Abstract:Objective: Computed tomography (CT) to material property conversion dominates proton range uncertainty, impacting the quality of proton treatment planning. Physics-based and machine learning-based methods have been investigated to leverage dual-energy CT (DECT) to predict proton ranges. Recent development includes physics-informed deep learning (DL) for material property inference. This paper aims to develop a framework to validate Monte Carlo dose calculation (MCDC) using CT-based material characterization mo… Show more
“…The IDD measurement includes the range uncertainty of ± 1 mm. Because the R80 value minimizes the dependency on initial energy spread [ 30 ], the measured R80 is compared with the continuous slowing down approximation range from the National Institute of Standards and Technology. The R80 differences are within 1 and 3 mm when compared with National Institute of Standards and Technology data for 249- and 250-MeV proton ranges, respectively.…”
Shoot-through proton FLASH radiation therapy has been proposed where the highest energy is extracted from a cyclotron to maximize the dose rate (DR). Although our proton pencil beam scanning system can deliver 250 MeV (the highest energy), this energy is not used clinically, and as such, 250 MeV has yet to be characterized during clinical commissioning. We aim to characterize the 250-MeV proton beam from the Varian ProBeam system for FLASH and assess the usability of the clinical monitoring ionization chamber (MIC) for FLASH use.
We measured the following data for beam commissioning: integral depth dose curve, spot sigma, and absolute dose. To evaluate the MIC, we measured output as a function of beam current. To characterize a 250 MeV FLASH beam, we measured (1) the central axis DR as a function of current and spot spacing and arrangement, (2) for a fixed spot spacing, the maximum field size that achieves FLASH DR (ie, > 40 Gy/s), and (3) DR reproducibility. All FLASH DR measurements were performed using an ion chamber for the absolute dose, and irradiation times were obtained from log files. We verified dose measurements using EBT-XD films and irradiation times using a fast, pixelated spectral detector.
R90 and R80 from integral depth dose were 37.58 and 37.69 cm, and spot sigma at the isocenter were σx = 3.336 and σy = 3.332 mm, respectively. The absolute dose output was measured as 0.343 Gy*mm2/MU for the commissioning conditions. Output was stable for beam currents up to 15 nA and gradually increased to 12-fold for 115 nA. Dose and DR depended on beam current, spot spacing, and arrangement and could be reproduced with 6.4% and 4.2% variations, respectively.
Although FLASH was achieved and the largest field size that delivers FLASH DR was determined as 35 × 35 mm2, the current MIC has DR dependence, and users should measure dose and DR independently each time for their FLASH applications.
“…The IDD measurement includes the range uncertainty of ± 1 mm. Because the R80 value minimizes the dependency on initial energy spread [ 30 ], the measured R80 is compared with the continuous slowing down approximation range from the National Institute of Standards and Technology. The R80 differences are within 1 and 3 mm when compared with National Institute of Standards and Technology data for 249- and 250-MeV proton ranges, respectively.…”
Shoot-through proton FLASH radiation therapy has been proposed where the highest energy is extracted from a cyclotron to maximize the dose rate (DR). Although our proton pencil beam scanning system can deliver 250 MeV (the highest energy), this energy is not used clinically, and as such, 250 MeV has yet to be characterized during clinical commissioning. We aim to characterize the 250-MeV proton beam from the Varian ProBeam system for FLASH and assess the usability of the clinical monitoring ionization chamber (MIC) for FLASH use.
We measured the following data for beam commissioning: integral depth dose curve, spot sigma, and absolute dose. To evaluate the MIC, we measured output as a function of beam current. To characterize a 250 MeV FLASH beam, we measured (1) the central axis DR as a function of current and spot spacing and arrangement, (2) for a fixed spot spacing, the maximum field size that achieves FLASH DR (ie, > 40 Gy/s), and (3) DR reproducibility. All FLASH DR measurements were performed using an ion chamber for the absolute dose, and irradiation times were obtained from log files. We verified dose measurements using EBT-XD films and irradiation times using a fast, pixelated spectral detector.
R90 and R80 from integral depth dose were 37.58 and 37.69 cm, and spot sigma at the isocenter were σx = 3.336 and σy = 3.332 mm, respectively. The absolute dose output was measured as 0.343 Gy*mm2/MU for the commissioning conditions. Output was stable for beam currents up to 15 nA and gradually increased to 12-fold for 115 nA. Dose and DR depended on beam current, spot spacing, and arrangement and could be reproduced with 6.4% and 4.2% variations, respectively.
Although FLASH was achieved and the largest field size that delivers FLASH DR was determined as 35 × 35 mm2, the current MIC has DR dependence, and users should measure dose and DR independently each time for their FLASH applications.
“…Rapid improvements in artificial intelligence (AI) have enabled broad application of AI-assisted medical image analysis (classification, segmentation, registration, synthesis) pipelines [4][5][6][7][8][9][10][11][12][13], including semi-automated retinopathy detection systems using machine learning (ML) classifiers [14] based on human-designed features as well as fully-automated deep learning (DL) systems [15,16]. Currently, mainstream DL frameworks include Multilayer Perceptrons (MLP), Transformers [17], and Convolutional Neural Networks (CNN) [18], which can only take in grid or sequence data.…”
Retinopathy refers to pathologies of the retina that can ultimately result in vision impairment and blindness. Optical Coherence Tomography (OCT) is a technique to image these diseases, aiding in the early detection of retinal damage, which may mitigate the risk of vision loss. In this work, we propose an end-to-end Graph Neural Network (GNN) pipeline that can extract deep graph-based features for multi-class retinopathy classification for the first time. To our knowledge, this is also the first work applying Vision-GNN for OCT image analysis. We trained and tested the proposed GNN on a public OCT retina dataset divided into four categories (Normal, Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), and Drusen). Using our method, we achieve an average accuracy of 99.07% over four classes proving the effectiveness of a deep learning classifier for OCT images with graph-based features. This work lays the foundation to apply GNNs for OCT imaging to aid the early detection of retinal damage.
“…They can automatically extract the image features for precise predictions in various computer vision (CV) tasks [7]. In medical imaging, DL-powered systems have significantly changed the landscape with unprecedented processing speed and accuracy [8][9][10][11][12][13][14]. Currently, convolutional neural networks (CNNs) [15] and Vision Transformers [16] are the most widely used backbone for these frameworks.…”
Breast cancer is the most commonly diagnosed cancer in women in the United States. Early detection of breast tumors enables prompt determination of cancer status, significantly boosting patient survival rate. Non-invasive and non-ionizing ultrasound imaging is a widely used diagnosing modality in clinic. To assist clinicians in breast cancer diagnosis, we implemented a vision graph neural networks (ViG)-based pipeline that can achieve accurate binary classification (normal vs. breast tumor) and multiclass classification (normal, benign, and malignant) from breast ultrasound images. Our results demonstrated that the average accuracy of ViG is 100.00% for binary and 87.18% for multiclass classification tasks. To the best of our knowledge, this is the first end-to-end, graph-feature-based deep learning pipeline to achieve accurate breast tumor detection from ultrasound images. The proposed ViG-based classifier is accessible for clinical implementation and has the potential to enhance lesion detection from ultrasound images.
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