The 50 most-cited articles in Medical Physics, 2021

PURPOSE: This paper is aimed at investigating the feasibility of developing a personal dosimeter of cumulative radiation dose which would incorporate the following features: 1) a small size compared to that of a proximity ID card; 2) instant dose readout; 3) no power source; 4) moderate cost. The dosimeter is proposed as a potential replacement for TLD and OSL dosimeters used by nuclear industry workers and some medical staff groups. METHODS: An original detector design is developed containing a two-color LED, two photodetectors located in one plane covered with a mirror coating. The power necessary for the operation comes from an RFID reader. A small (5x5 mm) piece of Gafchromic EBT3 photochromic film sensitive to both X-ray and gamma radiation is used as a sensor. Irradiation of samples under X-ray and gamma radiation is carried out in the dose range of 0.1 cGy-1 Gy. The transmittance spectra are measured in the 300 nm-1100 nm spectral range. RESULTS: Several prototypes of the dosimeter are presented, the distinctive features of which are the absence of the power source, easy…

Purpose SpekPy is a free toolkit for modeling x‐ray tube spectra with the Python programming language. In this article, the advances in version 2.0 (v2) of the software are described, including additional target materials and more accurate modeling of the heel effect. Use of the toolkit is also demonstrated. Methods The predictions of SpekPy are illustrated in comparison to experimentally determined spectra: three radiation quality reference (RQR) series tungsten spectra and one mammography spectrum with a molybdenum target. The capability of the software to correctly model changes in tube output with tube potential is also assessed, using the example of a GE RevolutionTM CT scanner (GE Healthcare, Waukesha, WI, USA) and specifications in the system's Technical Reference Manual. Furthermore, we note that there are several physics models available in SpekPy. These are compared on and off the central axis, to illustrate the differences. Results SpekPy agrees closely with the experimental spectra over a wide range of tube potentials, both visually and in terms of first and second…

Recently,deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: (i) to replace computed tomography in magnetic resonance (MR) based treatment planning, (ii) facilitate cone-beam computed tomography based image-guided adaptive radiotherapy, and (iii) derive attenuation maps for the correction of positron emission tomography. Appropriate database searching was performed on journal articles published between January 2014 and December 2020. The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarizing the achievements. Lastly, the statistics of all the cited works from various aspects were analyzed, revealing…

Radiation exposures at ultrahigh dose rates (UHDRs) at several orders of magnitude greater than in current clinical radiotherapy (RT) have been shown to manifest differential radiobiological responses compared to conventional (CONV) dose rates. This has led to studies investigating the application of UHDR for therapeutic advantage (FLASH-RT) that have gained significant interest since the initial discovery in 2014 that demonstrated reduced lung toxicity with equivalent levels of tumor control compared with conventional dose-rate RT. Many subsequent studies have demonstrated the potential protective role of FLASH-RT in normal tissues, yet the underlying molecular and cellular mechanisms of the FLASH effect remain to be fully elucidated. Here, we summarize the current evidence of the FLASH effect and review FLASH-RT studies performed in preclinical models of normal tissue response. To critically examine the underlying biological mechanisms of responses to UHDR radiation exposures, we evaluate in vitro studies performed with normal and tumor cells. Differential responses to UHDR versus…

The charges on this task group (TG) were as follows: (a) provide specific procedural guidelines for performing the tests recommended in TG 142; (b) provide estimate of the range of time, appropriate personnel, and qualifications necessary to complete the tests in TG 142; and (c) provide sample daily, weekly, monthly, or annual quality assurance (QA) forms. Many of the guidelines in this report are drawn from the literature and are included in the references. When literature was not available, specific test methods reflect the experiences of the TG members (e.g., a test method for door interlock is self-evident with no literature necessary). In other cases, the technology is so new that no literature for test methods was available. Given broad clinical adaptation of volumetric modulated arc therapy (VMAT), which is not a specific topic of TG 142, several tests and criteria specific to VMAT were added.

PURPOSE: The response to radiation typically used in radiotherapy treatments has been experimentally evaluated for three samples of two phototransistors (BPW85B and OP505A) and two PIN photodiodes types (VTB8440BH and BPW34S). METHODS: To that end, a staggered irradiation cycle has been applied which included dose rate values from 0.81 to 4.87 cGy/s, achieving a total absorbed dose of 21.4 Gy. The samples have been irradiated with a linear accelerator and the relations between the induced photocurrent and the average and instantaneous dose rates, and between the accumulated charge and the absorbed dose, have been determined. The radiation-induced output currents were measured by means of an external interface of the devices to a previously designed readout unit. RESULTS: Experimental results of Si PIN photodiode BPW34S have shown a sensitivity of (13.9 ± 0.5) nC/cGy, slight sensitivity dependence on dose rate, and a high linearity of the current with the average and instantaneous dose rate, requiring only 10 V of reverse bias voltage. This device thermal drift has characterized and…

BACKGROUND: In the domain of natural language processing, Transformers are recognized as state-of-the-art models, which opposing to typical convolutional neural networks (CNNs) do not rely on convolution layers. Instead, Transformers employ multi-head attention mechanisms as the main building block to capture long-range contextual relations between image pixels. Recently, CNNs dominated the deep learning solutions for diabetic retinopathy grade recognition. However, spurred by the advantages of Transformers, we propose a Transformer-based method that is appropriate for recognizing the grade of diabetic retinopathy. PURPOSE: The purposes of this work are to demonstrate that (i) the pure attention mechanism is suitable for diabetic retinopathy grade recognition and (ii) Transformers can replace traditional CNNs for diabetic retinopathy grade recognition. METHODS: This paper proposes a Vision Transformer-based method to recognize the grade of diabetic retinopathy. Fundus images are subdivided into non-overlapping patches, which are then converted into sequences by flattening, and…

Small-field dosimetry used in advance treatment technologies poses challenges due to loss of lateral charged particle equilibrium (LCPE), occlusion of the primary photon source, and the limited choice of suitable radiation detectors. These challenges greatly influence dosimetric accuracy. Many high-profile radiation incidents have demonstrated a poor understanding of appropriate methodology for small-field dosimetry. These incidents are a cause for concern because the use of small fields in various specialized radiation treatment techniques continues to grow rapidly. Reference and relative dosimetry in small and composite fields are the subject of the International Atomic Energy Agency (IAEA) dosimetry code of practice that has been published as TRS-483 and an AAPM summary publication (IAEA TRS 483; Dosimetry of small static fields used in external beam radiotherapy: An IAEA/AAPM International Code of Practice for reference and relative dose determination, Technical Report Series No. 483; Palmans et al., Med Phys 45(11):e1123, 2018). The charge of AAPM task group 155 (TG-155) is to…

Purpose To advance fair and consistent comparisons of dose prediction methods for knowledge‐based planning (KBP) in radiation therapy research. Methods We hosted OpenKBP, a 2020 AAPM Grand Challenge, and challenged participants to develop the best method for predicting the dose of contoured computed tomography (CT) images. The models were evaluated according to two separate scores: (a) dose score, which evaluates the full three‐dimensional (3D) dose distributions, and (b) dose‐volume histogram (DVH) score, which evaluates a set DVH metrics. We used these scores to quantify the quality of the models based on their out‐of‐sample predictions. To develop and test their models, participants were given the data of 340 patients who were treated for head‐and‐neck cancer with radiation therapy. The data were partitioned into training ( ), validation ( ), and testing ( ) datasets. All participants performed training and validation with the corresponding datasets during the first (validation) phase of the Challenge. In the second (testing) phase, the participants used their model on the…

The use of dedicated magnetic resonance simulation (MR-SIM) platforms in Radiation Oncology has expanded rapidly, introducing new equipment and functionality with the overall goal of improving the accuracy of radiation treatment planning. However, this emerging technology presents a new set of challenges that need to be addressed for safe and effective MR-SIM implementation. The major objectives of this report are to provide recommendations for commercially available MR simulators, including initial equipment selection, siting, acceptance testing, quality assurance, optimization of dedicated radiation therapy specific MR-SIM workflows, patient-specific considerations, safety, and staffing. Major contributions include guidance on motion and distortion management as well as MRI coil configurations to accommodate patients immobilized in the treatment position. Examples of optimized protocols and checklists for QA programs are provided. While the recommendations provided here are minimum requirements, emerging areas and unmet needs are also highlighted for future development.

Independent verification of the dose per monitor unit (MU) to deliver the prescribed dose to a patient has been a mainstay of radiation oncology quality assurance (QA). We discuss the role of secondary dose/MU calculation programs as part of a comprehensive QA program. This report provides guidelines on calculation-based dose/MU verification for intensity modulated radiation therapy (IMRT) or volumetric modulated arc therapy (VMAT) provided by various modalities. We provide a review of various algorithms for "independent/second check" of monitor unit calculations for IMRT/VMAT. The report makes recommendations on the clinical implementation of secondary dose/MU calculation programs; on commissioning and acceptance of various commercially available secondary dose/MU calculation programs; on benchmark QA and periodic QA; and on clinically reasonable action levels for agreement of secondary dose/MU calculation programs.

The development of medical imaging artificial intelligence (AI) systems for evaluating COVID-19 patients has demonstrated potential for improving clinical decision making and assessing patient outcomes during the recent COVID-19 pandemic. These have been applied to many medical imaging tasks, including disease diagnosis and patient prognosis, as well as augmented other clinical measurements to better inform treatment decisions. Because these systems are used in life-or-death decisions, clinical implementation relies on user trust in the AI output. This has caused many developers to utilize explainability techniques in an attempt to help a user understand when an AI algorithm is likely to succeed as well as which cases may be problematic for automatic assessment, thus increasing the potential for rapid clinical translation. AI application to COVID-19 has been marred with controversy recently. This review discusses several aspects of explainable and interpretable AI as it pertains to the evaluation of COVID-19 disease and it can restore trust in AI application to this disease. This…

FLASH radiotherapy is considered a new potential breakthrough in cancer treatment. Ultra-high dose rates (>40 Gy/s) have been shown to reduce toxicity in the normal tissue without compromising tumor control, resulting in a widened therapeutic window. These high dose rates are more easily achievable in the clinic with charged particles, and clinical trials are, indeed, ongoing using electrons or protons. FLASH could be an attractive solution also for heavier ions such as carbon and could even enhance the therapeutic window. However, it is not yet known whether the FLASH effect will be the same as for sparsely ionizing radiation when densely ionizing carbons ions are used. Here we discuss the technical challenges in beam delivery and present a promising solution using 3D range-modulators in order to apply ultra-high dose rates (UHDR) compatible with FLASH with carbon ions. Furthermore, we will discuss the possible outcome of C-ion therapy at UHDR on the level of the radiobiological and radiation chemical effects.

PURPOSE: To present the acceptance and the commissioning, to define the reference dose, and to prepare the reference data for a quality assessment (QA) program of an ultra-high dose rate (UHDR) electron device in order to validate it for preclinical animal FLASH radiotherapy (FLASH RT) experiments and for FLASH RT clinical human protocols. METHODS: device was evaluated with electron beams of 9 MeV in conventional (CONV) mode and of 6 and 9 MeV in UHDR mode (nominal energy). The acceptance was performed according to the acceptance protocol of the company. The commissioning consisted of determining the short- and long-term stability of the device, the measurement of percent depth dose curves (PDDs) and profiles at two different positions (with two different dose per pulse regimen) and for different collimator sizes, and the evaluation of the variability of these parameters when changing the pulse width and pulse repetition frequency. Measurements were performed using a redundant and validated dosimetric strategy with alanine and radiochromic films, as well as Advanced Markus…

We review the current status of proton FLASH experimental systems, including preclinical physical and biological results. Technological limitations on preclinical investigation of FLASH biological mechanisms and determination of clinically relevant parameters are discussed. A review of the biological data reveals no reproduced proton FLASH effect in vitro and a significant in vivo FLASH sparing effect of normal tissue toxicity observed with multiple proton FLASH irradiation systems. Importantly, multiple studies suggest little or no difference in tumor growth delay for proton FLASH when compared to conventional dose rate proton radiation. A discussion follows on future areas of development with a focus on the determination of the optimal parameters for maximizing the therapeutic ratio between tumor and normal tissue response and ultimately clinical translation of proton FLASH radiation.

PURPOSE: Although large datasets are available, to learn a robust dose prediction model from a limited dataset still remains challenging. This work employed cascaded deep learning models and advanced training strategies with a limited dataset to precisely predict three-dimensional (3D) dose distribution. METHODS: A Cascade 3D (C3D) model is developed based on the cascade mechanism and 3D U-Net network units. During model training, data augmentations are used to improve the generalization ability of the prediction model. A knowledge distillation technique is employed to further improve the capability of model learning. The C3D network was evaluated using the OpenKBP challenge dataset and competed with those models proposed by more than 40 teams globally. Additionally, it was compared with five existing cutting-edge dose prediction models. The performance of these prediction models was evaluated by voxel-based mean absolute error (MAE) and clinical-related dosimetric metrics. The code and models are publicly available online (https://github.com/LSL000UD/RTDosePrediction). RESULTS: The…

Abstract Current radiotherapy facilities are standardized to deliver dose rates around 0.1–0.4 Gy/s in 2 Gy daily fractions, designed to deliver total accumulated doses to reach the tolerance limit of normal tissues undergoing irradiation. FLASH radiotherapy (FLASH‐RT), on the other hand, relies on facilities capable of delivering ultrahigh dose rates in large doses in a single microsecond pulse, or in a few pulses given over a very short time sequence. For example, most studies to date have implemented 4–6 MeV electrons with intra‐pulse dose rates in the range 10 6 –10 7 Gy/s. The proposed dependence of the FLASH effect on oxygen tension has stimulated several theoretical models based on three different hypotheses: (i) Radiation‐induced transient oxygen depletion; (ii) cell‐specific differences in the ability to detoxify and/or recover from injury caused by reactive oxygen species; (iii) self‐annihilation of radicals by bimolecular recombination. This article focuses on the observations supporting or refuting these models in the frame of the chemical‐biological bases of the impact…

PURPOSE: Radiation therapy treatment planning is a time-consuming and iterative manual process. Consequently, plan quality varies greatly between and within institutions. Artificial intelligence shows great promise in improving plan quality and reducing planning times. This technical note describes our participation in the American Association of Physicists in Medicine Open Knowledge-Based Planning Challenge (OpenKBP), a competition to accurately predict radiation therapy dose distributions. METHODS: A three-dimensional (3D) densely connected U-Net with dilated convolutions was developed to predict 3D dose distributions given contoured CT images of head and neck patients as input. While traditional augmentation techniques such as rotations and translations were explored, it was found that training on random patches alone resulted in the greatest model performance. A custom-weighted mean squared error loss function was employed. Finally, an ensemble of best-performing networks was used to generate the final challenge predictions. RESULTS: Our team (SuperPod) placed second in the dose…

Ultra-high-dose rate "FLASH" radiotherapy (FLASH-RT) has been shown to drastically reduce normal tissue toxicities while being as efficacious as conventional dose rate radiotherapy to treat tumors. A large number of preclinical studies describing this so-called FLASH effect have led to the clinical translation of FLASH-RT using ultra-high-dose rate electron and proton beams. Although the vast majority of radiation therapy treatments are delivered using X-rays, few preclinical data using ultra-high-dose rate X-ray irradiation have been published. This review focuses on different methods that can be used to generate ultra-high-dose rate X-rays and their beam characteristics along with their effect on the biological tissues and the perspectives for the development of FLASH-RT with X-rays.

Over the last few years, magnetic resonance image-guided radiotherapy systems have been introduced into the clinic, allowing for daily online plan adaption. While quality assurance (QA) is similar to conventional radiotherapy systems, there is a need to introduce or modify measurement techniques. As yet, there is no consensus guidance on the QA equipment and test requirements for such systems. Therefore, this report provides an overview of QA equipment and techniques for mechanical, dosimetric, and imaging performance of such systems and recommendation of the QA procedures, particularly for a 1.5T MR-linac device. An overview of the system design and considerations for QA measurements, particularly the effect of the machine geometry and magnetic field on the radiation beam measurements is given. The effect of the magnetic field on measurement equipment and methods is reviewed to provide a foundation for interpreting measurement results and devising appropriate methods. And lastly, a consensus overview of recommended QA, appropriate methods, and tolerances is provided based on…

Thermal ablation is a form of hyperthermia in which oncologic control can be achieved by briefly inducing elevated temperatures, typically in the range 50-80°C, within a target tissue. Ablation modalities include high intensity focused ultrasound, radiofrequency ablation, microwave ablation, and laser interstitial thermal therapy which are all capable of generating confined zones of tissue destruction, resulting in fewer complications than conventional cancer therapies. Oncologic control is contingent upon achieving predefined coagulation zones; therefore, intraoperative assessment of treatment progress is highly desirable. Consequently, there is a growing interest in the development of ablation monitoring modalities. The first section of this review presents the mechanism of action and common applications of the primary ablation modalities. The following section outlines the state-of-the-art in thermal dosimetry which includes interstitial thermal probes and radiologic imaging. Both the physical mechanism of measurement and clinical or pre-clinical performance are discussed for…

Abstract Purpose The purpose of this work is to (a) demonstrate the feasibility of delivering a spread‐out Bragg peak (SOBP) proton beam in ultra‐high dose rate (FLASH) using a proton therapy synchrocyclotron as a major step toward realizing an experimental platform for preclinical studies, and (b) evaluate the response of four models of ionization chambers in such a radiation field. Methods A clinical Mevion HYPERSCAN®synchrocyclotron was adjusted for ultra‐high dose rate proton delivery. Protons with nominal energy of 230 MeV were delivered in pulses with temporal width ranging from 12.5 μs to 24 μs spanning from conventional to FLASH dose rates. A boron carbide absorber and a range modulator block were placed in the beam path for range modulation and creating an SOBP dose profile. The radiation field was defined by a brass aperture with 11 mm diameter. Two Faraday cups were used to determine the number of protons per pulse at various dose rates. The dosimetric response of two cylindrical (IBA CC04 and CC13) and two plane‐parallel (IBA PPC05 and PTW Advanced Markus®) ionization…

PURPOSE: To predict radiation pneumonitis (RP) grade 2 or worse after lung stereotactic body radiation therapy (SBRT) using dose-based radiomic (dosiomic) features. METHODS: This multi-institutional study included 247 early-stage nonsmall cell lung cancer patients who underwent SBRT with a prescribed dose of 48-70 Gy at an isocenter between June 2009 and March 2016. Ten dose-volume indices (DVIs) were used, including the mean lung dose, internal target volume size, and percentage of entire lung excluding the internal target volume receiving greater than x Gy (x = 5, 10, 15, 20, 25, 30, 35, and 40). A total of 6,808 dose-segmented dosiomic features, such as shape, first order, and texture features, were extracted from the dose distribution. Patients were randomly partitioned into two groups: model training (70%) and test datasets (30%) over 100 times. Dosiomic features were converted to z-scores (standardized values) with a mean of zero and a standard deviation (SD) of one to put different variables on the same scale. The feature dimension was reduced using the following methods:…

PURPOSE: Compared to CONV-RT (with conventional dose rate), FLASH-RT (with ultra-high dose rate) can provide biological dose sparing for organs-at-risk (OARs) via the so-called FLASH effect, in addition to physical dose sparing. However, the FLASH effect only occurs, when both dose and dose rate meet certain minimum thresholds. This work will develop a simultaneous dose and dose rate optimization (SDDRO) method accounting for both FLASH dose and dose rate constraints during treatment planning for pencil-beam-scanning proton therapy. METHODS: SDDRO optimizes the FLASH effect (specific to FLASH-RT) as well as the dose distribution (similar to CONV-RT). The nonlinear dose rate constraint is linearized, and the reformulated optimization problem is efficiently solved via iterative convex relaxation powered by alternating direction method of multipliers. To resolve and quantify the generic tradeoff of FLASH-RT between FLASH and dose optimization, we propose the use of FLASH effective dose based on dose modifying factor (DMF) owing to the FLASH effect. RESULTS: FLASH-RT via transmission…

OBJECTIVES: We evaluate the feasibility of treatment response prediction using MRI-based pre-, post-, and delta-radiomic features for locally advanced rectal cancer (LARC) patients treated by neoadjuvant chemoradiation therapy (nCRT). MATERIALS AND METHODS: This retrospective study included 53 LARC patients divided into a training set (Center#1, n = 36) and external validation set (Center#2, n = 17). T2-weighted (T2W) MRI was acquired for all patients, 2 weeks before and 4 weeks after nCRT. Ninety-six radiomic features, including intensity, morphological and second- and high-order texture features were extracted from segmented 3D volumes from T2W MRI. All features were harmonized using ComBat algorithm. Max-Relevance-Min-Redundancy (MRMR) algorithm was used as feature selector and k-nearest neighbors (KNN), Naïve Bayes (NB), Random forests (RF), and eXtreme Gradient Boosting (XGB) algorithms were used as classifiers. The evaluation was performed using the area under the receiver operator characteristic (ROC) curve (AUC), sensitivity, specificity and accuracy. RESULTS: In univariate…

PURPOSE: Phantoms are a basic tool for assessing and verifying performance in CT research and clinical practice. Patient-based realistic lung phantoms accurately representing textures and densities are essential in developing and evaluating novel CT hardware and software. This study introduces PixelPrint, a 3D printing solution to create patient-based lung phantoms with accurate attenuation profiles and textures. METHODS: PixelPrint, a software tool, was developed to convert patient digital imaging and communications in medicine (DICOM) images directly into FDM printer instructions (G-code). Density was modeled as the ratio of filament to voxel volume to emulate attenuation profiles for each voxel, with the filament ratio controlled through continuous modification of the printing speed. A calibration phantom was designed to determine the mapping between filament line width and Hounsfield units (HU) within the range of human lungs. For evaluation of PixelPrint, a phantom based on a single human lung slice was manufactured and scanned with the same CT scanner and protocol used for the…

PURPOSE: Liver tumor segmentation is a crucial prerequisite for computer-aided diagnosis of liver tumors. In the clinical diagnosis of liver tumors, radiologists usually examine multiphase CT images as these images provide abundant and complementary information of tumors. However, most known automatic segmentation methods extract tumor features from CT images merely of a single phase, in which valuable multiphase information is ignored. Therefore, it is highly demanded to develop a method effectively incorporating multiphase information for automatic and accurate liver tumor segmentation. METHODS: In this paper, we propose a phase attention residual network (PA-ResSeg) to model multiphase features for accurate liver tumor segmentation. A phase attention (PA) is newly proposed to additionally exploit the images of arterial (ART) phase to facilitate the segmentation of portal venous (PV) phase. The PA block consists of an intraphase attention (intra-PA) module and an interphase attention (inter-PA) module to capture channel-wise self-dependencies and cross-phase interdependencies,…

Purpose The purpose of this work was to provide a flexible platform for FLASH research with protons by adapting a former clinical pencil beam scanning gantry to irradiations with ultra‐high dose rates. Methods PSI Gantry 1 treated patients until December 2018. We optimized the beamline parameters to transport the 250 MeV beam extracted from the PSI COMET accelerator to the treatment room, maximizing the transmission of beam intensity to the sample. We characterized a dose monitor on the gantry to ensure good control of the dose, delivered in spot‐scanning mode. We characterized the beam for different dose rates and field sizes for transmission irradiations. We explored scanning possibilities in order to enable conformal irradiations or transmission irradiations of large targets (with transverse scanning). Results We achieved a transmission of 86% from the cyclotron to the treatment room. We reached a peak dose rate of 9000 Gy/s at 3 mm water equivalent depth, along the central axis of a single pencil beam. Field sizes of up to 5 × 5 mm2 were achieved for single‐spot FLASH…

Abstract Purpose To compare the impact on CT image quality and dose reduction of two versions of a Deep Learning Image Reconstruction algorithm. Material and methods Acquisitions on the CT ACR 464 phantom were performed at five dose levels (CTDIvol: 10/7.5/5/2.5/1 mGy) using chest or abdomen pelvis protocol parameters. Raw data were reconstructed using the filtered‐back projection (FBP), the enhanced level of AIDR 3D (AIDR 3De), and the three levels of AiCE (Mild, Standard, and Strong) for the two versions (AiCE V8 vs AiCE V10). The noise power spectrum (NPS) and task‐based transfer function (TTF) for bone (high‐contrast insert) and acrylic (low‐contrast insert) inserts were computed. To quantify the changes of noise magnitude and texture, the square root of the area under the NPS curve and the average spatial frequency (fav) of the NPS curve were measured. The detectability index (d’) was computed to model the detectability of either a large mass in the liver or lung, or a small calcification or high contrast tissue boundaries. Results The noise magnitude was lower with both AiCE…

PURPOSE: Owing to histologic complexities of brain tumors, its diagnosis requires the use of multimodalities to obtain valuable structural information so that brain tumor subregions can be properly delineated. In current clinical workflow, physicians typically perform slice-by-slice delineation of brain tumor subregions, which is a time-consuming process and also more susceptible to intra- and inter-rater variabilities possibly leading to misclassification. To deal with this issue, this study aims to develop an automatic segmentation of brain tumor in MR images using deep learning. METHOD: In this study, we develop a context deep-supervised U-Net to segment brain tumor subregions. A context block which aggregates multiscale contextual information for dense segmentation was proposed. This approach enlarges the effective receptive field of convolutional neural networks, which, in turn, improves the segmentation accuracy of brain tumor subregions. We performed the fivefold cross-validation on the Brain Tumor Segmentation Challenge (BraTS) 2020 training dataset. The BraTS 2020 testing…

PURPOSE: 4) and indolent cancer (Gleason pattern 3) on a per-pixel basis on MRI to facilitate the targeting of aggressive cancer during biopsy. METHODS: We created the Stanford Prostate Cancer Network (SPCNet), a convolutional neural network model, trained to distinguish between aggressive cancer, indolent cancer, and normal tissue on MRI. Ground truth cancer labels were obtained by registering MRI with whole-mount digital histopathology images from patients who underwent radical prostatectomy. Before registration, these histopathology images were automatically annotated to show Gleason patterns on a per-pixel basis. The model was trained on data from 78 patients who underwent radical prostatectomy and 24 patients without prostate cancer. The model was evaluated on a pixel and lesion level in 322 patients, including six patients with normal MRI and no cancer, 23 patients who underwent radical prostatectomy, and 293 patients who underwent biopsy. Moreover, we assessed the ability of our model to detect clinically significant cancer (lesions with an aggressive component) and compared…

PURPOSE: Detectors for positron emission tomography (PET) typically use two types of scintillation crystals, pixelated or monolithic. A variant of these types of scintillators are the so-called semi-monolithic crystals. They consist of a monolithic crystal segmented in one direction in pieces called slabs. These scintillators have the potential to successfully combine the benefits of pixelated and monolithic configurations, providing good timing and spatial resolutions as well as the capacity to decode the depth of interaction (DOI) information. In this work, the timing performance of a detector based on semi-monolithic crystals was studied in depth. The energy response was also evaluated. METHODS: active area, 50 μm cell size and 3.2 mm pitch. The 64 output signals were independently readout by the TOFPET2 ASIC. In order to achieve the best coincidence time resolution (CTR), four different time walk corrections were tested. Additional work investigated the best method of combining the timestamps belonging to the same event. RESULTS: The resolvability of the slabs in the measured…

Purpose The implementation of photon‐counting detectors is widely expected to be the next breakthrough in X‐ray computed tomography (CT) instrumentation. A small number of prototype scanners equipped with direct‐conversion detectors based on room‐temperature semiconductors, such as CdTe and CdZnTe (CZT), are currently installed at medical centers. Here, we investigate the feasibility of using silicon photomultiplier (SiPM)‐based scintillation detectors in photon‐counting computed tomography (PCCT) scanners, as a potential alternative to CdTe and CZT detectors. Methods We introduce a model that allows us to compute the expected energy resolution as well as the expected pulse shape and associated rate capability of SiPM‐based PCCT detectors. The model takes into account SiPM saturation and optical crosstalk, because these phenomena may substantially affect the performance of SiPM‐based PCCT detectors with sub‐mm pixels. We present model validation experiments using a single‐pixel detector consisting of a 0.9 × 0.9 × 1.0 mm3 LuAP:Ce scintillation crystal coupled to a 1 × 1 mm2 SiPM. We…

PURPOSE: Accurately delineating clinical target volumes (CTV) is essential for completing radiotherapy plans but is time-consuming, labor-intensive, and prone to inter-observer variation. Automating CTV delineation has the benefits of both speeding up contouring process and improving the quality of contours. Recently, auto-segmentation approaches based on deep learning have achieved some improvements. However, unlike organ segmentation, the CTV contains potential tumor spread tissues or subclinical disease tissues, resulting in poorly defined margin interface and irregular shape. It is not reasonable to directly apply the deep learning segmentation algorithms to CTV tasks without considering the unique characteristics of shape and margin. In this work, we propose a novel automatic CTV delineation algorithm based on deep learning addressing the unique shape and margin challenges. METHODS: Our deep learning method, called RA-CTVNet, segments the CTV from cervical cancer CT images. RA-CTVNet denotes our automatic CTV delineation algorithm based on deep learning with Area-aware reweight…

PURPOSE: To present a dataset of computational digital breast phantoms derived from high-resolution three-dimensional (3D) clinical breast images for the use in virtual clinical trials in two-dimensional (2D) and 3D x-ray breast imaging. ACQUISITION AND VALIDATION METHODS: Uncompressed computational breast phantoms for investigations in dedicated breast CT (BCT) were derived from 150 clinical 3D breast images acquired via a BCT scanner at UC Davis (California, USA). Each image voxel was classified in one out of the four main materials presented in the field of view: fibroglandular tissue, adipose tissue, skin tissue, and air. For the image classification, a semi-automatic software was developed. The semi-automatic classification was compared via manual glandular classification performed by two researchers. A total of 60 compressed computational phantoms for virtual clinical trials in digital mammography (DM) and digital breast tomosynthesis (DBT) were obtained from the corresponding uncompressed phantoms via a software algorithm simulating the compression and the elastic deformation…

PURPOSE: Recent studies have illustrated that the peritumoral regions of medical images have value for clinical diagnosis. However, the existing approaches using peritumoral regions mainly focus on the diagnostic capability of the single region and ignore the advantages of effectively fusing the intratumoral and peritumoral regions. In addition, these methods need accurate segmentation masks in the testing stage, which are tedious and inconvenient in clinical applications. To address these issues, we construct a deep convolutional neural network that can adaptively fuse the information of multiple tumoral-regions (FMRNet) for breast tumor classification using ultrasound (US) images without segmentation masks in the testing stage. METHODS: To sufficiently excavate the potential relationship, we design a fused network and two independent modules to extract and fuse features of multiple regions simultaneously. First, we introduce two enhanced combined-tumoral (EC) region modules, aiming to enhance the combined-tumoral features gradually. Then, we further design a three-branch module…

PURPOSE: Metal artifacts can drastically reduce the diagnostic value of computed tomography (CT) images. Even the state-of-the-art algorithms cannot remove them completely. Photon-counting CT inherently provides spectral information, similar to dual-energy CT. Many applications, such as material decomposition, are not possible when metal artifacts are present. Our aim is to develop a prior-based metal artifact reduction specifically for photon-counting CT that can correct each bin image individually or in their combinations. METHODS: Photon-counting CT sorts incoming photons into several energy bins, producing bin and threshold images containing spectral information. We use this spectral information to obtain a better prior image for the state-of-the-art metal artifact reduction algorithm FSNMAR. First, we apply a non-linear transformation to the bin images to obtain bone-emphasized images. Subsequently, we forward-project the bin images and bone-emphasized images and multiply the resulting sinograms with each other element-wise to mimic beam hardening effects. These sinograms are…

PURPOSE: MR-to-CT synthesis is one of the first steps in the establishment of an MRI-only workflow in radiotherapy. Current MR-to-CT synthesis methods in deep learning use unpaired MR and CT training images with a cycle generative adversarial network (CycleGAN) to minimize the effect of misalignment between paired images. However, this approach critically assumes that the underlying interdomain mapping is approximately deterministic and one-to-one. In the current study, we use an Augmented CycleGAN (AugCGAN) model to create a robust model that can be applied to different scanners and sequences using unpaired data. MATERIALS AND METHODS: This study included T2-weighted MR and CT pelvic images of 38 patients in treatment position from five different centers. The AugCGAN was trained on 2D transverse slices of 19 patients from three different sites. The network was then used to generate synthetic CT (sCT) images of 19 patients from the two other sites. Mean absolute errors (MAEs) for each patient were evaluated between real and synthetic CT images. Original treatment plans of nine…

PURPOSE: Long-axial field-of-view (FOV) positron emission tomography (PET) scanners have gained a lot of interest in the recent years. Such scanners provide increased sensitivity and enable unique imaging opportunities that were not previously feasible. Benefiting from the high sensitivity of a long-axial FOV PET scanner, we studied a computed tomography (CT)-less reconstruction algorithm for the Siemens Biograph Vision Quadra with an axial FOV of 106 cm. METHODS: In this work, the background radiation from radioisotope lutetium-176 in the scintillators was used to create an initial estimate of the attenuation maps. Then, joint activity and attenuation reconstruction algorithms were used to create an improved attenuation map of the object. The final attenuation maps were then used to reconstruct quantitative PET images, which were compared against CT-based PET images. The proposed method was evaluated on data from three patients who underwent a flurodeoxyglucouse PET scan. RESULTS: Segmentation of the PET images of the three studied patients showed an average quantitative error of…

BACKGROUND: Sparse-view CT image reconstruction problems encountered in dynamic CT acquisitions are technically challenging. Recently, many deep learning strategies have been proposed to reconstruct CT images from sparse-view angle acquisitions showing promising results. However, two fundamental problems with these deep learning reconstruction methods remain to be addressed: (1) limited reconstruction accuracy for individual patients and (2) limited generalizability for patient statistical cohorts. PURPOSE: The purpose of this work is to address the previously mentioned challenges in current deep learning methods. METHODS: A method that combines a deep learning strategy with prior image constrained compressed sensing (PICCS) was developed to address these two problems. In this method, the sparse-view CT data were reconstructed by the conventional filtered backprojection (FBP) method first, and then processed by the trained deep neural network to eliminate streaking artifacts. The outputs of the deep learning architecture were then used as the needed prior image in PICCS to…

PURPOSE: Cardiovascular magnetic resonance (CMR) is a vital diagnostic tool in the management of cardiovascular diseases. The advent of advanced CMR technologies combined with artificial intelligence (AI) has the potential to simplify imaging, reduce image acquisition time without compromising image quality (IQ), and improve magnetic field uniformity. Here, we aim to implement two AI-based deep learning techniques for automatic slice alignment and cardiac shimming and evaluate their performance in clinical cardiac magnetic resonance imaging (MRI). METHODS: Two deep neural networks were developed, trained, and validated on pre-acquired cardiac MRI datasets (>500 subjects) to achieve automatic slice planning and shimming (implemented in the scanner) for CMR. To examine the performance of our automated cardiac planning (EasyScan) and AI-based shim (AI shim), two prospective studies were performed subsequently. For the EasyScan validation, 10 healthy subjects underwent two identical CMR protocols: with manual cardiac planning and with AI-based EasyScan to assess protocol scan time…

The American Association of Physicists in Medicine (AAPM) formed Task Group 178 (TG-178) to perform the following tasks: review in-phantom and in-air calibration protocols for gamma stereotactic radiosurgery (GSR), suggest a dose rate calibration protocol that can be successfully utilized with all gamma stereotactic radiosurgery (GSR) devices, and update quality assurance (QA) protocols in TG-42 (AAPM Report 54, 1995) for static GSR devices. The TG-178 report recommends a GSR dose rate calibration formalism and provides tabulated data to implement it for ionization chambers commonly used in GSR dosimetry. The report also describes routine mechanical, dosimetric, and safety checks for GSR devices, and provides treatment process quality assurance recommendations. Sample worksheets, checklists, and practical suggestions regarding some QA procedures are given in appendices. The overall goal of the report is to make recommendations that help standardize GSR physics practices and promote the safe implementation of GSR technologies.

Purpose To present the technical details of the runner‐up model in the open knowledge‐based planning (OpenKBP) challenge for the dose–volume histogram (DVH) stream. The model was designed to ensure simple and reproducible training, without the necessity of costly advanced generative adversarial network (GAN) techniques. Methods The model was developed based on the OpenKBP challenge dataset, consisting of 200 and 40 head‐and‐neck patients for training and validation, respectively. The final model is a U‐Net with additional ResNet blocks between up‐ and down convolutions. The results were obtained by training the model with AdamW with the One Cycle scheduler. The loss function is a combination of the L1 loss with a feature loss, which uses a pretrained video classifier as a feature extractor. The performance was evaluated on another 100 patients in the OpenKBP test dataset. The DVH metrics of the test data were evaluated, where , and were calculated for the organs at risk (OARs) and , , and were computed for the target structures. DVH metric differences between predicted and true dose…

PURPOSE: Breast ultrasound (BUS) image segmentation plays a crucial role in computer-aided diagnosis systems for BUS examination, which are useful for improved accuracy of breast cancer diagnosis. However, such performance remains a challenging task owing to the poor image quality and large variations in the sizes, shapes, and locations of breast lesions. In this paper, we propose a new convolutional neural network with coarse-to-fine feature fusion to address the aforementioned challenges. METHODS: The proposed fusion network consists of an encoder path, a decoder path, and a core fusion stream path (FSP). The encoder path is used to capture the context information, and the decoder path is used for localization prediction. The FSP is designed to generate beneficial aggregate feature representations (i.e., various-sized lesion features, aggregated coarse-to-fine information, and high-resolution edge characteristics) from the encoder and decoder paths, which are eventually used for accurate breast lesion segmentation. To better retain the boundary information and alleviate the effect…

PURPOSE: The present study assessed the predictive value of peritumoral regions on three tumor tasks, and further explored the influence of peritumors with different sizes. METHODS: We retrospectively collected 333 samples of gastrointestinal stromal tumors from the Second Affiliated Hospital of Zhejiang University School of Medicine, and 183 samples of gastrointestinal stromal tumors from Tianjin Medical University Cancer Hospital. We also collected 211 samples of laryngeal carcinoma and 233 samples of nasopharyngeal carcinoma from the First Affiliated Hospital of Jinan University. The tasks of three tumor datasets were risk assessment (gastrointestinal stromal tumor), T3/T4 staging prediction (laryngeal carcinoma), and distant metastasis prediction (nasopharyngeal carcinoma), respectively. First, deep learning and radiomics were respectively used to construct peritumoral models, to study whether the peritumor had predictive value on three tumor datasets. Furthermore, we defined different sizes peritumors including fixed size (not considering tumor size) and adaptive size…

PURPOSE: A crucial step in the preoperative planning for a revision total hip replacement (THR) surgery is the accurate identification of the failed implant design, especially if one or more well-fixed/functioning components are to be retained. Manual identification of the implant design from preoperative radiographic images can be time-consuming and inaccurate, which can ultimately lead to increased operating room time, more complex surgery, and increased healthcare costs. METHOD: In this study, we present a novel approach to identifying THR femoral implants' design from plain radiographs using a convolutional neural network (CNN). We evaluated a total of 402 radiographs of nine different THR implant designs including, Accolade II (130 radiographs), Corail (89 radiographs), M/L Taper (31 radiographs), Summit (31 radiographs), Anthology (26 radiographs), Versys (26 radiographs), S-ROM (24 radiographs), Taperloc Standard Offset (24 radiographs), and Taperloc High Offset (21 radiographs). We implemented a transfer learning approach and adopted a DenseNet-201 CNN architecture by…

PURPOSE: Laryngoscopy, the most common diagnostic method for vocal cord lesions (VCLs), is based mainly on the visual subjective inspection of otolaryngologists. This study aimed to establish a highly objective computer-aided VCLs diagnosis system based on deep convolutional neural network (DCNN) and transfer learning. METHODS: To classify VCLs, our method combined the DCNN backbone with transfer learning on a system specifically finetuned for a laryngoscopy image dataset. Laryngoscopy image database was collected to train the proposed system. The diagnostic performance was compared with other DCNN-based models. Analysis of F1 score and receiver operating characteristic curves were conducted to evaluate the performance of the system. RESULTS: Beyond the existing VCLs diagnosis method, the proposed system achieved an overall accuracy of 80.23%, an F1 score of 0.7836, and an area under the curve (AUC) of 0.9557 for four fine-grained classes of VCLs, namely, normal, polyp, keratinization, and carcinoma. It also demonstrated robust classification capacity for detecting urgent…

PURPOSE: Positron emission tomography (PET) has been widely used in various clinical applications. PET is a type of emission computed tomography and operates by positron annihilation radiation. With magnetic resonance imaging (MRI) providing anatomical information, joint PET/MRI reduces the radiation exposure risk of patients. Improved hardware and imaging algorithms have been proposed to further decrease the dose from radioactive tracers or the bed duration, but few methods focus on denoising low-count PET with MRI input. The existing methods are based on fixed conventional convolution and local attention, which do not sufficiently extract and fuse contextual and complementary information from multimodal input. There is still much room for improvement. Therefore, we propose a novel deep learning method for low-count PET/MRI denoising called the spatial-adaptive and transformer fusion network (STFNet), which consists of a Siamese encoder with a spatial-adaptive block (SA-block) and the transformer fusion encoder (TFE). METHODS: Our proposed STFNet consists of a Siamese encoder with…

PURPOSE: In current clinical practice, noisy and artifact-ridden weekly cone beam computed tomography (CBCT) images are only used for patient setup during radiotherapy. Treatment planning is performed once at the beginning of the treatment using high-quality planning CT (pCT) images and manual contours for organs-at-risk (OARs) structures. If the quality of the weekly CBCT images can be improved while simultaneously segmenting OAR structures, this can provide critical information for adapting radiotherapy mid-treatment as well as for deriving biomarkers for treatment response. METHODS: Using a novel physics-based data augmentation strategy, we synthesize a large dataset of perfectly/inherently registered pCT and synthetic-CBCT pairs for locally advanced lung cancer patient cohort, which are then used in a multitask three-dimensional (3D) deep learning framework to simultaneously segment and translate real weekly CBCT images to high-quality pCT-like images. RESULTS: We compared the synthetic CT and OAR segmentations generated by the model to real pCT and manual OAR segmentations and…

PURPOSE: Breast cancer is the cancer with the highest incidence in women, and early detection can effectively improve the survival rate of patients. Mammography is an important method for physicians to screening breast cancer, but the diagnosis of mammograms by physicians depends largely on clinical practice experience. Studies have shown that using computer-aided diagnosis techniques can help doctors diagnose breast cancer. METHODS: In this paper, the method of convolutional neural network is mainly used to classify benign and malignant breast masses in the mammograms. First, we use multi-scale residual networks and densely connected networks as backbone networks to extract the features of global image patches and local image patches. Second, we use the attention module named convolutional block attention module (CBAM) to improve the two feature extraction networks to enhance the network's feature expression ability. Finally, we fuse the features of multi-scale image patches to achieve the classification of benign and malignant breast masses. RESULTS: In the digital database for…