The 50 most-cited articles in Medical Physics, 2022

The clinical translation of FLASH radiotherapy (RT) requires challenges related to dosimetry and beam monitoring of ultra-high dose rate (UHDR) beams to be addressed. Detectors currently in use suffer from saturation effects under UHDR regimes, requiring the introduction of correction factors. There is significant interest from the scientific community to identify the most reliable solutions and suitable experimental approaches for UHDR dosimetry. This interest is manifested through the increasing number of national and international projects recently proposed concerning UHDR dosimetry. Attaining the desired solutions and approaches requires further optimization of already established technologies as well as the investigation of novel radiation detection and dosimetry methods. New knowledge will also emerge to fill the gap in terms of validated protocols, assessing new dosimetric procedures and standardized methods. In this paper, we discuss the main challenges coming from the peculiar beam parameters characterizing UHDR beams for FLASH RT. These challenges vary considerably…

The clinical use of surface imaging has increased dramatically, with demonstrated utility for initial patient positioning, real-time motion monitoring, and beam gating in a variety of anatomical sites. The Therapy Physics Subcommittee and the Imaging for Treatment Verification Working Group of the American Association of Physicists in Medicine commissioned Task Group 302 to review the current clinical uses of surface imaging and emerging clinical applications. The specific charge of this task group was to provide technical guidelines for clinical indications of use for general positioning, breast deep-inspiration breath hold treatment, and frameless stereotactic radiosurgery. Additionally, the task group was charged with providing commissioning and on-going quality assurance (QA) requirements for surface-guided radiation therapy (SGRT) as part of a comprehensive QA program including risk assessment. Workflow considerations for other anatomic sites and for computed tomography simulation, including motion management, are also discussed. Finally, developing clinical applications, such…

In their seminal paper from 2014, Fauvadon et al. coined the term FLASH irradiation to describe ultra-high-dose rate irradiation with dose rates greater than 40 Gy/s, which results in delivery times of fractions of a second. The experiments presented in that paper were performed with a high-dose-per-pulse 4.5 MeV electron beam, and the results served as the basis for the modern-day field of FLASH radiation therapy (RT). In this article, we review the studies that have been published after those early experiments, demonstrating the robust effects of FLASH RT on normal tissue sparing in preclinical models. We also outline the various irradiation parameters that have been used. Although the robustness of the biological response has been established, the mechanisms behind the FLASH effect are currently under investigation in a number of laboratories. However, differences in the magnitude of the FLASH effect between experiments in different labs have been reported. Reasons for these differences even within the same animal model are currently unknown, but likely has to do with the marked…

PURPOSE: Early detection and size quantification of renal calculi are important for optimizing treatment and preventing severe kidney stone disease. Prior work has shown that volumetric measurements of kidney stones are more informative and reproducible than linear measurements. Deep learning-based systems that use abdominal noncontrast computed tomography (CT) scans may assist in detection and reduce workload by removing the need for manual stone volume measurement. Prior to this work, no such system had been developed for use on noisy low-dose CT or tested on a large-scale external dataset. METHODS: We used a dataset of 91 CT colonography (CTC) scans with manually marked kidney stones combined with 89 CTC scans without kidney stones. To compare with a prior work half the data was used for training and half for testing. A set of CTC scans from 6185 patients from a separate institution with patient-level labels were used as an external validation set. A 3D U-Net model was employed to segment the kidneys, followed by gradient-based anisotropic denoising, thresholding, and region…

Dose uncertainty induced by respiratory motion remains a major concern for treating thoracic and abdominal lesions using particle beams. This Task Group report reviews the impact of tumor motion and dosimetric considerations in particle radiotherapy, current motion-management techniques, and limitations for different particle-beam delivery modes (i.e., passive scattering, uniform scanning, and pencil-beam scanning). Furthermore, the report provides guidance and risk analysis for quality assurance of the motion-management procedures to ensure consistency and accuracy, and discusses future development and emerging motion-management strategies. This report supplements previously published AAPM report TG76, and considers aspects of motion management that are crucial to the accurate and safe delivery of particle-beam therapy. To that end, this report produces general recommendations for commissioning and facility-specific dosimetric characterization, motion assessment, treatment planning, active and passive motion-management techniques, image guidance and related decision-making,…

PURPOSE: FLASH radiotherapy (RT) is an emerging technique in which beams with ultra-high dose rates (UH-DR) and dose per pulse (UH-DPP) are used. Commercially available active real-time dosimeters have been shown to be unsuitable in such conditions, due to severe response nonlinearities. In the present study, a novel diamond-based Schottky diode detector was specifically designed and realized to match the stringent requirements of FLASH-RT. METHODS: A systematic investigation of the main features affecting the diamond response in UH-DPP conditions was carried out. Several diamond Schottky diode detector prototypes with different layouts were produced at Rome Tor Vergata University in cooperation with PTW-Freiburg. Such devices were tested under electron UH-DPP beams. The linearity of the prototypes was investigated up to DPPs of about 26 Gy/pulse and dose rates of approximately 1 kGy/s. In addition, percentage depth dose (PDD) measurements were performed in different irradiation conditions. Radiochromic films were used for reference dosimetry. RESULTS: The response linearity of the…

Abstract Background Conventional air ionization chambers (ICs) exhibit ion recombination correction factors that deviate substantially from unity when irradiated with dose per pulse magnitudes higher than those used in conventional radiotherapy. This fact makes these devices unsuitable for the dosimetric characterization of beams in ultra‐high dose per pulse as used for FLASH radiotherapy. Purpose We present the design, development, and characterization of an ultra‐thin parallel plate IC that can be used in ultra‐high dose rate (UHDR) deliveries with minimal recombination. Methods The charge collection efficiency (CCE) of parallel plate ICs was modeled through a numerical solution of the coupled differential equations governing the transport of charged carriers produced by ionizing radiation. It was used to find out the optimal parameters for the purpose of designing an IC capable of exhibiting a linear response with dose (deviation less than 1%) up to 10 Gy per pulse at 4 s pulse duration. As a proof of concept, two vented parallel plate IC prototypes have been built and tested in…

Rapid advances in artificial intelligence (AI) and machine learning, and specifically in deep learning (DL) techniques, have enabled broad application of these methods in health care. The promise of the DL approach has spurred further interest in computer-aided diagnosis (CAD) development and applications using both "traditional" machine learning methods and newer DL-based methods. We use the term CAD-AI to refer to this expanded clinical decision support environment that uses traditional and DL-based AI methods. Numerous studies have been published to date on the development of machine learning tools for computer-aided, or AI-assisted, clinical tasks. However, most of these machine learning models are not ready for clinical deployment. It is of paramount importance to ensure that a clinical decision support tool undergoes proper training and rigorous validation of its generalizability and robustness before adoption for patient care in the clinic. To address these important issues, the American Association of Physicists in Medicine (AAPM) Computer-Aided Image Analysis Subcommittee…

The use of deep learning (DL) to improve cone-beam CT (CBCT) image quality has gained popularity as computational resources and algorithmic sophistication have advanced in tandem. CBCT imaging has the potential to facilitate online adaptive radiation therapy (ART) by utilizing up-to-date patient anatomy to modify treatment parameters before irradiation. Poor CBCT image quality has been an impediment to realizing ART due to the increased scatter conditions inherent to cone-beam acquisitions. Given the recent interest in DL applications in radiation oncology, and specifically DL for CBCT correction, we provide a systematic theoretical and literature review for future stakeholders. The review encompasses DL approaches for synthetic CT generation, as well as projection domain methods employed in the CBCT correction literature. We review trends pertaining to publications from January 2018 to April 2022 and condense their major findings-with emphasis on study design and DL techniques. Clinically relevant endpoints relating to image quality and dosimetric accuracy are summarized,…

BACKGROUND: Radiation treatment is considered an effective and the most common treatment option for prostate cancer. The treatment planning process requires accurate and precise segmentation of the prostate and organs at risk (OARs), which is laborious and time-consuming when contoured manually. Artificial intelligence (AI)-based auto-segmentation has the potential to significantly accelerate the radiation therapy treatment planning process; however, the accuracy of auto-segmentation needs to be validated before its full clinical adoption. PURPOSE: A commercial AI-based contouring model was trained to provide segmentation of the prostate and surrounding OARs. The segmented structures were input to a commercial auto-planning module for automated prostate treatment planning. This study comprehensively evaluates the performance of this contouring model in the automated prostate treatment planning process. METHODS AND MATERIALS: A 3D U-Net-based model (INTContour, Carina AI) was trained and validated on 84 computed tomography (CT) scans and tested on an additional 23 CT scans from…

PURPOSE: Consolidation immunotherapy after completion of chemoradiotherapy has become the standard of care for unresectable locally advanced non-small cell lung cancer and can induce potentially severe and life-threatening adverse events, including both immune checkpoint inhibitor-related pneumonitis (CIP) and radiation pneumonitis (RP), which are very challenging for radiologists to diagnose. Differentiating between CIP and RP has significant implications for clinical management such as the treatments for pneumonitis and the decision to continue or restart immunotherapy. The purpose of this study is to differentiate between CIP and RP by a CT radiomics approach. METHODS: We retrospectively collected the CT images and clinical information of patients with pneumonitis who received immune checkpoint inhibitor (ICI) only (n = 28), radiotherapy (RT) only (n = 31), and ICI+RT (n = 14). Three kinds of radiomic features (intensity histogram, gray-level co-occurrence matrix [GLCM] based, and bag-of-words [BoW] features) were extracted from CT images, which characterize tissue texture at…

While FLASH radiation therapy is inspiring enthusiasm to transform the field, it is neither new nor well understood with respect to the radiobiological mechanisms. As FLASH clinical trials are designed, it will be important to ensure we can deliver dose consistently and safely to every patient. Much like hyperthermia and proton therapy, FLASH is a promising new technology that will be complex to implement in the clinic and similarly will require customized credentialing for multi-institutional clinical trials. There is no doubt that FLASH seems promising, but many technologies that we take for granted in conventional radiation oncology, such as rigorous dosimetry, 3D treatment planning, volumetric image guidance, or motion management, may play a major role in defining how to use, or whether to use, FLASH radiotherapy. Given the extended time frame for patients to experience late effects, we recommend moving deliberately but cautiously forward toward clinical trials. In this paper, we review the state of quality assurance and safety systems in FLASH, identify critical pre-clinical…

Abstract Purpose Breast cancer is the most commonly occurring cancer worldwide. The ultrasound reflectivity imaging technique can be used to obtain breast ultrasound (BUS) images, which can be used to classify benign and malignant tumors. However, the classification is subjective and dependent on the experience and skill of operators and doctors. The automatic classification method can assist doctors and improve the objectivity, but current convolution neural network (CNN) is not good at learning global features and vision transformer (ViT) is not good at extraction local features. In this study, we proposed a visual geometry group attention ViT (VGGA‐ViT) network to overcome their disadvantages. Methods In the proposed method, we used a CNN module to extract the local features and employed a ViT module to learn the global relationship among different regions and enhance the relevant local features. The CNN module was named the VGGA module. It was composed of a VGG backbone, a feature extraction fully connected layer, and a squeeze‐and‐excitation block. Both the VGG backbone and the…

BACKGROUND: Ultrahigh dose-rate irradiation (FLASH-IR) was reported to be efficient in tumor control while reducing normal tissue radiotoxicity. However, the mechanism of such phenomenon is still unclear. Besides, the FLASH experiments using high energy X-ray, the most common modality in clinical radiotherapy, are rarely reported. This study aims to investigate the radiobiological response using 6 MV X-ray FLASH-IR or conventional dose-rate IR (CONV-IR). METHODS: scintillation detector were used to monitor the time structure and dose rate of FLASH pulses. BALB/c nude mice received whole abdominal 6 MV X-ray FLASH-IR or CONV-IR, the prescribed dose was 15 Gy or 10 Gy and the delivered absolute dose was monitored with EBT3 films. The mice were either euthanized 24 h post-IR to evaluate acute tissue responses or followed up for 6 weeks to observe late-stage responses and survival probability. Complete blood count, histological analyses, and measurement of cytokine expression and redox status were performed. RESULTS: Gy/s was reached in FLASH-IR at the center of mice body. After 6…

PURPOSE: The ARRONAX cyclotron facility offers the possibility to deliver proton beams from low to ultra-high dose rates (UHDR). As a good control of the dosimetry is a prerequisite of UHDR experimentations, we evaluated in different conditions the usability and the dose rate dependency of several radiochromic films commonly used for dosimetry in radiotherapy. METHODS: We compared the dose rate dependency of three types of radiochromic films: GAFchromic™ EBT3 and GAFchromic™ EBT-XD (Ashland Inc., Wayne, NJ, USA), and OrthoChromic OC-1 (OrthoChrome Inc., Hillsborough, NJ, USA), after proton irradiations at various mean dose rates (0.25, 40, 1500, and 7500 Gy/s) and for 10 doses (2-130 Gy). We also evaluated the dose rate dependency of each film considering beam structures, from single pulse to multiple pulses with various frequencies. RESULTS: EBT3 and EBT-XD films showed differences of response between conventional (0.25 Gy/s) and UHDR (7500 Gy/s) conditions, above 10 Gy. On the contrary, OC-1 films did not present overall difference of response for doses except below 3 Gy. We…

PURPOSE: The Oriatron eRT6 is a linear accelerator (linac) used in FLASH preclinical studies able to reach dose rates ranging from conventional (CONV) up to ultrahigh (UHDR). This work describes the implementation of commercially available beam current transformers (BCTs) as online monitoring tools compatible with CONV and UHDR irradiations for preclinical FLASH studies. METHODS: Two BCTs were used to measure the output of the Oriatron eRT6 linac. First, the correspondence between the set nominal beam parameters and those measured by the BCTs was checked. Then, we established the relationship between the total exit charge (measured by BCTs) and the absorbed dose to water. The influence of the pulse width (PW) and the pulse repetition frequency (PRF) at UHDR was characterized, as well as the short- and long-term stabilities of the relationship between the exit charge and the dose at CONV and UHDR. RESULTS: The BCTs were able to determine consistently the number of pulses, PW, and PRF. For fixed PW and pulse height, the exit charge measured from BCTs was correlated with the dose, and…

The increased radioresistence of healthy tissues when irradiated at very high dose rates (known as the Flash effect) is a radiobiological mechanism that is currently investigated to increase the therapeutic ratio of radiotherapy treatments. To maximize the benefits of the clinical application of Flash, a patient-specific balance between different properties of the dose distribution should be found, that is, Flash needs to be one of the variables considered in treatment planning. We investigated the Flash potential of three proton therapy planning and beam delivery techniques, each on a different anatomical region. Based on a set of beam delivery parameters, on hypotheses on the dose and dose rate thresholds needed for the Flash effect to occur, and on two definitions of Flash dose rate, we generated exemplary illustrations of the capabilities of current proton therapy equipment to generate Flash dose distributions. All techniques investigated could both produce dose distributions comparable with a conventional proton plan and reach the Flash regime, to an extent that was strongly…

PURPOSE: A diamond detector prototype was recently proposed by Marinelli et al. (Medical Physics 2022, https://doi.org/10.1002/mp.15473) for applications in ultrahigh-dose-per-pulse (UH-DPP) and ultrahigh-dose-rate (UH-DR) beams, as used in FLASH radiotherapy (FLASH-RT). In the present study, such so-called flashDiamond (fD) was investigated from the dosimetric point of view, under pulsed electron beam irradiation. It was then used for the commissioning of an ElectronFlash linac (SIT S.p.A., Italy) both in conventional and UH-DPP modalities. METHODS: Co and electron beam irradiation. Its response linearity was investigated in UH-DPP conditions. For this purpose, the DPP was varied in the 1.2-11.9 Gy range, by changing either the beam applicator or the pulse duration from 1 to 4 μs. Dosimetric validation of the fD detector prototype was then performed in conventional modality, by measuring percentage depth dose (PDD) curves, beam profiles, and output factors (OFs). All such measurements were carried out in a motorized water phantom. The obtained results were compared with the ones…

Transcranial ultrasound is more and more used for therapy and imaging of the brain. However, the skull is a highly attenuating and aberrating medium, with different structures and acoustic properties among samples and even within a sample. Thus, case-specific simulations are needed to perform transcranial focused ultrasound interventions safely. In this article, we provide a review of the different methods used to model the skull and to simulate ultrasound propagation through it.

PURPOSE: Brain metastases (BM) occur frequently in patients with metastatic cancer. Early and accurate detection of BM is essential for treatment planning and prognosis in radiation therapy. Due to their tiny sizes and relatively low contrast, small BM are very difficult to detect manually. With the recent development of deep learning technologies, several res earchers have reported promising results in automated brain metastasis detection. However, the detection sensitivity is still not high enough for tiny BM, and integration into clinical practice in regard to differentiating true metastases from false positives (FPs) is challenging. METHODS: The DeepMedic network with the binary cross-entropy (BCE) loss is used as our baseline method. To improve brain metastasis detection performance, a custom detection loss called volume-level sensitivity-specificity (VSS) is proposed, which rates metastasis detection sensitivity and specificity at a (sub)volume level. As sensitivity and precision are always a trade-off, either a high sensitivity or a high precision can be achieved for brain…

BACKGROUND: Online adaptive radiation therapy (RT) using hybrid magnetic resonance linear accelerators (MR-Linacs) can administer a tailored radiation dose at each treatment fraction. Daily MR imaging followed by organ and target segmentation adjustments allow to capture anatomical changes, improve target volume coverage, and reduce the risk of side effects. The introduction of automatic segmentation techniques could help to further improve the online adaptive workflow by shortening the re-contouring time and reducing intra- and inter-observer variability. In fractionated RT, prior knowledge, such as planning images and manual expert contours, is usually available before irradiation, but not used by current artificial intelligence-based autocontouring approaches. PURPOSE: The goal of this study was to train convolutional neural networks (CNNs) for automatic segmentation of bladder, rectum (organs at risk, OARs), and clinical target volume (CTV) for prostate cancer patients treated at 0.35 T MR-Linacs. Furthermore, we tested the CNNs generalization on data from independent facilities…

BACKGROUND: Recently, computed tomography (CT) manufacturers have developed deep-learning-based reconstruction algorithms to compensate for the limitations of iterative reconstruction (IR) algorithms, such as image smoothing and the spatial resolution's dependence on contrast and dose levels. PURPOSE: To assess the impact of an artificial intelligence deep-learning reconstruction (AI-DLR) algorithm on image quality and dose reduction compared with a hybrid IR algorithm in chest CT for different clinical indications. METHODS: levels 4 (i4) and 7 (i7)), and five levels of AI-DLR (Precise Image; Smoother, Smooth, Standard, Sharp, Sharper). Noise power spectrum (NPS), task-based transfer function, and detectability index (d') were computed: d'-modeled detection of a soft tissue mediastinal nodule (low-contrast soft tissue chest nodule within the mediastinum [LCN]), ground-glass opacity (GGO), or high-contrast pulmonary (HCP) lesion. The subjective image quality of chest anthropomorphic phantom images was independently evaluated by two radiologists. They assessed image noise, image…

PURPOSE: Experimental measurements of two-dimensional (2D) dose rate distributions in proton pencil beam scanning (PBS) FLASH radiation therapy (RT) are currently lacking. In this study, we characterize a newly designed 2D strip-segmented ionization chamber array (SICA) with high spatial and temporal resolution and demonstrate its applications in a modern proton PBS delivery system at both conventional and ultrahigh dose rates. METHODS: A dedicated research beamline of the Varian ProBeam system was employed to deliver a 250-MeV proton PBS beam with nozzle currents up to 215 nA. In the research and clinical beamlines, the spatial, temporal, and dosimetric performances of the SICA were characterized and compared with measurements using parallel-plate ion chambers (IBA PPC05 and PTW Advanced Markus chamber), a 2D scintillator camera (IBA Lynx), Gafchromic films (EBT-XD), and a Faraday cup. A novel reconstruction approach was proposed to enable the measurement of 2D dose and dose rate distributions using such a strip-type detector. RESULTS: > 0.99 for both dose and dose rate with nozzle…

PURPOSE: Magnetic resonance (MR) imaging is the gold standard in image-guided brachytherapy (IGBT) due to its superior soft-tissue contrast for target and organs-at-risk (OARs) delineation. Accurate and fast segmentation of MR images are very important for high-quality IGBT treatment planning. The purpose of this work is to implement and evaluate deep learning (DL) models for the automatic segmentation of targets and OARs in MR image-based high-dose-rate (HDR) brachytherapy for cervical cancer. METHODS: A 2D DL model using residual neural network architecture (ResNet50) was developed to contour the targets (gross tumor volume (GTV), high-risk clinical target volume (HR CTV), and intermediate-risk clinical target volume (IR CTV)) and OARs (bladder, rectum, sigmoid, and small intestine) automatically on axial MR slices of HDR brachytherapy patients. Furthermore, two additional 2D DL models using sagittal and coronal images were also developed. A 2.5D model was generated by combining the outputs from axial, sagittal, and coronal DL models. Similarly, a 2D and 2.5D DL models were also…

BACKGROUND: Low-intensity transcranial focused ultrasound (tFUS) has gained considerable attention as a promising noninvasive neuromodulatory technique for human brains. However, the complex morphology of the skull hinders scholars from precisely predicting the acoustic energy transmitted and the region of the brain impacted during the sonication. This is due to the fact that different ultrasound frequencies and skull morphology variations greatly affect wave propagation through the skull. PURPOSE: Although the acoustic properties of human skull have been studied for tFUS applications, such as tumor ablation using a multielement phased array, there is no consensus about how to choose a single-element focused ultrasound (FUS) transducer with a suitable frequency for neuromodulation. There are interests in exploring the magnitude and dimension of tFUS beam through human parietal bone for modulating specific brain lobes. Herein, we aim to investigate the wave propagation of tFUS on human skulls to understand and address the concerns above. METHODS: Both experimental measurements and…

BACKGROUND: Current clinical positron emission tomography (PET) systems utilize detectors where the scintillator typically contains single elements of 3-6-mm width and about 20-mm height. While providing good time-of-flight performance, this design limits the spatial resolution and causes radial astigmatism as the depth-of-interaction (DOI) remains unknown. PURPOSE: We propose an alternative, aiming to combine the advantages of current detectors with the DOI capabilities shown for monolithic concepts, based on semi-monolithic scintillators (slabs). Here, the optical photons spread along one dimension enabling DOI-encoding with a still small readout area beneficial for timing performance. METHODS: was read out by a 64-channel photosensor containing digital SiPMs (DPC3200-22-44, Philips Digital Photon Counting). The position estimation in the detector's monolithic and DOI direction was based on a calibration with a fan beam collimator and the machine learning technique gradient tree boosting (GTB). RESULTS: We achieved a positioning performance in terms of mean absolute error (MAE) of…

PURPOSE: In ultrahigh dose rate radiotherapy, the FLASH effect can lead to substantially reduced healthy tissue damage without affecting tumor control. Although many studies show promising results, the underlying biological mechanisms and the relevant delivery parameters are still largely unknown. It is unclear, particularly for scanned proton therapy, how treatment plans could be optimized to maximally exploit this protective FLASH effect. MATERIALS AND METHODS: To investigate the potential of pencil beam scanned proton therapy for FLASH treatments, we present a phenomenological model, which is purely based on experimentally observed phenomena such as potential dose rate and dose thresholds, and which estimates the biologically effective dose during FLASH radiotherapy based on several parameters. We applied this model to a wide variety of patient geometries and proton treatment planning scenarios, including transmission and Bragg peak plans as well as single- and multifield plans. Moreover, we performed a sensitivity analysis to estimate the importance of each model parameter.…

Multiparametric magnetic resonance imaging (mpMRI) is an indispensable tool in the clinical workflow for the diagnosis and treatment planning of various diseases. Machine learning-based artificial intelligence (AI) methods, especially those adopting the deep learning technique, have been extensively employed to perform mpMRI image classification, segmentation, registration, detection, reconstruction, and super-resolution. The current availabilities of increasing computational power and fast-improving AI algorithms have empowered numerous computer-based systems for applying mpMRI to disease diagnosis, imaging-guided radiotherapy, patient risk and overall survival time prediction, and the development of advanced quantitative imaging technology for magnetic resonance fingerprinting. However, the wide application of these developed systems in the clinic is still limited by a number of factors, including robustness, reliability, and interpretability. This survey aims to provide an overview for new researchers in the field as well as radiologists with the hope that they can understand the…

Abstract Background Proton arc technology has recently shown dosimetric gains for various treatment indications. The increased number of beams and energy layers (ELs) in proton arc plans, increases the degrees of freedom in plan optimization and thereby flexibility to spare dose in organs at risk (OARs). A relationship exists between dosimetric plan quality, delivery efficiency, the number of ELs ‐and beams in a proton arc plan. Purpose This work aims to investigate the effect of the number of beams and ELs in a proton arc plan, on toxicity and delivery time for oropharyngeal cancer patients (OPC) selected for intensity modulated proton therapy (IMPT) based on the Dutch model‐based approach. Methods The EL reduction algorithm iteratively selects ELs from beams equidistantly spaced over a 360° arc. The beams in the final plan may contain multiple ELs, making them suited for static delivery on the studied treatment machine. The produced plans can therefore be called “step and shoot” proton arc plans. The number of beams and ELs were varied to determine the relationship with the…

PURPOSE: Although flash radiation therapy (FLASH-RT) is a promising novel technique that has the potential to achieve a better therapeutic ratio between tumor control and normal tissue complications, the ultrahigh pulsed dose rates (UHPDR) mean that experimental dosimetry is very challenging. There is a need for real-time dosimeters in the development and implementation of FLASH-RT. In this work, we characterize a novel plastic scintillator capable of temporal resolution short enough (2.5 ms) to resolve individual pulses. METHODS: We characterized a novel plastic dosimeter for use in a linac converter to deliver 16-MeV electrons at 100-Gy/s UHPDR average dose rates. The linearity and reproducibility were established by comparing relative measurements with a pinpoint ionization chamber placed at 10-cm water-equivalent depth where the electrometer is not saturated by the high dose per pulse. The accuracy was established by comparing the plastic scintillator dose measurements with EBT-XD Gafchromic radiochromic films, the current reference dosimeter for UHPDR. Finally, the plastic…

Higher dose rates, a trend for radiotherapy machines, can be beneficial in shortening treatment times for radiosurgery and mitigating the effects of motion. Recently, even higher doses (e.g., 100 times greater) have become targeted because of their potential to generate the FLASH effect (FE). We refer to these physical dose rates as ultra-high (UHDR). The complete relationship between UHDR and the FE is unknown. But UHDR systems are needed to explore the relationship further and to deliver clinical UHDR treatments, where indicated. Despite the challenging set of unknowns, the authors seek to make reasonable assumptions to probe how existing and developing technology can address the UHDR conditions needed to provide beam generation capable of producing the FE in preclinical and clinical applications. As a preface, this paper discusses the known and unknown relationships between UHDR and the FE. Based on these, different accelerator and ionizing radiation types are then discussed regarding the relevant UHDR needs. The details of UHDR beam production are discussed for existing and…

It is known that changes in the mechanical properties of tissues are associated with the onset and progression of certain diseases. Ultrasound elastography is a technique to characterize tissue stiffness using ultrasound imaging either by measuring tissue strain using quasi-static elastography or natural organ pulsation elastography, or by tracing a propagated shear wave induced by a source or a natural vibration using dynamic elastography. In recent years, deep learning has begun to emerge in ultrasound elastography research. In this review, several common deep learning frameworks in the computer vision community, such as multilayered perceptron, convolutional neural network, and recurrent neural network, are described. Then, recent advances in ultrasound elastography using such deep learning techniques are revisited in terms of algorithm development and clinical diagnosis. Finally, the current challenges and future developments of deep learning in ultrasound elastography are prospected.

Abstract Purpose To provide ultrahigh dose rate (UHDR) pencil beam scanning (PBS) proton dosimetry comparison of clinically used plane‐parallel ion chambers, PTW (Physikalisch‐Technische Werkstaetten) Advanced Markus and IBA (Ion Beam Application) PPC05, with a proton graphite calorimeter in a support of first in‐human proton FLASH clinical trial. Methods Absolute dose measurement intercomparison of the plane‐parallel plate ion chambers and the proton graphite calorimeter was performed at 5‐cm water‐equivalent depth using rectangular 250‐MeV single‐layer treatment plans designed for the first in‐human FLASH clinical trial. The dose rate for each field was designed to remain above 60 Gy/s. The ion recombination effects of the plane‐parallel plate ion chambers at various bias voltages were also investigated in the range of dose rates between 5 and 60 Gy/s. Two independent model‐based extrapolation methods were used to calculate the ion recombination correction factors ks to compare with the two‐voltage technique from most widely used clinical protocols. Results The mean measured dose…

BACKGROUND: Time-resolved 4D cone beam-computed tomography (4D-CBCT) allows a daily assessment of patient anatomy and respiratory motion. However, 4D-CBCTs suffer from imaging artifacts that affect the CT number accuracy and prevent accurate proton dose calculations. Deep learning can be used to correct CT numbers and generate synthetic CTs (sCTs) that can enable CBCT-based proton dose calculations. PURPOSE: In this work, sparse view 4D-CBCTs were converted into 4D-sCT utilizing a deep convolutional neural network (DCNN). 4D-sCTs were evaluated in terms of image quality and dosimetric accuracy to determine if accurate proton dose calculations for adaptive proton therapy workflows of lung cancer patients are feasible. METHODS: A dataset of 45 thoracic cancer patients was utilized to train and evaluate a DCNN to generate 4D-sCTs, based on sparse view 4D-CBCTs reconstructed from projections acquired with a 3D acquisition protocol. Mean absolute error (MAE) and mean error were used as metrics to evaluate the image quality of single phases and average 4D-sCTs against 4D-CTs acquired on…

Since the publication of AAPM Task Group (TG) 148 on quality assurance (QA) for helical tomotherapy, there have been many new developments on the tomotherapy platform involving treatment delivery, on-board imaging options, motion management, and treatment planning systems (TPSs). In response to a need for guidance on quality control (QC) and QA for these technologies, the AAPM Therapy Physics Committee commissioned TG 306 to review these changes and make recommendations related to these technology updates. The specific objectives of this TG were (1) to update, as needed, recommendations on tolerance limits, frequencies and QC/QA testing methodology in TG 148, (2) address the commissioning and necessary QA checks, as a supplement to Medical Physics Practice Guidelines (MPPG) with respect to tomotherapy TPS and (3) to provide risk-based recommendations on the new technology implemented clinically and treatment delivery workflow. Detailed recommendations on QA tests and their tolerance levels are provided for dynamic jaws, binary multileaf collimators, and Synchrony motion management.…

Abstract Purpose The transmission proton FLASH technique delivers high doses to the normal tissue distal to the target, which is less conformal compared to the Bragg peak technique. To investigate FLASH radiotherapy (RT) planning using single‐energy Bragg peak beams with a similar beam arrangement as clinical intensity‐modulated proton therapy (IMPT) in a liver stereotactic body radiation therapy (SBRT) and to characterize the plan quality, dose sparing of organs‐at‐risk (OARs), and FLASH dose rate percentage. Materials and methods An in‐house platform was developed to enable inverse IMPT‐FLASH planning using single‐energy Bragg peaks. A universal range shifter and range compensators were utilized to effectively align the Bragg peak to the distal edge of the target. Two different minimum MU settings of 400 and 800 MU/spot (Bragg‐400 MU and Bragg‐800 MU) plans were investigated on 10 consecutive hepatocellular carcinoma patients previously treated by IMPT–SBRT to evaluate the FLASH dose and dose rate coverage for OARs. The IMPT–FLASH using single‐energy Bragg peaks delivered 50 Gy in…

PURPOSE: With the rising number of computed tomography (CT) examinations and the trend toward personalized medicine, patient-specific dose estimates are becoming more and more important in CT imaging. However, current approaches are often too slow or too inaccurate to be applied routinely. Therefore, we propose the so-called deep dose estimation (DDE) to provide highly accurate patient dose distributions in real time METHODS: To combine accuracy and computational performance, the DDE algorithm uses a deep convolutional neural network to predict patient dose distributions. To do so, a U-net like architecture is trained to reproduce Monte Carlo simulations from a two-channel input consisting of a CT reconstruction and a first-order dose estimate. Here, the corresponding training data were generated using CT simulations based on 45 whole-body patient scans. For each patient, simulations were performed for different anatomies (pelvis, abdomen, thorax, head), different tube voltages (80 kV, 100 kV, 120 kV), different scan trajectories (circle, spiral), and with and without bowtie…

BACKGROUND: A considerable proportion of metastatic brain tumors progress locally despite stereotactic radiation treatment, and it can take months before such local progression is evident on follow-up imaging. Prediction of radiotherapy outcome in terms of tumor local failure is crucial for these patients and can facilitate treatment adjustments or allow for early salvage therapies. PURPOSE: In this work, a novel deep learning architecture is introduced to predict the outcome of local control/failure in brain metastasis treated with stereotactic radiation therapy using treatment-planning magnetic resonance imaging (MRI) and standard clinical attributes. METHODS: At the core of the proposed architecture is an InceptionResentV2 network to extract distinct features from each MRI slice for local outcome prediction. A recurrent or transformer network is integrated into the architecture to incorporate spatial dependencies between MRI slices into the predictive modeling. A visualization method based on prediction difference analysis is coupled with the deep learning model to illustrate how…

Abstract Purpose This paper focused on the design and optimization of ridge filter–based intensity‐modulated proton therapy (IMPT), and its potential applications for FLASH. Differing from the standard pencil beam scanning (PBS) mode, no energy/layer switching is required and total treatment time can be shortened. Methods Unique dose‐influence matrices were generated as a proton beam traverses through slabs of different thicknesses (i.e., modulation by different layers). To establish the references for comparison, conventional IMPT plans (single field) were created using a large‐scale nonlinear solver. The spot weights from the reference IMPT plans were used as inputs for optimizing the design of ridge filters. Two designs were evaluated: model A (static) and model B (dynamic). The ridge filter designs were first verified (by GEANT4 simulation) in a water phantom and then in an H&N case. A direct comparison was made between the GEANT4 simulation results of two models and their respective references, with regard to plan quality, dose‐averaged dose rate, and total treatment time.…

PURPOSE: To develop a deep learning model design that integrates radiomics analysis for enhanced performance of COVID-19 and non-COVID-19 pneumonia detection using chest x-ray images. METHODS: As a novel radiomics approach, a 2D sliding kernel was implemented to map the impulse response of radiomic features throughout the entire chest x-ray image; thus, each feature is rendered as a 2D map in the same dimension as the x-ray image. Based on each of the three investigated deep neural network architectures, including VGG-16, VGG-19, and DenseNet-121, a pilot model was trained using x-ray images only. Subsequently, two radiomic feature maps (RFMs) were selected based on cross-correlation analysis in reference to the pilot model saliency map results. The radiomics-boosted model was then trained based on the same deep neural network architecture using x-ray images plus the selected RFMs as input. The proposed radiomics-boosted design was developed using 812 chest x-ray images with 262/288/262 COVID-19/non-COVID-19 pneumonia/healthy cases, and 649/163 cases were assigned as…

BACKGROUND: Early diagnosis of lung cancer is a key intervention for the treatment of lung cancer in which computer-aided diagnosis (CAD) can play a crucial role. Most published CAD methods perform lung cancer diagnosis by classifying each lung nodule in isolation. However, this does not reflect clinical practice, where clinicians diagnose a patient based on a set of images of nodules, instead of looking at one nodule at a time. Besides, the low interpretability of the output provided by these methods presents an important barrier for their adoption. METHOD: In this article, we treat lung cancer diagnosis as a multiple instance learning (MIL) problem, which better reflects the diagnosis process in the clinical setting and provides higher interpretability of the output. We selected radiomics as the source of input features and deep attention-based MIL as the classification algorithm. The attention mechanism provides higher interpretability by estimating the importance of each instance in the set for the final diagnosis. To improve the model's performance in a small imbalanced…

PURPOSE: Key factors in FLASH treatments are the ultra-high dose rate (UHDR) and the time structure of the beam delivery. Measurement of the time structure in pencil beam scanning (PBS) proton FLASH treatments is challenging for many types of detectors since high temporal resolution is needed. In this study, a fast scintillator detector system was developed and used to measure the individual spot durations as well as the time when the beam moves between two positions (transition duration) during PBS proton FLASH and UHDR treatments. The spot durations were compared with machine log-file recordings. METHODS: A detector system based on inorganic scintillating crystals was developed. The system consisted of four detector probes made of a sub-millimeter ZnSe:O crystal that was coupled via an optical fiber to an optical reader with 50 kHz sampling rate. The detector system was used in two experiments, both performed with a PBS proton beam with 250 MeV beam energy and 215 nA requested nozzle beam current. The sampling rate enabled multiple measurements during each spot delivery and during…

PURPOSE: Organ autosegmentation efforts to date have largely been focused on adult populations, due to limited availability of pediatric training data. Pediatric patients may present additional challenges for organ segmentation. This paper describes a dataset of 359 pediatric chest-abdomen-pelvis and abdomen-pelvis Computed Tomography (CT) images with expert contours of up to 29 anatomical organ structures to aid in the evaluation and development of autosegmentation algorithms for pediatric CT imaging. ACQUISITION AND VALIDATION METHODS: The dataset collection consists of axial CT images in Digital Imaging and Communications in Medicine (DICOM) format of 180 male and 179 female pediatric chest-abdomen-pelvis or abdomen-pelvis exams acquired from one of three CT scanners at Children's Wisconsin. The datasets represent random pediatric cases based upon routine clinical indications. Subjects ranged in age from 5 days to 16 years, with a mean age of 7 years. The CT acquisition, contrast, and reconstruction protocols varied across the scanner models and patients, with specifications…

BACKGROUND: Intra-scan rigid-body motion is a costly and ubiquitous problem in clinical magnetic resonance imaging (MRI) of the head. PURPOSE: State-of-the-art methods for retrospective motion correction in MRI are often computationally expensive or in the case of image-to-image deep learning (DL) based methods can be prone to undesired alterations of the image (hallucinations'). In this work we introduce a novel rigid-body motion correction method which combines the advantages of classical model-driven and data-consistency (DC) preserving approaches with a novel DL algorithm, to provide fast and robust retrospective motion correction. METHODS: The proposed Motion Parameter Estimating Densenet (MoPED) retrospectively estimates subject head motion during MRI acquisitions using a DL network with DenseBlocks and multitask learning. It quantifies the 2D rigid in-plane motion parameters slice-wise for each echo train (ET) of a Cartesian T2-weighted 2D Turbo-Spin-Echo sequence. The network receives a center patch of the motion corrupted k-space as well as an additional motion-free…

BACKGROUND: In recent years, low-dose computed tomography (LDCT) has played an important role in the diagnosis CT to reduce the potential adverse effects of X-ray radiation on patients, while maintaining the same diagnostic image quality. PURPOSE: Deep learning (DL)-based methods have played an increasingly important role in the field of LDCT imaging. However, its performance is highly dependent on the consistency of feature distributions between training data and test data. Due to patient's breathing movements during data acquisition, the paired LDCT and normal dose CT images are difficult to obtain from realistic imaging scenarios. Moreover, LDCT images from simulation or clinical CT examination often have different feature distributions due to the pollution by different amounts and types of image noises. If a network model trained with a simulated dataset is used to directly test clinical patients' LDCT data, its denoising performance may be degraded. Based on this, we propose a novel domain-adaptive denoising network (DADN) via noise estimation and transfer learning to resolve…

PURPOSE: We aimed to develop a noninvasive artificial intelligence (AI) model to diagnose signet-ring cell carcinoma (SRCC) of gastric cancer (GC) and identify patients with SRCC who could benefit from postoperative chemotherapy based on preoperative contrast-enhanced computed tomography (CT). METHODS: A total of 855 GC patients with 855 single GCs were included, of which 249 patients were diagnosed as SRCC by histopathologic examinations. The AI model was generated with clinical, handcrafted radiomic, and deep learning features. Model diagnostic performance was measured by area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, while predictive performance was measured by Kaplan-Meier curves. RESULTS: In the test cohort (n = 257), the AUC, sensitivity, and specificity of our AI model for diagnosing SRCC were 0.786 (95% CI: 0.721-0.845), 77.3%, and 69.2%, respectively. For the entire cohort, patients with AI-predicted high risk had a significantly shorter median OS compared with those with low risk (median overall survival [OS], 38.8 vs. 64.2…

PURPOSE: Proton minibeam radiation therapy (pMBRT) is a new radiotherapy approach that has shown a significant increase in the therapeutic window in glioma-bearing rats compared to conventional proton therapy. The dosimetry of pMBRT is challenging and error prone due to the submillimetric beamlet sizes used. The aim of this study was to perform a robustness analysis on the setup parameters utilized in current preclinical trials and provide guidelines for reproducible dosimetry. The results of this work are intended to guide upcoming implementations of pMBRT worldwide, as well as pave the way for future clinical implementations. METHODS: Monte Carlo simulations and experimental data were used to evaluate the impact of variations in setup parameters and uncertainties in collimator specifications on lateral pMBRT dose distributions. The value of each parameter was modified individually to evaluate their effect on dose distributions. Experimental dosimetry was performed by means of high-resolution detectors, that is, radiochromic films, the IBA Razor and the Microdiamond detector. New…

BACKGROUND: Manual contouring is very labor-intensive, time-consuming, and subject to intra- and inter-observer variability. An automated deep learning approach to fast and accurate contouring and segmentation is desirable during radiotherapy treatment planning. PURPOSE: This work investigates an efficient deep-learning-based segmentation algorithm in abdomen computed tomography (CT) to facilitate radiation treatment planning. METHODS: In this work, we propose a novel deep-learning model utilizing U-shaped multi-layer perceptron mixer (MLP-Mixer) and convolutional neural network (CNN) for multi-organ segmentation in abdomen CT images. The proposed model has a similar structure to V-net, while a proposed MLP-Convolutional block replaces each convolutional block. The MLP-Convolutional block consists of three components: an early convolutional block for local features extraction and feature resampling, a token-based MLP-Mixer layer for capturing global features with high efficiency, and a token projector for pixel-level detail recovery. We evaluate our proposed network using: (1) an…

PURPOSE: Spot-scanning arc therapy (SPArc) is an emerging proton modality that can potentially offer a combination of advantages in plan quality and delivery efficiency, compared with traditional IMPT of a few beam angles. Unlike IMPT, frequent low-to-high energy layer switching (so called switch-up (SU)) can degrade delivery efficiency for SPArc. However, it is a tradeoff between the minimization of SU times and the optimization of plan quality. This work will consider the energy layer optimization (ELO) problem for SPArc and develop a new ELO method via energy matrix (EM) regularization to improve plan quality and delivery efficiency. METHODS: The major innovation of EM method for ELO is to design an EM that encourages desirable energy-layer map with minimal SU during SPArc, and then incorporate this EM into the SPArc treatment planning to simultaneously minimize the number of SU and optimize plan quality. The EM method is solved by the fast iterative shrinkage-thresholding algorithm and validated in comparison with a state-of-the-art method, so-called energy sequencing (ES).…

BACKGROUND: The number of patients who suffer from glioma has been increasing, and this malignancy is a serious threat to human health. The mainstream treatment for glioma is surgical resection; therefore, accurate resection can improve postoperative patient recovery. PURPOSE: Many studies have investigated surgical navigation guided by mixed reality, with good outcomes. However, the limitations of mixed reality, such as spatial drift caused by environmental changes, limit its clinical application. Therefore, we present a mixed reality surgical navigation system for glioma resection. Preoperative information can be fused precisely with the real patient with the spatial compensation method to achieve clinically suitable accuracy. METHODS: A head-mounted device was used to display virtual information, and a markerless spatial registration method was applied to precisely align the virtual anatomy with the real patient preoperatively. High-accuracy preoperative and intraoperative movement and spatial drift compensation methods were used to increase the positional accuracy of the mixed…