiple breast dosimetry estimation methods for mammography and its variants in use throughout the world. This fact alone introduces uncertainty, since it is often impossible to distinguish which model is internally used by a specific imaging system. In addition, all current models are hampered by various limitations, in terms of overly simplified models of the breast and its composition, as well as simplistic models of the imaging system. Many of these simplifications were necessary, for the most part, due to the need to limit the computational cost of obtaining the required dose conversion coefficients decades ago, when these models were first implemented. With the advancements in computational power, and to address most of the known limitations of previous breast dosimetry methods, a new breast dosimetry method, based on new breast models, has been developed, implemented, and tested. This model, developed jointly by the American Association of Physicists in Medicine and the European Federation for Organizations of Medical Physics, is applicable to standard…

Abstract Purpose Classifying the subtypes of non‐small cell lung cancer (NSCLC) is essential for clinically adopting optimal treatment strategies and improving clinical outcomes, but the histological subtypes are confirmed by invasive biopsy or post‐operative examination at present. Based on multi‐center data, this study aimed to analyze the importance of extracted CT radiomics features and develop the model with good generalization performance for precisely distinguishing major NSCLC subtypes: adenocarcinoma (ADC) and squamous cell carcinoma (SCC). Methods We collected a multi‐center CT dataset with 868 patients from eight international databases on the cancer imaging archive (TCIA). Among them, patients from five databases were mixed and split to training and test sets (560:140). The remaining three databases were used as independent test sets: TCGA set (n = 97) and lung3 set (n = 71). A total of 1409 features containing shape, intensity, and texture information were extracted from tumor volume of interest (VOI), then the ℓ2,1‐norm minimization was used for feature selection and…

Abstract Ion chambers are required for calibration and reference dosimetry applications in radiation therapy (RT). However, exposure of ion chambers in ultra‐high dose rate (UHDR) conditions pertinent to FLASH‐RT leads to severe saturation and ion recombination, which limits their performance and usability. The purpose of this study was to comprehensively evaluate a set of commonly used commercially available ion chambers in RT, all with different design characteristics, and use this information to produce a prototype ion chamber with improved performance in UHDR conditions as a first step toward ion chambers specific for FLASH‐RT. The Advanced Markus and Exradin A10, A26, and A20 ion chambers were evaluated. The chambers were placed in a water tank, at a depth of 2 cm, and exposed to an UHDR electron beam at different pulse repetition frequency (PRF), pulse width (PW), and pulse amplitude settings on an IntraOp Mobetron. Ion chamber responses were investigated for the various beam parameter settings to isolate their dependence on integrated dose, mean dose rate and instantaneous…

BACKGROUND: Invasive coronary angiography (ICA) is a primary imaging modality that visualizes the lumen area of coronary arteries for diagnosis and interventional guidance. In the current practice of quantitative coronary analysis (QCA), semi-automatic segmentation tools require labor-intensive and time-consuming manual correction, limiting their application in the catheterization room. PURPOSE: This study aims to propose rank-based selective ensemble methods that improve the segmentation performance and reduce morphological errors that limit fully automated quantification of coronary artery using deep-learning segmentation of ICA. METHODS: Two selective ensemble methods proposed in this work integrated the weighted ensemble approach with per-image quality estimation. The segmentation outcomes from five base models with different loss functions were ranked either by mask morphology or estimated dice similarity coefficient (DSC). The final output was determined by imposing different weights according to the ranks. The ranking criteria based on mask morphology were formulated from…

PURPOSE: Delineation of the clinical target volume (CTV) and organs-at-risk (OARs) is important in cervical cancer radiotherapy. But it is generally labor-intensive, time-consuming, and subjective. This paper proposes a parallel-path attention fusion network (PPAF-net) to overcome these disadvantages in the delineation task. METHODS: The PPAF-net utilizes both the texture and structure information of CTV and OARs by employing a U-Net network to capture the high-level texture information, and an up-sampling and down-sampling (USDS) network to capture the low-level structure information to accentuate the boundaries of CTV and OARs. Multi-level features extracted from both networks are then fused together through an attention module to generate the delineation result. RESULTS: The dataset contains 276 computed tomography (CT) scans of patients with cervical cancer of staging IB-IIA. The images are provided by the West China Hospital of Sichuan University. Simulation results demonstrate that PPAF-net performs favorably on the delineation of the CTV and OARs (e.g., rectum, bladder and…

BACKGROUND: With the development of low-dose computed tomography (CT), incomplete data reconstruction has been widely concerned. The total variation (TV) minimization algorithm can accurately reconstruct images from sparse or noisy data. PURPOSE: However, the traditional TV algorithm ignores the direction of structures in images, leading to the loss of edge information and block artifacts when the object is not piecewise constant. Since the anisotropic information can facilitate preserving the edge and detail information in images, we aim to improve the TV algorithm in terms of reconstruction accuracy via this approach. METHODS: In this paper, we propose an adaptive-weighted high order total variation (awHOTV) algorithm. We construct the second order TV-norm using the second order gradient, adapt the anisotropic edge property between neighboring image pixels, adjust the local image-intensity gradient to keep edge information, and design the corresponding Chambolle-Pock (CP) solving algorithm. Implementing the proposed algorithm, comprehensive studies are conducted in the ideal…

BACKGROUND: Artificial intelligence-based methods have generated substantial interest in nuclear medicine. An area of significant interest has been the use of deep-learning (DL)-based approaches for denoising images acquired with lower doses, shorter acquisition times, or both. Objective evaluation of these approaches is essential for clinical application. PURPOSE: DL-based approaches for denoising nuclear-medicine images have typically been evaluated using fidelity-based figures of merit (FoMs) such as root mean squared error (RMSE) and structural similarity index measure (SSIM). However, these images are acquired for clinical tasks and thus should be evaluated based on their performance in these tasks. Our objectives were to: (1) investigate whether evaluation with these FoMs is consistent with objective clinical-task-based evaluation; (2) provide a theoretical analysis for determining the impact of denoising on signal-detection tasks; and (3) demonstrate the utility of virtual imaging trials (VITs) to evaluate DL-based methods. METHODS: A VIT to evaluate a DL-based method for…

MR-guided radiotherapy (MRgRT) is one of the most significant advances in radiotherapy in recent years. The hybrid systems were designed to visualize patient anatomical and physiological changes during the course of radiotherapy, enabling more precise treatment. However, before MR-linacs reach their full potential in delivering safe and accurate treatments to patients, the radiotherapy team must understand how a magnetic field alters the dosimetric properties of the radiation beam and its potential impact on treatment quality and clinical outcomes. This review aims to provide an in-depth description of the magnetic field induced dose effects for the two widely available systems, the 0.35 T and the 1.5 T MR-linacs. In MR-linac treatments, the primary photon beam passes through MR components that never exist in conventional linacs, which alter both in-field and out-of-field doses. More importantly, the interplay between the always-on magnetic field and the secondary electrons is not negligible. This interplay affects dose deposition in the patient, resulting in reduced in-field skin…

Abstract Background EBT4 was newly released for radiotherapy quality assurance to improve the signal‐to‐noise ratio in radiochromic film dosimetry. It is important to know its dose‐response characteristics before its use in the clinic. Purpose This study aims to investigate and compare the dose‐response curves of the Gafchromic EBT4 film for megavoltage and kilovoltage x‐ray beams with different dose levels, scanning spatial resolutions, and sizes of region of interest (ROI). Methods EBT4 film (Lot#07052201) calibration strips (3.5 × 20 cm2) were exposed to a 10×10 cm2 open field at doses of 0, 63, 125, 500, 750, 1000 cGy using 6 MV photon beam. EBT4 film strips from the same lot were then exposed to each x‐ray beam (6 MV, 6 MV FFF, 10 MV FFF, 15 MV, and 70 kV) at six dose values (50, 100, 300, 600, 800, 1000 cGy). A full sheet (25 × 20 cm2) of EBT4 film was irradiated at each energy with 300 cGy for profile comparison with the treatment planning calculation. At two different spatial resolutions of 72 and 300 dpi, each film piece was scanned three consecutive times in the center of…

Abstract 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…

BACKGROUND: Ultra-high dose rate (UHDR) FLASH beams typically deliver dose at rates of >40 Gy/sec. Characterization of these beams with respect to dose, mean dose rate, and dose per pulse requires dosimeters which exhibit high temporal resolution and fast readout capabilities. PURPOSE: A diode EDGE Detector with a newly designed electrometer has been characterized for use in an UHDR electron beam and demonstrated appropriateness for UHDR FLASH radiotherapy dosimetry. METHODS: Dose linearity, mean dose rate, and dose per pulse dependencies of the EDGE Detector were quantified and compared with dosimeters including a W1 scintillator detector, radiochromic film, and ionization chamber that were irradiated with a 10 MeV UHDR beam. The dose, dose rate, and dose per pulse were controlled via an in-house developed scintillation-based feedback mechanism, repetition rate of the linear accelerator, and source-to-surface distance, respectively. Depth-dose profiles and temporal profiles at individual pulse resolution were compared to the film and scintillation measurements, respectively. The…

BACKGROUND: The potential reduction of normal tissue toxicities during FLASH radiotherapy (FLASH-RT) has inspired many efforts to investigate its underlying mechanism and to translate it into the clinic. Such investigations require experimental platforms of FLASH-RT capabilities. PURPOSE: To commission and characterize a 250 MeV proton research beamline with a saturated nozzle monitor ionization chamber for proton FLASH-RT small animal experiments. METHODS: A 2D strip ionization chamber array (SICA) with high spatiotemporal resolution was used to measure spot dwell times under various beam currents and to quantify dose rates for various field sizes. An Advanced Markus chamber and a Faraday cup were irradiated with spot-scanned uniform fields and nozzle currents from 50 to 215 nA to investigate dose scaling relations. The SICA detector was set up upstream to establish a correlation between SICA signal and delivered dose at isocenter to serve as an in vivo dosimeter and monitor the delivered dose rate. Two off-the-shelf brass blocks were used as apertures to shape the dose laterally.…

Abstract Background Plastic scintillators have been used as radiation detectors for the past few years, as they are water‐equivalent and independent of the dose, dose rate, and angle of incidence. In addition, they are also independent of the presence of a magnetic field and could be used for in vivo dosimetry in an MR‐Linac. With the advent of a new commercial scintillation detector, Blue Physics Model 10, its characterization has been performed on an MR‐Linac with a view to future applications. Purpose To perform the dosimetric characterization and study potential applications of a novel commercial plastic scintillation detector in a MR‐Linac. Methods Scintillation detector description, calibration procedure, short‐term repeatability, dose‐response linearity, dose‐rate dependence, angular dependence, and temperature dependence have been studied. Percent‐depth‐dose (PDD) and beam profiles were measured for small fields and a standard field, as well as output factors, for comparison with other PTW detectors: a diamond diode and PinPoint and Semiflex 3D ionization chambers. The…

BACKGROUND: Deep learning has shown promising results to generate MRI-based synthetic CTs and to enable accurate proton dose calculations on MRIs. For clinical implementation of synthetic CTs, quality assurance tools that verify their quality and reliability are required but still lacking. PURPOSE: This study aims to evaluate the predictive value of uncertainty maps generated with Monte Carlo dropout (MCD) for verifying proton dose calculations on deep-learning-based synthetic CTs (sCTs) derived from MRIs in online adaptive proton therapy. METHODS: Two deep-learning models (DCNN and cycleGAN) were trained for CT image synthesis using 101 paired CT-MR images. sCT images were generated using MCD for each model by performing 10 inferences with activated dropout layers. The final sCT was obtained by averaging the inferred sCTs, while the uncertainty map was obtained from the HU variance corresponding to each voxel of 10 sCTs. The resulting uncertainty maps were compared to the observed HU-, range-, WET-, and dose-error maps between the sCT and planning CT. For range and WET errors, the…

PURPOSE: BUS-Set is a reproducible benchmark for breast ultrasound (BUS) lesion segmentation, comprising of publicly available images with the aim of improving future comparisons between machine learning models within the field of BUS. METHOD: Four publicly available datasets were compiled creating an overall set of 1154 BUS images, from five different scanner types. Full dataset details have been provided, which include clinical labels and detailed annotations. Furthermore, nine state-of-the-art deep learning architectures were selected to form the initial benchmark segmentation result, tested using five-fold cross-validation and MANOVA/ANOVA with Tukey statistical significance test with a threshold of 0.01. Additional evaluation of these architectures was conducted, exploring possible training bias, and lesion size and type effects. RESULTS: Of the nine state-of-the-art benchmarked architectures, Mask R-CNN obtained the highest overall results, with the following mean metric scores: Dice score of 0.851, intersection over union of 0.786 and pixel accuracy of 0.975. MANOVA/ANOVA and…

BACKGROUND: Lattice radiation therapy (LRT) alternates regions of high and low doses within the target. The heterogeneous dose distribution is delivered to a geometrical structure of vertices segmented inside the tumor. LRT is typically used to treat patients with large tumor volumes with cytoreduction intent. Due to the geometric complexity of the target volume and the required dose distribution, LRT treatment planning demands additional resources, which may limit clinical integration. PURPOSE: We introduce a fully automated method to (1) generate an ordered lattice of vertices with various sizes and center-to-center distances and (2) perform dose optimization and calculation. We aim to report the dosimetry associated with these lattices to help clinical decision-making. METHODS: who received radiotherapy treatment between 2010 and 2018 at our institution were considered for inclusion. Automated segmentation and dose optimization/calculation were performed by using the Eclipse Scripting Application Programming Interface (ESAPI, v16, Varian Medical Systems, Palo Alto, USA). Vertices…

Abstract Background Deep learning‐based auto‐planning is an active research field; however, for some tasks a treatment planning system (TPS) is still required. Purpose To introduce a deep learning‐based model generating deliverable DICOM RT treatment plans that can be directly irradiated by a linear accelerator (LINAC). The model was based on an encoder‐decoder network and can predict multileaf collimator (MLC) motion sequences for prostate VMAT radiotherapy. Methods A total of 619 treatment plans from 460 patients treated for prostate cancer with single‐arc VMAT were included in this study. An encoder‐decoder network was trained using 465 clinical treatment plans and validated on 77 plans. The performance was analyzed on a separate test set of 77 treatment plans. Separate L1 losses were computed for the leaf and jaw positions as well as the monitor units, with the leaf loss being weighted by a factor of 100 before being added to the other losses. The generated treatment plans were recalculated in a treatment planning system and the dose‐volume metrics and gamma passing rates were…

BACKGROUND: In treatment planning, beam angle optimization (BAO) refers to the selection of a subset with a given number of beam angles from all available angles that provides the best plan quality. BAO is a NP-hard combinatorial problem. Although exhaustive search (ES) can exactly solve BAO by exploring all possible combinations, ES is very time-consuming and practically infeasible. PURPOSE: To the best of our knowledge, (1) no optimization method has been demonstrated that can provide the exact solution to BAO, and (2) no study has validated an optimization method for solving BAO by benchmarking with the optimal BAO solution (e.g., via ES), both of which will be addressed by this work. METHODS: This work considers BAO for proton therapy, for example, the selection of 2-4 beam angles for IMPT. The optimal BAO solution is obtained via ES and serves as the ground truth. A new BAO algorithm, namely angle generation (AG) method, is proposed, and demonstrated to provide nearly-exact solutions for BAO in reference to the ES solution. AG iteratively optimizes the angular set via…

BACKGROUND: Reducing the radiation dose from computed tomography (CT) can significantly reduce the radiation risk to patients. However, low-dose CT (LDCT) suffers from severe and complex noise interference that affects subsequent diagnosis and analysis. Recently, deep learning-based methods have shown superior performance in LDCT image-denoising tasks. However, most methods require many normal-dose and low-dose CT image pairs, which are difficult to obtain in clinical applications. Unsupervised methods, on the other hand, are more general. PURPOSE: Deep learning methods based on GAN networks have been widely used for unsupervised LDCT denoising, but the additional memory requirements of the model also hinder its further clinical application. To this end, we propose a simpler multi-stage denoising framework trained using unpaired data, the progressive cyclical convolutional neural network (PCCNN), which can remove the noise from CT images in latent space. METHODS: Our proposed PCCNN introduces a noise transfer model that transfers noise from LDCT to normal-dose CT (NDCT), denoised CT…

Abstract Background High‐resolution magnetic resonance imaging (MRI) with excellent soft‐tissue contrast is a valuable tool utilized for diagnosis and prognosis. However, MRI sequences with long acquisition time are susceptible to motion artifacts, which can adversely affect the accuracy of post‐processing algorithms. Purpose This study proposes a novel retrospective motion correction method named “motion artifact reduction using conditional diffusion probabilistic model” (MAR‐CDPM). The MAR‐CDPM aimed to remove motion artifacts from multicenter three‐dimensional contrast‐enhanced T1 magnetization‐prepared rapid acquisition gradient echo (3D ceT1 MPRAGE) brain dataset with different brain tumor types. Materials and methods This study employed two publicly accessible MRI datasets: one containing 3D ceT1 MPRAGE and 2D T2‐fluid attenuated inversion recovery (FLAIR) images from 230 patients with diverse brain tumors, and the other comprising 3D T1‐weighted (T1W) MRI images of 148 healthy volunteers, which included real motion artifacts. The former was used to train and evaluate the…

Abstract Background Small coronary arteries containing stents pose a challenge in CT imaging due to metal‐induced blooming artifact. High spatial resolution imaging capability is as the presence of highly attenuating materials limits noninvasive assessment of luminal patency. Purpose The purpose of this study was to quantify the effective lumen diameter within coronary stents using a clinical photon‐counting‐detector (PCD) CT in concert with a convolutional neural network (CNN) denoising algorithm, compared to an energy‐integrating‐detector (EID) CT system. Methods Seven coronary stents of different materials and inner diameters between 3.43 and 4.72 mm were placed in plastic tubes of diameters 3.96–4.87 mm containing 20 mg/mL of iodine solution, mimicking stented contrast‐enhanced coronary arteries. Tubes were placed parallel with or perpendicular to the scanner's z‐axis in an anthropomorphic phantom emulating an average‐sized patient and scanned with a clinical EID‐CT and PCD‐CT. EID scans were performed using our standard coronary computed tomography angiography (cCTA) protocol…

BACKGROUND: Hybrid proton-photon radiotherapy (RT) can provide better plan quality than proton or photon only RT, in terms of robustness of target coverage and sparing of organs-at-risk (OAR). PURPOSE: This work develops a hybrid treatment planning method that can optimize the number of proton and photon fractions as well as proton and photon plan variables, so that the hybrid plans can be clinically delivered day-to-day using either proton or photon machine. METHODS: In the new hybrid treatment planning method, the total dose distribution (sum of proton dose and photon dose) is optimized for robust target coverage and optimal OAR sparing, by jointly optimizing proton spots and photon fluences, while the target dose uniformity is also enforced individually on both proton dose and photon dose, so that the hybrid plans can be separately and robustly delivered on proton or photon machine. To ensure the target dose uniformity for proton and photon plans, the number of proton and photon fractions is explicitly modeled and optimized, so that the target dose for proton and photon dose…

Conformality has been a key requirement in radiation therapy for cancer to minimize normal tissue toxicity while maintaining tumor control. Since 2014, there has been great interest in ultra-high dose rate (UHDR), "FLASH," radiation therapy to enhance this therapeutic window. In multiple pre-clinical studies, it was seen that normal tissue demonstrated less damage due to radiation of various modalities when the same dose was delivered at ultra-high mean dose rates exceeding ∼40 Gy/s while tumor control remained indifferent to changes in dose rate. The scientific community has large-scale interdisciplinary studies to investigate this potentially breakthrough technique to enhance treatment options for cancer. FLASH studies have been performed using a number of modalities and delivery techniques for many pre-clinical models. There have been several studies reporting evidence of the FLASH effect as well as technological developments relating to UHDR studies. There is sustained interest and motivation for this topic as well as many questions that are yet to be answered. We provide a…

BACKGROUND: Fast dose calculation is critical for online and real-time adaptive therapy workflows. While modern physics-based dose algorithms must compromise accuracy to achieve low computation times, deep learning models can potentially perform dose prediction tasks with both high fidelity and speed. PURPOSE: We present a deep learning algorithm that, exploiting synergies between transformer and convolutional layers, accurately predicts broad photon beam dose distributions in few milliseconds. METHODS: The proposed improved Dose Transformer Algorithm (iDoTA) maps arbitrary patient geometries and beam information (in the form of a 3D projected shape resulting from a simple ray tracing calculation) to their corresponding 3D dose distribution. Treating the 3D CT input and dose output volumes as a sequence of 2D slices along the direction of the photon beam, iDoTA solves the dose prediction task as sequence modeling. The proposed model combines a Transformer backbone routing long-range information between all elements in the sequence, with a series of 3D convolutions extracting local…

Abstract Background Linear energy transfer (LET) is closely related to the biological effect of ionizing radiation. Increasing the dose‐averaged LET (LET d ) within the target volume has been proposed as a means to improve clinical outcome for hypoxic tumors. However, doing so can lead to reduced robustness to range uncertainty. Purpose To quantify the relationship between robust target coverage, target dose uniformity, and LET d , we employ robust optimization using dose‐based and LET d ‐based functions and allow varying amounts of target non‐uniformity. Methods and Materials Robust carbon therapy optimization is used to create plans for phantom cases with increasing target sizes (radii 1, 3, and 5 cm). First, the influence of respectively range and setup uncertainty on the LET d in the target is studied. Second, we employ strategies allowing overdosage in the clinical target volume (CTV) or gross tumor volume (GTV), which enable increased LET d in the target. The relationship between robust target coverage and LET d in the target is illustrated by tradeoff curves generated by…

Abstract Background Deformable Image Registration (DIR) is an essential technique required in many applications of radiation oncology. However, conventional DIR approaches typically take several minutes to register one pair of 3D CT images and the resulting deformable vector fields (DVFs) are only specific to the pair of images used, making it less appealing for clinical application. Purpose A deep‐learning‐based DIR method using CT images is proposed for lung cancer patients to address the common drawbacks of the conventional DIR approaches and in turn can accelerate the speed of related applications, such as contour propagation, dose deformation, adaptive radiotherapy (ART), etc. Methods A deep neural network based on VoxelMorph was developed to generate DVFs using CT images collected from 114 lung cancer patients. Two models were trained with the weighted mean absolute error (wMAE) loss and structural similarity index matrix (SSIM) loss (optional) (i.e., the MAE model and the M+S model). In total, 192 pairs of initial CT (iCT) and verification CT (vCT) were included as a training…

BACKGROUND: Deep learning auto-segmentation (DLAS) models have been adopted in the clinic; however, they suffer from performance deterioration owing to the clinical practice variability. Some commercial DLAS software provide an incremental retraining function that enables users to train a custom model using their institutional data to account for clinical practice variability. PURPOSE: This study was performed to evaluate and implement the commercial DLAS software with the incremental retraining function for definitive treatment of patients with prostate cancer in a multi-user environment. METHODS: CT-based target organs and organs-at-risk (OAR) delineation of 215 prostate cancer patients were utilized. The performance of three commercial DLAS software built-in models was validated with 20 patients. A retrained custom model was developed using 100 patients and evaluated on the remaining data (n = 115). Dice similarity coefficient (DSC), Hausdorff distance (HD), mean surface distance (MSD), and surface DSC (SDSC) were utilized for quantitative evaluation. A multi-rater qualitative…

PURPOSE: To develop a neural ordinary differential equation (ODE) model for visualizing deep neural network behavior during multi-parametric MRI-based glioma segmentation as a method to enhance deep learning explainability. METHODS: By hypothesizing that deep feature extraction can be modeled as a spatiotemporally continuous process, we implemented a novel deep learning model, Neural ODE, in which deep feature extraction was governed by an ODE parameterized by a neural network. The dynamics of (1) MR images after interactions with the deep neural network and (2) segmentation formation can thus be visualized after solving the ODE. An accumulative contribution curve (ACC) was designed to quantitatively evaluate each MR image's utilization by the deep neural network toward the final segmentation results. The proposed Neural ODE model was demonstrated using 369 glioma patients with a 4-modality multi-parametric MRI protocol: T1, contrast-enhanced T1 (T1-Ce), T2, and FLAIR. Three Neural ODE models were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT),…

BACKGROUND: Contouring error is one of the top failure modes in radiation treatment. Multiple efforts have been made to develop tools to automatically detect segmentation errors. Deep learning-based auto-segmentation (DLAS) has been used as a baseline for flagging manual segmentation errors, but those efforts are limited to using only one or two contour comparison metrics. PURPOSE: The purpose of this research is to develop an improved contouring quality assurance system to identify and flag manual contouring errors. METHODS AND MATERIALS: DLAS contours were used as a reference to compare with manually segmented contours. A total of 27 geometric agreement metrics were determined from the comparisons between the two segmentation approaches. Feature selection was performed to optimize the training of a machine learning classification model to identify potential contouring errors. A public dataset with 339 cases was used to train and test the classifier. Four independent classifiers were trained using five-fold cross validation, and the predictions from each classifier were ensembled…

Abstract Background Iterative reconstruction (IR) has increasingly replaced traditional reconstruction methods in computed tomography (CT). The next paradigm shift in image reconstruction is likely to come from artificial intelligence, with deep learning reconstruction (DLR) solutions already entering the clinic. An enduring disadvantage to IR has been a change in noise texture, which can affect diagnostic confidence. DLR has demonstrated the potential to overcome this issue and has recently become available for dual‐energy CT. Purpose To evaluate the spatial resolution, noise properties, and detectability index of a commercially available DLR algorithm for dual‐energy CT of the abdomen and compare it to single‐energy (SE) CT. Methods An oval 25 cm x 35 cm custom‐made phantom was scanned on a GE Revolution CT scanner (GE Healthcare, Waukesha, WI) at two dose levels (13 and 5 mGy) and two iodine concentrations (8 and 2 mg/mL), using three typical abdominal scan protocols: dual‐energy (DE), SE 80 kV (SE‐80 kV) and SE 120 kV (SE‐120 kV). Reconstructions were performed with three…

BACKGROUND: The intensities (i.e., number of protons in monitor unit [MU]) of deliverable proton spots need to be either zero or meet a minimum-MU (MMU) threshold, which is a nonconvex problem. Since the dose rate is proportionally associated with the MMU threshold, higher-dose-rate proton radiation therapy (RT) (e.g., efficient intensity modulated proton therapy (IMPT) and ARC proton therapy, and high-dose-rate-induced FLASH effect needs to solve the MMU problem with larger MMU threshold, which however makes the nonconvex problem more difficult to solve. PURPOSE: This work will develop a more effective optimization method based on orthogonal matching pursuit (OMP) for solving the MMU problem with large MMU thresholds, compared to state-of-the-art methods, such as alternating direction method of multipliers (ADMM), proximal gradient descent method (PGD), or stochastic coordinate descent method (SCD). METHODS: The new method consists of two essential components. First, the iterative convex relaxation (ICR) method is used to determine the active sets for dose-volume planning…