The 50 most-cited articles in Medical Physics, 2024

A chemistry module has been implemented in Geant4-DNA since Geant4 version 10.1 to simulate the radiolysis of water after irradiation. It has been used in a number of applications, including the calculation of G-values and early DNA damage, allowing the comparison with experimental data. Since the first version, numerous modifications have been made to the module to improve the computational efficiency and extend the simulation to homogeneous kinetics in bulk solution. With these new developments, new applications have been proposed and released as Geant4 examples, showing how to use chemical processes and models. This work reviews the models implemented and application developments for modeling water radiolysis in Geant4-DNA as reported in the ESA BioRad III Project.

BACKGROUND: Denoising low-dose computed tomography (CT) images is a critical task in medical image computing. Supervised deep learning-based approaches have made significant advancements in this area in recent years. However, these methods typically require pairs of low-dose and normal-dose CT images for training, which are challenging to obtain in clinical settings. Existing unsupervised deep learning-based methods often require training with a large number of low-dose CT images or rely on specially designed data acquisition processes to obtain training data. PURPOSE: To address these limitations, we propose a novel unsupervised method that only utilizes normal-dose CT images during training, enabling zero-shot denoising of low-dose CT images. METHODS: Our method leverages the diffusion model, a powerful generative model. We begin by training a cascaded unconditional diffusion model capable of generating high-quality normal-dose CT images from low-resolution to high-resolution. The cascaded architecture makes the training of high-resolution diffusion models more feasible.…

Abstract Background The automatic segmentation of medical images has widespread applications in modern clinical workflows. The Segment Anything Model (SAM), a recent development of foundational models in computer vision, has become a universal tool for image segmentation without the need for specific domain training. However, SAM's reliance on prompts necessitates human‐computer interaction during the inference process. Its performance on specific domains can also be limited without additional adaptation. In contrast, traditional models like nnUNet are designed to perform segmentation tasks automatically during inference and can work well for each specific domain, but they require extensive training on domain‐specific datasets. Purpose To leverage the advantages of both foundational and domain‐specific models and achieve fully automated segmentation with limited training samples, we propose nnSAM, which combines the robust feature extraction capabilities of SAM with the automatic configuration abilities of nnUNet to enhance the accuracy and robustness of medical image segmentation…

PURPOSE: Positron Emission Tomography (PET) has been a commonly used imaging modality in broad clinical applications. One of the most important tradeoffs in PET imaging is between image quality and radiation dose: high image quality comes with high radiation exposure. Improving image quality is desirable for all clinical applications while minimizing radiation exposure is needed to reduce risk to patients. METHODS: We introduce PET Consistency Model (PET-CM), an efficient diffusion-based method for generating high-quality full-dose PET images from low-dose PET images. It employs a two-step process, adding Gaussian noise to full-dose PET images in the forward diffusion, and then denoising them using a PET Shifted-window Vision Transformer (PET-VIT) network in the reverse diffusion. The PET-VIT network learns a consistency function that enables direct denoising of Gaussian noise into clean full-dose PET images. PET-CM achieves state-of-the-art image quality while requiring significantly less computation time than other methods. Evaluation with normalized mean absolute error (NMAE),…

BACKGROUND: Long-lasting efforts have been made to reduce radiation dose and thus the potential radiation risk to the patient for computed tomography (CT) acquisitions without severe deterioration of image quality. To this end, various techniques have been employed over the years including iterative reconstruction methods and noise reduction algorithms. PURPOSE: Recently, deep learning-based methods for noise reduction became increasingly popular and a multitude of papers claim ever improving performance both quantitatively and qualitatively. However, the lack of a standardized benchmark setup and inconsistencies in experimental design across studies hinder the verifiability and reproducibility of reported results. METHODS: In this study, we propose a benchmark setup to overcome those flaws and improve reproducibility and verifiability of experimental results in the field. We perform a comprehensive and fair evaluation of several state-of-the-art methods using this standardized setup. RESULTS: Our evaluation reveals that most deep learning-based methods show statistically similar…

Cone-beam CT (CBCT) is the most commonly used onboard imaging technique for target localization in radiation therapy. Conventional 3D CBCT acquires x-ray cone-beam projections at multiple angles around the patient to reconstruct 3D images of the patient in the treatment room. However, despite its wide usage, 3D CBCT is limited in imaging disease sites affected by respiratory motions or other dynamic changes within the body, as it lacks time-resolved information. To overcome this limitation, 4D-CBCT was developed to incorporate a time dimension in the imaging to account for the patient's motion during the acquisitions. For example, respiration-correlated 4D-CBCT divides the breathing cycles into different phase bins and reconstructs 3D images for each phase bin, ultimately generating a complete set of 4D images. 4D-CBCT is valuable for localizing tumors in the thoracic and abdominal regions where the localization accuracy is affected by respiratory motions. This is especially important for hypofractionated stereotactic body radiation therapy (SBRT), which delivers much higher…

BACKGROUND AND PURPOSE: FLASH or ultra-high dose rate (UHDR) radiation therapy (RT) has gained attention in recent years for its ability to spare normal tissues relative to conventional dose rate (CDR) RT in various preclinical trials. However, clinical implementation of this promising treatment option has been limited because of the lack of availability of accelerators capable of delivering UHDR RT. Commercial options are finally reaching the market that produce electron beams with average dose rates of up to 1000 Gy/s. We established a framework for the acceptance, commissioning, and periodic quality assurance (QA) of electron FLASH units and present an example of commissioning. METHODS: A protocol for acceptance, commissioning, and QA of UHDR linear accelerators was established by combining and adapting standards and professional recommendations for standard linear accelerators based on the experience with UHDR at four clinical centers that use different UHDR devices. Non-standard dosimetric beam parameters considered included pulse width, pulse repetition frequency, dose per…

BACKGROUND: Although cone beam computed tomography (CBCT) has lower resolution compared to planning CTs (pCT), its lower dose, higher high-contrast resolution, and shorter scanning time support its widespread use in clinical applications, especially in ensuring accurate patient positioning during the image-guided radiation therapy (IGRT) process. PURPOSE: While CBCT is critical to IGRT, CBCT image quality can be compromised by severe stripe and scattering artifacts. Tumor movement secondary to respiratory motion also decreases CBCT resolution. In order to improve the image quality of CBCT, we propose a Lung Diffusion Model (L-DM) framework. METHODS: Our proposed algorithm is based on a conditional diffusion model trained on pCT and deformed CBCT (dCBCT) image pairs to synthesize lung CT images from dCBCT images and benefit CBCT-based radiotherapy. dCBCT images were used as the constraint for the L-DM. The image quality and Hounsfield unit (HU) values of the synthetic CTs (sCT) images generated by the proposed L-DM were compared to three selected mainstream generation models.…

Abstract Background Ultrahigh dose‐rate radiation (UHDR) produces less hydrogen peroxide (H 2 O 2 ) in pure water, as suggested by some experimental studies, and is used as an argument for the validity of the theory that FLASH spares the normal tissue due to less reactive oxygen species (ROS) production. In contrast, most Monte Carlo simulation studies suggest the opposite. Purpose We aim to unveil the effect of UHDR on H 2 O 2 production in pure water and its underlying mechanism, to serve as a benchmark for Monte Carlo simulation. We hypothesized that the reaction of solvated electrons () removing hydroxyl radicals (•OH), the precursor of H 2 O 2 , is the reason why UHDR leads to a lower G‐value (molecules/100 eV) for H 2 O 2 (G[H 2 O 2 ]), because: 1, the third‐order reaction between and •OH is more sensitive to increased instantaneous ROS concentration by UHDR than a two‐order reaction of •OH self‐reaction producing H 2 O 2 ; 2, has two times higher diffusion coefficient and higher reaction rate constant than that of •OH, which means would dominate the competition for •OH and…

BACKGROUND: The extremely fast delivery of doses with ultra high dose rate (UHDR) beams necessitates the investigation of novel approaches for real-time dosimetry and beam monitoring. This aspect is fundamental in the perspective of the clinical application of FLASH radiotherapy (FLASH-RT), as conventional dosimeters tend to saturate at such extreme dose rates. PURPOSE: This study aims to experimentally characterize newly developed silicon carbide (SiC) detectors of various active volumes at UHDRs and systematically assesses their response to establish their suitability for dosimetry in FLASH-RT. METHODS: ) and thicknesses (10-20 µm), were irradiated using 9 MeV UHDR pulsed electron beams accelerated by the ElectronFLASH linac at the Centro Pisano for FLASH Radiotherapy (CPFR). The linearity of the SiC response as a function of the delivered dose per pulse (DPP), which in turn corresponds to a specific instantaneous dose rate, was studied under various experimental conditions by measuring the produced charge within the SiC active layer with an electrometer. Due to the extremely high…

BACKGROUND: The Joint AAPM-ESTRO TG-360 is developing a quantitative framework to evaluate treatment verification systems used for patient-specific quality assurance (PSQA). A subgroup was commissioned to determine which potential failure modes had the greatest risk to treatment quality and safety, and therefore should be evaluated as part of the PSQA verification. PURPOSE: To create an extensive database of potential radiotherapy failure modes that should be detected by PSQA and to determine their relative importance for maximizing treatment quality. METHODS: The subgroup consisted of eight physicists from seven countries, including representatives from three international quality assurance groups. We collected error reports from RO-ILS, SAFRON, AAPM TG publications, and other literature, including international audits. We focused on the subset of failure modes that impact whether the planned dose matches the dose received by the patient. We performed a failure-mode-and-effects analysis (FMEA), estimating the severity (S), occurrence (O), and detectability (D) of each failure mode.…

PURPOSE: This manuscript presents RADCURE, one of the most extensive head and neck cancer (HNC) imaging datasets accessible to the public. Initially collected for clinical radiation therapy (RT) treatment planning, this dataset has been retrospectively reconstructed for use in imaging research. ACQUISITION AND VALIDATION METHODS: RADCURE encompasses data from 3346 patients, featuring computed tomography (CT) RT simulation images with corresponding target and organ-at-risk contours. These CT scans were collected using systems from three different manufacturers. Standard clinical imaging protocols were followed, and contours were manually generated and reviewed at weekly RT quality assurance rounds. RADCURE imaging and structure set data was extracted from our institution's radiation treatment planning and oncology information systems using a custom-built data mining and processing system. Furthermore, images were linked to our clinical anthology of outcomes data for each patient and includes demographic, clinical and treatment information based on the 7th edition TNM staging system…

BACKGROUND: Photon counting computed tomography (PCCT) employs direct and spectrally resolved counting of individual x-ray quanta, enhancing image quality compared to the standard energy-integrating CT (EICT). PURPOSE: To evaluate the quantitative improvements in CT image quality metrics by comparing the first medical PCCT with a state-of-the-art EICT. METHODS: The PCCT versus EICT noise improvement ratio R was derived from the quantum statistics of the measurement process and measured across the clinical x-ray flux range for both systems. Detector and system modulation transfer functions (MTFs) were obtained using tilted-slit and wire phantom measurements. Image root mean square (RMS) noise, noise power spectrum (NPS), and x-ray patient dose were compared using a CatPhan phantom at two identical clinical target resolutions. RESULTS: The measurement of the PCCT noise improvement ratio R showed an elimination of electronic noise and a 10% noise transfer advantage. The PCCT detector MTF exhibited 3x higher angular resolution limits in comparison to EICT and close to ideal sinc…

BACKGROUND: Although the FLASH radiotherapy (FLASH) can improve the sparing of organs-at-risk (OAR) via the FLASH effect, it is generally a tradeoff between the physical dose coverage and the biological FLASH coverage, for which the concept of FLASH effective dose (FED) is needed to quantify the net improvement of FLASH, compared to the conventional radiotherapy (CONV). PURPOSE: This work will develop the first-of-its-kind treatment planning method called simultaneous dose and dose rate optimization via dose modifying factor modeling (SDDRO-DMF) for proton FLASH that directly optimizes FED. METHODS: SDDRO-DMF models and optimizes FED using FLASH dose modifying factor (DMF) models, which can be classified into two categories: (1) the phenomenological model of the FLASH effect, such as the FLASH effectiveness model (FEM); (2) the mechanistic model of the FLASH radiobiology, such as the radiolytic oxygen depletion (ROD) model. The general framework of SDDRO-DMF will be developed, with specific DMF models using FEM and ROD, as a demonstration of general applicability of SDDRO-DMF for…

Abstract Background Biology‐guided radiotherapy (BgRT) is a novel radiotherapy delivery technique that utilizes the tumor itself to guide dynamic delivery of treatment dose to the tumor. The RefleXion X1 system is the first radiotherapy system developed to deliver SCINTIX® BgRT. The X1 is characterized by its split arc design, employing two 90‐degree positron emission tomography (PET) arcs to guide therapeutic radiation beams in real time, currently cleared by FDA to treat bone and lung tumors. Purpose This study aims to comprehensively evaluate the capabilities of the SCINTIX radiotherapy delivery system by evaluating its sensitivity to changes in PET contrast, its adaptability in the context of patient motion, and its performance across a spectrum of prescription doses. Methods A series of experimental scenarios, both static and dynamic, were designed to assess the SCINTIX BgRT system's performance, including an end‐to‐end test. These experiments involved a range of factors, including changes in PET contrast, motion, and prescription doses. Measurements were performed using a…

BACKGROUND: Dosimetry in ultra-high dose rate (UHDR) beamlines is significantly challenged by limitations in real-time monitoring and accurate measurement of beam output, beam parameters, and delivered doses using conventional radiation detectors, which exhibit dependencies in ultra-high dose-rate (UHDR) and high dose-per-pulse (DPP) beamline conditions. PURPOSE: In this study, we characterized the response of the Exradin W2 plastic scintillator (Standard Imaging, Inc.), a water-equivalent detector that provides measurements with a time resolution of 100 Hz, to determine its feasibility for use in UHDR electron beamlines. METHODS: The W2 scintillator was exposed to an UHDR electron beam with different beam parameters by varying the pulse repetition frequency (PRF), pulse width (PW), and pulse amplitude settings of an electron UHDR linear accelerator system. The response of the W2 scintillator was evaluated as a function of the total integrated dose delivered, DPP, and mean and instantaneous dose rate. To account for detector radiation damage, the signal sensitivity (pC/Gy) of the W2…

BACKGROUND: Reference dosimetry in ultra-high dose rate (UHDR) beamlines is significantly hindered by limitations in conventional ionization chamber design. In particular, conventional chambers suffer from severe charge collection efficiency (CCE) degradation in high dose per pulse (DPP) beams. PURPOSE: The aim of this study was to optimize the design and performance of parallel plate ion chambers for use in UHDR dosimetry applications, and evaluate their potential as reference class chambers for calibration purposes. Three chamber designs were produced to determine the influence of the ion chamber response on electrode separation, field strength, and collection volume on the ion chamber response under UHDR and ultra-high dose per pulse (UHDPP) conditions. METHODS: Three chambers were designed and produced: the A11-VAR (0.2-1.0 mm electrode gap, 20 mm diameter collector), the A11-TPP (0.3 mm electrode gap, 20 mm diameter collector), and the A30 (0.3 mm electrode gap, 5.4 mm diameter collector). The chambers underwent full characterization using an UHDR 9 MeV electron beam with…

BACKGROUND: High-quality 3D-anatomy of the day is needed for treatment plan adaptation in radiotherapy. For online x-ray-based CBCT workflows, one approach is to create a synthetic CT or to utilize a fan-beam CT with corresponding registrations. The former potentially introduces uncertainties in the dose calculation if deformable image registration is used. The latter can introduce burden and complexity to the process, the facility, and the patient. PURPOSE: Using the CBCT of the day, acquired on the treatment device, for direct dose calculation and plan adaptation can overcome these limitations. This study aims to assess the accuracy of the calculated dose on the CBCT scans acquired on a Halcyon linear accelerator equipped with HyperSight. METHODS: HyperSight's new CBCT reconstruction algorithm includes improvements in scatter correction, HU calibration of the imager, and beam shape adaptation. Furthermore, HyperSight introduced a new x-ray detector. To show the effect of the implemented improvements, gamma comparisons of 2%/2 mm, 2%/1 mm, and 1%/1 mm were made between the dose…

BACKGROUND: Predicting the accurate preoperative staging of bladder cancer (BLCA), which markedly affects treatment decisions and patient outcomes, using traditional clinical parameters is challenging. Nevertheless, emerging studies in radiomics, especially machine learning-based computed tomography (CT) image-based radiomics, hold promise in improving stage prediction accuracy in various tumors. However, the comparative performance and clinical utility of models for BLCA are under investigation. PURPOSE: We aimed to investigate the application value of machine learning-based CT radiomics in preoperative staging prediction by comparing the performance of clinical, radiomics, and clinical-radiomics combined models. METHODS: A retrospective cohort of 105 patients with initial BLCA was randomized into training (70%) and testing (30%) cohorts. Radiomics features were extracted from CT images using the optimal feature filter, followed by the application of the least absolute shrinkage and selection operator algorithm for optimum feature selection. Furthermore, machine learning algorithms…

Abstract Background FLASH Radiotherapy (RT) is an emergent cancer RT modality where an entire therapeutic dose is delivered at more than 1000 times higher dose rate than conventional RT. For clinical trials to be conducted safely, a precise and fast beam monitor that can generate out‐of‐tolerance beam interrupts is required. This paper describes the overall concept and provides results from a prototype ultra‐fast, scintillator‐based beam monitor for both proton and electron beam FLASH applications. Purpose A FLASH Beam Scintillator Monitor (FBSM) is being developed that employs a novel proprietary scintillator material. The FBSM has capabilities that conventional RT detector technologies are unable to simultaneously provide: (1) large area coverage; (2) a low mass profile; (3) a linear response over a broad dynamic range; (4) radiation hardness; (5) real‐time analysis to provide an IEC‐compliant fast beam‐interrupt signal based on true two‐dimensional beam imaging, radiation dosimetry and excellent spatial resolution. Methods The FBSM uses a proprietary low mass, less than 0.5 mm…

BACKGROUND: Efficient and accurate delineation of organs at risk (OARs) is a critical procedure for treatment planning and dose evaluation. Deep learning-based auto-segmentation of OARs has shown promising results and is increasingly being used in radiation therapy. However, existing deep learning-based auto-segmentation approaches face two challenges in clinical practice: generalizability and human-AI interaction. A generalizable and promptable auto-segmentation model, which segments OARs of multiple disease sites simultaneously and supports on-the-fly human-AI interaction, can significantly enhance the efficiency of radiation therapy treatment planning. PURPOSE: Meta's segment anything model (SAM) was proposed as a generalizable and promptable model for next-generation natural image segmentation. We further evaluated the performance of SAM in radiotherapy segmentation. METHODS: Computed tomography (CT) images of clinical cases from four disease sites at our institute were collected: prostate, lung, gastrointestinal, and head & neck. For each case, we selected the OARs important in…

Abstract Background Treatment planning is currently a patient specific, time‐consuming, and resource demanding task in radiotherapy. Dose‐volume histogram (DVH) prediction plays a critical role in automating this process. The geometric relationship between DVHs in radiotherapy plans and organs‐at‐risk (OAR) and planning target volume (PTV) has been well established. This study explores the potential of deep learning models for predicting DVHs using images and subsequent human intervention facilitated by a large‐language model (LLM) to enhance the planning quality. Method We propose a pipeline to convert unstructured images to a structured graph consisting of image‐patch nodes and dose nodes. A novel Dose Graph Neural Network (DoseGNN) model is developed for predicting DVHs from the structured graph. The proposed DoseGNN is enhanced with the LLM to encode massive knowledge from prescriptions and interactive instructions from clinicians. In this study, we introduced an online human‐AI collaboration (OHAC) system as a practical implementation of the concept proposed for the automation…

Multi-energy computed tomography (MECT) offers the opportunity for advanced visualization, detection, and quantification of select elements (e.g., iodine) or materials (e.g., fat) beyond the capability of standard single-energy computed tomography (CT). However, the use of MECT requires careful consideration as substantially different hardware and software approaches have been used by manufacturers, including different sets of user-selected or hidden parameters that affect the performance and radiation dose of MECT. Another important consideration when designing MECT protocols is appreciation of the specific tasks being performed; for instance, differentiating between two different materials or quantifying a specific element. For a given task, it is imperative to consider both the radiation dose and task-specific image quality requirements. Development of a quality control (QC) program is essential to ensure the accuracy and reproducibility of these MECT applications. Although standard QC procedures have been well established for conventional single-energy CT, the substantial…

The rapid advancement in the field of medical imaging presents a challenge in keeping up to date with the necessary objective evaluations and optimizations for safe and effective use in clinical settings. These evaluations are traditionally done using clinical imaging trials, which while effective, pose several limitations including high costs, ethical considerations for repetitive experiments, time constraints, and lack of ground truth. To tackle these issues, virtual trials (aka in silico trials) have emerged as a promising alternative, using computational models of human subjects and imaging devices, and observer models/analysis to carry out experiments. To facilitate the widespread use of virtual trials within the medical imaging research community, a major need is to establish a common consensus framework that all can use. Based on the ongoing efforts of an AAPM Task Group (TG387), this article provides a comprehensive overview of the requirements for establishing virtual imaging trial frameworks, paving the way toward their widespread use within the medical imaging research…

BACKGROUND: Complexity metrics are mathematical quantities designed to quantify aspects of radiotherapy treatment plans that may affect both their deliverability and dosimetric accuracy. Despite numerous studies investigating their utility, there remains a notable absence of shared tools for their extraction. PURPOSE: This study introduces UCoMX (Universal Complexity Metrics Extractor), a software package designed for the extraction of complexity metrics from the DICOM-RT plan files of radiotherapy treatments. METHODS: UCoMX is developed around two extraction engines: VCoMX (VMAT Complexity Metrics Extractor) for VMAT/IMRT plans, and TCoMX (Tomotherapy Complexity Metrics Extractor) tailored for Helical Tomotherapy plans. The software, built using Matlab, is freely available in both Matlab-based and stand-alone versions. More than 90 complexity metrics, drawn from relevant literature, are implemented in the package: 43 for VMAT/IMRT and 51 for Helical Tomotherapy. RESULTS: The package is designed to read DICOM-RT plan files generated by most commercially available Treatment Planning…

BACKGROUND: Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) stand as pivotal diagnostic tools for brain disorders, offering the potential for mutually enriching disease diagnostic perspectives. However, the costs associated with PET scans and the inherent radioactivity have limited the widespread application of PET. Furthermore, it is noteworthy to highlight the promising potential of high-field and ultra-high-field neuroimaging in cognitive neuroscience research and clinical practice. With the enhancement of MRI resolution, a related question arises: can high-resolution MRI improve the quality of PET images? PURPOSE: This study aims to enhance the quality of synthesized PET images by leveraging the superior resolution capabilities provided by high-field and ultra-high-field MRI. METHODS: From a statistical perspective, the joint probability distribution is considered the most direct and fundamental approach for representing the correlation between PET and MRI. In this study, we proposed a novel model, the joint diffusion attention model, namely, the joint…

BACKGROUND: Photon-counting computed tomography (PCCT) has recently emerged into clinical use; however, its optimum imaging protocols and added benefits remains unknown in terms of providing more accurate lung density quantification compared to energy-integrating computed tomography (EICT) scanners. PURPOSE: To systematically assess the performance of a clinical PCCT scanner for lung density quantifications and compare it against EICT. METHODS: This cross-sectional study involved a retrospective analysis of subjects scanned (August-December 2021) using a clinical PCCT system. The influence of altering reconstruction parameters was studied (reconstruction kernel, pixel size, slice thickness). A virtual CT dataset of anthropomorphic virtual subjects was acquired to demonstrate the correspondence of findings to clinical dataset, and to perform systematic imaging experiments, not possible using human subjects. The virtual subjects were imaged using a validated, scanner-specific CT simulator of a PCCT and two EICT (defined as EICT A and B) scanners. The images were evaluated using mean…

Abstract Background FLASH radiotherapy (FLASH‐RT) with ultra‐high dose rate has yielded promising results in reducing normal tissue toxicity while maintaining tumor control. Planning with single‐energy proton beams modulated by ridge filters (RFs) has been demonstrated feasible for FLASH‐RT. Purpose This study explored the feasibility of a streamlined pin‐shaped RF (pin‐RF) design, characterized by coarse resolution and sparsely distributed ridge pins, for single‐energy proton FLASH planning. Methods An inverse planning framework integrated within a treatment planning system was established to design streamlined pin RFs for single‐energy FLASH planning. The framework involves generating a multi‐energy proton beam plan using intensity‐modulated proton therapy (IMPT) planning based on downstream energy modulation strategy (IMPT‐DS), followed by a nested pencil‐beam‐direction‐based (PBD‐based) spot reduction process to iteratively reduce the total number of PBDs and energy layers along each PBD for the IMPT‐DS plan. The IMPT‐DS plan is then translated into the pin‐RFs and the…

BACKGROUND: In radiation therapy (RT), accelerated partial breast irradiation (APBI) has emerged as an increasingly preferred treatment modality over conventional whole breast irradiation due to its targeted dose delivery and shorter course of treatment. APBI can be delivered through various modalities including Cobalt-60-based systems and linear accelerators with C-arm, O-ring, or robotic arm design. Each modality possesses distinct features, such as beam energy or the degrees of freedom in treatment planning, which influence their respective dose distributions. These modality-specific considerations emphasize the need for a quantitative approach in determining the optimal dose delivery modality on a patient-specific basis. However, manually generating treatment plans for each modality across every patient is time-consuming and clinically impractical. PURPOSE: We aim to develop an efficient and personalized approach for determining the optimal RT modality for APBI by training predictive models using two different deep learning-based convolutional neural networks. The baseline…

BACKGROUND: Motor dysfunctions in children with cerebral palsy (CP) are caused by nonprogressive brain damage. Understanding the functional characteristics of the brain is important for rehabilitation. PURPOSE: This paper aimed to study the brain networks of children with CP during bilateral lower limb movement using functional near-infrared spectroscopy (fNIRS) and to explore effective fNIRS indices for reflecting functional brain activity. METHODS: Using fNIRS, cerebral oxygenation signals in the bilateral prefrontal cortex (LPFC/RPFC) and motor cortex (LMC/RMC) were recorded from fifteen children with spastic CP and seventeen children with typical development (CTDs) in the resting state and during bilateral lower limb movement. Functional connectivity matrices based on phase-locking values (PLVs) were calculated using Hilbert transformation, and binary networks were constructed at different sparsity levels. Network metrics such as the clustering coefficient, global efficiency, local efficiency, and transitivity were calculated. Furthermore, the time-varying curves of network…

Abstract This review article synthesizes key findings from studies on the use of diamond dosimeters in advanced radiotherapy techniques, showcasing their applications, challenges, and contributions to enhancing dosimetric accuracy. The article explores various dosimeters, highlighting synthetic diamond dosimeters as potential candidates especially due to their high spatial resolution and negligible ion recombination effect. The clinically validated commercial dosimeter, PTW microDiamond (mD), faces limitations in small fields, proton and hadron therapy and ultra‐high dose per pulse (UHDPP) conditions. Variability in reported values for field sizes 2 2 is noted, reflecting the competition between volume averaging and density perturbation effects. PTW's introduction of flashDiamond (fD) holds promise for dosimetric measurements in UHDPP conditions and is reliable for commissioning ultra‐high dose rate (UHDR) electron beam systems, pending the clinical validation of the device. Other advancements in diamond detectors, such as in 3D configurations and real‐time dose per pulse x‐ray…

BACKGROUND: Minibeam represents a preclinical spatially fractionated radiotherapy modality with great translational potential. The advantage lies in its high therapeutic index (compared to GRID and LATTICE) and ability to treat at greater depth (compared to microbeam). Proton minibeam radiotherapy (pMBRT) is a synergy of proton and minibeam. While the single-gantry proton facility has gained popularity due to its affordability and compact design, it often has limited beam time available for research purposes. Conversely, given the current requirement of pMBRT on specific minibeam hardware collimators, necessitates a reproducible and fast setup to minimize pMBRT treatment time and streamline the switching time between pMBRT and conventional treatment for clinically translation. PURPOSE: The contribution of this work is the development and characterization of the first pMBRT system tailored for single-gantry proton facility. The system allows for efficient and reproducible plug-and-play setup, achievable within minutes. METHODS: The single room pMBRT system is constructed based on IBA…

BACKGROUND: While the Bragg peak proton beam (BP) is capable of superior target conformity and organs-at-risk sparing than the transmission proton beam (TB), its efficacy in FLASH-RT is hindered by both a slow energy switching process and the beam current. A universal range shifter (URS) can pull back the high-energy proton beam while preserving the beam current. Meanwhile, a superconducting gantry with large momentum acceptance (LMA-SC gantry) enables fast energy switching. PURPOSE: This study explores the feasibility of multiple-energy BP FLASH-RT on the LMA-SC gantry. METHOD AND MATERIALS: A simultaneous dose and spot map optimization algorithm was developed for BP FLASH-RT treatment planning to improve the dose delivery efficiency. The URS was designed to be 0-27 cm thick, with 1 cm per step. BP plans using the URS were optimized using single-field optimization (SFO) and multiple-field optimization (MFO) for ten prostate cancer patients and ten lung cancer patients. The plan delivery parameters, dose, and dose rate metrics of BP plans were compared to those of TB plans using the…

BACKGROUND: Bi-parametric magnetic resonance imaging (bpMRI) has demonstrated promising results in prostate cancer (PCa) detection. Vision transformers have achieved competitive performance compared to convolutional neural network (CNN) in deep learning, but they need abundant annotated data for training. Self-supervised learning can effectively leverage unlabeled data to extract useful semantic representations without annotation and its associated costs. PURPOSE: This study proposes a novel self-supervised learning framework and a transformer model to enhance PCa detection using prostate bpMRI. METHODS AND MATERIALS: We introduce a novel end-to-end Cross-Shaped windows (CSwin) transformer UNet model, CSwin UNet, to detect clinically significant prostate cancer (csPCa) in prostate bpMRI. We also propose a multitask self-supervised learning framework to leverage unlabeled data and improve network generalizability. Using a large prostate bpMRI dataset (PI-CAI) with 1476 patients, we first pretrain CSwin transformer using multitask self-supervised learning to improve data-efficiency…

BACKGROUND: The permitted input power density of rotating anode x-ray sources is limited by the performance of available target materials. The commonly used simplified formulas for the focal spot surface temperature ignore the tube voltage despite its variation in clinical practice. Improved modeling of electron transport and target erosion, as proposed in this work, improves the prediction of x-ray output degradation by target erosion, the absolute x-ray dose output and the quality of diagnostic imaging and orthovolt cancer therapy for a wide range of technique factors. PURPOSE: Improved modeling of electronic power absorption to include volume effects and surface erosion, to improve the understanding of x-ray output degradation, enhance the reliability of x-ray tubes, and safely widen their fields of use. METHODS: We combine Monte Carlo electron transport simulations, coupled thermoelasticity finite element modelling, erosion-induced surface granularity, and the temperature dependence of thermophysical and thermomechanical target properties. A semi-empirical thermomechanical…

BACKGROUND: The role of radiolytic oxygen consumption for the in-vitro "Ultra-High Dose Rate" (UHDR) sparing and in-vivo FLASH effect is subject to active debate, but data on key dependencies such as the radiation quality are lacking. PURPOSE: The influence of "dose-averaged Linear Energy Transfer" (LETd) and dose rate on radiolytic oxygen consumption was investigated by monitoring the oxygen concentration during irradiation with electrons, protons, helium, carbon, and oxygen ions at UHDR and "Standard Dose Rates" (SDR). METHODS: Sealed "Bovine Serum Albumin" (BSA) 5% samples were exposed to 15 Gy of electrons and protons, and for the first time helium, carbon, and oxygen ions with LETd values of 1, 5.4, 14.4, 65, and 100.3 keV/µm, respectively, delivered at mean dose rates of either 0.3-0.4 Gy/s for SDR or approximately 100 Gy/s for UHDR. The Oxylite (Oxford Optronics) system allowed measurements of the oxygen concentration before and after irradiation to calculate the oxygen consumption rate. RESULTS: The oxygen consumption rate was found to decrease with increasing LETd from…

AAPM Task Group Report 135.B covers new technology components that have been added to an established radiosurgery platform and updates the components that were not well covered in the previous report. Considering the current state of the platform, this task group (TG) is a combination of a foundational task group to establish the basis for new processes/technology and an educational task group updating guidelines on the established components of the platform. Because the technology discussed in this document has a relatively small user base compared to C-arm isocentric linacs, the authors chose to emphasize the educational components to assist medical physicists who are new to the technology and have not had the opportunity to receive in-depth vendor training at the time of reading this report. The TG has developed codes of practice, introduced QA, and developed guidelines which are generally expected to become enduring practice. This report makes prescriptive recommendations as there has not been enough longitudinal experience with some of the new technical components to develop a…

BACKGROUND: Accurate pancreas and pancreatic tumor segmentation from abdominal scans is crucial for diagnosing and treating pancreatic diseases. Automated and reliable segmentation algorithms are highly desirable in both clinical practice and research. PURPOSE: Segmenting the pancreas and tumors is challenging due to their low contrast, irregular morphologies, and variable anatomical locations. Additionally, the substantial difference in size between the pancreas and small tumors makes this task difficult. This paper proposes an attention-enhanced multiscale feature fusion network (AMFF-Net) to address these issues via 3D attention and multiscale context fusion methods. METHODS: First, to prevent missed segmentation of tumors, we design the residual depthwise attention modules (RDAMs) to extract global features by expanding receptive fields of shallow layers in the encoder. Second, hybrid transformer modules (HTMs) are proposed to model deep semantic features and suppress irrelevant regions while highlighting critical anatomical characteristics. Additionally, the multiscale feature…

BACKGROUND: Computed tomography (CT) is routinely used to guide cryoablation procedures. Notably, CT-guidance provides 3D localization of cryoprobes and can be used to delineate frozen tissue during ablation. However, metal-induced artifacts from ablation probes can make accurate probe placement challenging and degrade the ice ball conspicuity, which in combination could lead to undertreatment of potentially curable lesions. PURPOSE: In this work, we propose an image-based neural network (CNN) model for metal artifact reduction for CT-guided interventional procedures. METHODS: An image domain metal artifact simulation framework was developed and validated for deep-learning-based metal artifact reduction for interventional oncology (MARIO). CT scans were acquired for 19 different cryoablation probe configurations. The probe configurations varied in the number of probes and the relative orientations. A combination of intensity thresholding and masking based on maximum intensity projections (MIPs) was used to segment both the probes only and probes + artifact in each phantom image.…

Abstract Background Accurate, noninvasive, and reliable assessment of epidermal growth factor receptor (EGFR) mutation status and EGFR molecular subtypes is essential for treatment plan selection and individualized therapy in lung adenocarcinoma (LUAD). Radiomics models based on 18F‐FDG PET/CT have great potential in identifying EGFR mutation status and EGFR subtypes in patients with LUAD. The validation of multi‐center data, model visualization, and interpretation are significantly important for the management, application and trust of machine learning predictive models. However, few EGFR‐related research involved model visualization and interpretation, and multi‐center trial. Purpose To develop explainable optimal predictive models based on handcrafted radiomics features (HRFs) extracted from multi‐center 18F‐FDG PET/CT to predict EGFR mutation status and molecular subtypes in LUAD. Methods Baseline 18F‐FDG PET/CT images of 383 LUAD patients from three hospitals and one public data set were collected. Further, 1808 HRFs were extracted from the primary tumor regions using…

BACKGROUND: Self-supervised learning (SSL) is an approach to extract useful feature representations from unlabeled data, and enable fine-tuning on downstream tasks with limited labeled examples. Self-pretraining is a SSL approach that uses curated downstream task dataset for both pretraining and fine-tuning. Availability of large, diverse, and uncurated public medical image sets presents the opportunity to potentially create foundation models by applying SSL in the "wild" that are robust to imaging variations. However, the benefit of wild- versus self-pretraining has not been studied for medical image analysis. PURPOSE: Compare robustness of wild versus self-pretrained models created using convolutional neural network (CNN) and transformer (vision transformer [ViT] and hierarchical shifted window [Swin]) models for non-small cell lung cancer (NSCLC) segmentation from 3D computed tomography (CT) scans. METHODS: CNN, ViT, and Swin models were wild-pretrained using unlabeled 10,412 3D CTs sourced from the cancer imaging archive and internal datasets. Self-pretraining was applied to…

BACKGROUND: The evolution of coronary atherosclerotic heart disease (CAD) is intricately linked to alterations in the pericoronary adipose tissue (PCAT). In recent epochs, characteristics of the PCAT have progressively ascended as focal points of research in CAD risk stratification and individualized clinical decision-making. Harnessing radiomic methodologies allows for the meticulous extraction of imaging features from these adipose deposits. Coupled with machine learning paradigms, we endeavor to establish predictive models for the onset of major adverse cardiovascular events (MACE). PURPOSE: To appraise the predictive utility of radiomic features of PCAT derived from coronary computed tomography angiography (CCTA) in forecasting MACE. METHODS: We retrospectively incorporated data from 314 suspected or confirmed CAD patients admitted to our institution from June 2019 to December 2022. An additional cohort of 242 patients from two external institutions was encompassed for external validation. The endpoint under consideration was the occurrence of MACE after a 1-year follow-up. MACE…

BACKGROUND: Lung cancer is the most common type of cancer. Detection of lung cancer at an early stage can reduce mortality rates. Pulmonary nodules may represent early cancer and can be identified through computed tomography (CT) scans. Malignant risk can be estimated based on attributes like size, shape, location, and density. PURPOSE: Deep learning algorithms have achieved remarkable advancements in this domain compared to traditional machine learning methods. Nevertheless, many existing anchor-based deep learning algorithms exhibit sensitivity to predefined anchor-box configurations, necessitating manual adjustments to obtain optimal outcomes. Conversely, current anchor-free deep learning-based nodule detection methods normally adopt fixed-size nodule models like cubes or spheres. METHODS: To address these technical challenges, we propose a multiscale 3D anchor-free deep learning network (M3N) for pulmonary nodule detection, leveraging adjustable nodule modeling (ANM). Within this framework, ANM empowers the representation of target objects in an anisotropic manner, with a novel…

BACKGROUND: Spinal degeneration and vertebral compression fractures are common among the elderly that adversely affect their mobility, quality of life, lung function, and mortality. Assessment of vertebral fractures in chronic obstructive pulmonary disease (COPD) is important due to the high prevalence of osteoporosis and associated vertebral fractures in COPD. PURPOSE: We present new automated methods for (1) segmentation and labelling of individual vertebrae in chest computed tomography (CT) images using deep learning (DL), multi-parametric freeze-and-grow (FG) algorithm, and separation of apparently fused vertebrae using intensity autocorrelation and (2) vertebral deformity fracture detection using computed vertebral height features and parametric computational modelling of an established protocol outlined for trained human experts. METHODS: A chest CT-based automated method was developed for quantitative deformity fracture assessment following the protocol by Genant et al. The computational method was accomplished in the following steps: (1) computation of a voxel-level…

BACKGROUND: High-resolution brain positron emission tomography (PET) scanner is emerging as a significant and transformative non-invasive neuroimaging tool to advance neuroscience research as well as improve diagnosis and treatment in neurology and psychiatry. Time-of-flight (TOF) and depth-of-interaction (DOI) information provide markedly higher PET imaging performance by increasing image signal-to-noise ratio and mitigating spatial resolution degradation due to parallax error, respectively. PET detector modules that utilize light sharing can inherently carry DOI information from the multiple timestamps that are generated per gamma event. The difference between two timestamps that are triggered by scintillation photons traveling in opposite directions signifies the event's depth-dependent optical photon TOF (oTOF). However, light leak at the crystal-readout interface substantially degrades the resolution of this oTOF-based depth encoding. PURPOSE: We demonstrate the feasibility of oTOF-based depth encoding by mitigating light leak in single-ended-readout Prism-PET detector modules…

BACKGROUND: Preoperative microvascular invasion (MVI) of liver cancer is an effective method to reduce the recurrence rate of liver cancer. Hepatectomy with extended resection and additional adjuvant or targeted therapy can significantly improve the survival rate of MVI+ patients by eradicating micrometastasis. Preoperative prediction of MVI status is of great clinical significance for surgical decision-making and the selection of other adjuvant therapy strategies to improve the prognosis of patients. PURPOSE: Established a radiomics machine learning model based on multimodal MRI and clinical data, and analyzed the preoperative prediction value of this model for microvascular invasion (MVI) of hepatocellular carcinoma (HCC). METHOD: The preoperative liver MRI data and clinical information of 130 HCC patients who were pathologically confirmed to be pathologically confirmed were retrospectively studied. These patients were divided into MVI-positive group (MVI+) and MVI-negative group (MVI-) based on postoperative pathology. After a series of dimensionality reduction analysis, six…

BACKGROUND: Notwithstanding the encouraging results of previous studies reporting on the efficiency of deep learning (DL) in COVID-19 prognostication, clinical adoption of the developed methodology still needs to be improved. To overcome this limitation, we set out to predict the prognosis of a large multi-institutional cohort of patients with COVID-19 using a DL-based model. PURPOSE: This study aimed to evaluate the performance of deep privacy-preserving federated learning (DPFL) in predicting COVID-19 outcomes using chest CT images. METHODS: After applying inclusion and exclusion criteria, 3055 patients from 19 centers, including 1599 alive and 1456 deceased, were enrolled in this study. Data from all centers were split (randomly with stratification respective to each center and class) into a training/validation set (70%/10%) and a hold-out test set (20%). For the DL model, feature extraction was performed on 2D slices, and averaging was performed at the final layer to construct a 3D model for each scan. The DensNet model was used for feature extraction. The model was developed…

Abstract Background Clinical carbon ion beams offer the potential to overcome hypoxia‐induced radioresistance in pancreatic tumors, due to their high dose‐averaged Linear Energy Transfer (LETd), as previous studies have linked a minimum LETd within the tumor to improved local control. Current clinical practices at the Heidelberg Ion‐Beam Therapy Center (HIT), which use two posterior beams, do not fully exploit the LETd advantage of carbon ions, as the high LETd is primarily focused on the beams’ distal edges. Different LETd‐boosting strategies, such as Spot‐scanning Hadron Arc (SHArc), could enhance LETd distribution by concentrating high‐LETd values in potential hypoxic tumor cores while sparing organs at risk. Purpose This study aims to investigate and verify different LETd‐boosting strategies using an anthropomorphic pancreas phantom. Methods Various LETd‐boosting strategies were investigated for a cylindrical and a pancreas‐shaped target in an anthropomorphic pancreas phantom. Treatment plans were optimized using single field optimization (SFO) or multi field optimization (MFO),…

Abstract Background Ultra‐high dose rate radiotherapy (UHDR‐RT) has demonstrated normal tissue sparing capabilities, termed the FLASH effect; however, available dosimetry tools make it challenging to characterize the UHDR beams with sufficiently high concurrent spatial and temporal resolution. Novel dosimeters are needed for safe clinical implementation and improved understanding of the effect of UHDR‐RT. Purpose Ultra‐fast scintillation imaging has been shown to provide a unique tool for spatio‐temporal dosimetry of conventional cyclotron pencil beam scanning (PBS) deliveries, indicating the potential use for characterization of UHDR PBS proton beams. The goal of this work is to introduce this novel concept and demonstrate its capabilities in recording high‐resolution dose rate maps at FLASH‐capable proton beam currents, as compared to log‐based dose rate calculation, internally developed UHDR beam simulation, and a fast point detector (EDGE diode). Methods The light response of a scintillator sheet located at isocenter and irradiated by PBS proton fields (40–210 nA, 250 MeV) was…

Abstract Background Cardiac radioablation is a noninvasive stereotactic body radiation therapy (SBRT) technique to treat patients with refractory ventricular tachycardia (VT) by delivering a single high‐dose fraction to the VT isthmus. Cardiorespiratory motion induces position uncertainties resulting in decreased dose conformality. Electocardiograms (ECG) are typically used during cardiac MRI (CMR) to acquire images in a predefined cardiac phase, thus mitigating cardiac motion during image acquisition. Purpose We demonstrate real‐time cardiac physiology‐based radiotherapy beam gating within a preset cardiac phase on an MR‐linac. Methods MR images were acquired in healthy volunteers (n = 5, mean age = 29.6 years, mean heart‐rate (HR) = 56.2 bpm) on the 1.5 T Unity MR‐linac (Elekta AB, Stockholm, Sweden) after obtaining written informed consent. The images were acquired using a single‐slice balance steady‐state free precession (bSSFP) sequence in the coronal or sagittal plane (TR/TE = 3/1.48 ms, flip angle = 48, SENSE = 1.5, , voxel size = , partial Fourier factor = 0.65, frame rate =…