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Publications (311)
Background
Cone‐beam computed tomography (CBCT) scans, performed fractionally (e.g., daily or weekly), are widely utilized for patient alignment in the image‐guided radiotherapy (IGRT) process, thereby making it a potential imaging modality for the implementation of adaptive radiotherapy (ART) protocols. Nonetheless, significant artifacts and incor...
Purpose
Apparent diffusion coefficient (ADC) maps derived from diffusion weighted magnetic resonance imaging (DWI MRI) provides functional measurements about the water molecules in tissues. However, DWI is time consuming and very susceptible to image artifacts, leading to inaccurate ADC measurements. This study aims to develop a deep learning frame...
Very fast imaging techniques can enhance the precision of image-guided radiation therapy, which can be useful for external beam radiation therapy. This work aims to develop a deep learning (DL)-based image-guide framework to enable fast volumetric image reconstruction for accurate target localization for treating lung cancer patients with gating, a...
In this work, we present a new imaging system to support real-time tumor tracking for surface-guided radiotherapy (SGRT). SGRT uses optical surface imaging (OSI) to acquire real-time surface topography images of the patient on the treatment couch. This serves as a surrogate for intra-fractional tumor motion tracking to guide radiation delivery. How...
We present a new imaging system to support real-time tumor tracking for surface-guided radiotherapy (SGRT). SGRT uses optical surface imaging (OSI) to acquire real-time surface topography images of the patient on the treatment couch. However, OSI cannot visualize internal anatomy. This study proposes an Advanced Surface Imaging (A-SI) framework to...
Background
Cone beam computed tomography (CBCT) can be used to evaluate the inter‐fraction anatomical changes during the entire course for image‐guided radiotherapy (IGRT). However, CBCT artifacts from various sources restrict the full application of CBCT‐guided adaptive radiation therapy (ART).
Purpose
Inter‐fraction anatomical changes during ART...
Artificial intelligence (AI) has the potential to revolutionize brachytherapy’s clinical workflow. This review comprehensively examines the application of AI, focusing on machine learning and deep learning, in facilitating various aspects of brachytherapy. We analyze AI’s role in making brachytherapy treatments more personalized, efficient, and eff...
Artificial intelligence (AI) has the potential to revolutionize brachytherapy's clinical workflow. This review comprehensively examines the application of AI, focusing on machine learning and deep learning, in facilitating various aspects of brachytherapy. We analyze AI's role in making brachytherapy treatments more personalized, efficient, and eff...
Background: Limited-angle (LA) dual-energy (DE) cone-beam CT (CBCT) is considered as a potential solution to achieve fast and low-dose DE imaging on current CBCT scanners without hardware modification. However, its clinical implementations are hindered by the challenging image reconstruction from LA projections. While optimization-based and deep le...
Objective. The study aimed to generate synthetic contrast-enhanced Dual-energy CT (CE-DECT) images from non-contrast single-energy CT (SECT) scans, addressing the limitations posed by the scarcity of DECT scanners and the health risks associated with iodinated contrast agents, particularly for high-risk patients. Approach. A conditional denoising d...
Background: Cone-beam computed tomography (CBCT) scans, performed fractionally (e.g., daily or weekly), are widely utilized for patient alignment in the image-guided radiotherapy (IGRT) process, thereby making it a potential imaging modality for the implementation of adaptive radiotherapy (ART) protocols. Nonetheless, significant artifacts and inco...
Background
Iodine maps, derived from image‐processing of contrast‐enhanced dual‐energy computed tomography (DECT) scans, highlight the differences in tissue iodine intake. It finds multiple applications in radiology, including vascular imaging, pulmonary evaluation, kidney assessment, and cancer diagnosis. In radiation oncology, it can contribute t...
Background
Dual‐energy computed tomography (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image signal‐to‐noise ratios (SNRs). While existing iterative algorithms perform noise suppression using diffe...
Background
Stereotactic body radiotherapy (SBRT) is a well‐established treatment modality for liver metastases in patients unsuitable for surgery. Both CT and MRI are useful during treatment planning for accurate target delineation and to reduce potential organs‐at‐risk (OAR) toxicity from radiation. MRI‐CT deformable image registration (DIR) is re...
Background
Stereotactic body radiotherapy (SBRT) is a well-established treatment modality for liver metastases in patients unsuitable for surgery. Both CT and MRI are useful during treatment planning for accurate target delineation and to reduce potential organs-at-risk (OAR) toxicity from radiation. MRI-CT deformable image registration (DIR) is re...
Background
7 Tesla (7T) apparent diffusion coefficient (ADC) maps derived from diffusion‐weighted imaging (DWI) demonstrate improved image quality and spatial resolution over 3 Tesla (3T) ADC maps. However, 7T magnetic resonance imaging (MRI) currently suffers from limited clinical unavailability, higher cost, and increased susceptibility to artifa...
Purpose
Positron Emission Tomography (PET) has been a commonly used imaging modality in broad clinical applications. One of the most important tradeoffs in PET imaging is between image quality and radiation dose: high image quality comes with high radiation exposure. Improving image quality is desirable for all clinical applications while minimizin...
Background and purpose
A novel radiotracer, ¹⁸F-fluciclovine (anti-3-¹⁸F-FACBC), has been demonstrated to be associated with significantly improved survival when it is used in PET/CT imaging to guide postprostatectomy salvage radiotherapy for prostate cancer. We aimed to investigate the feasibility of using a deep learning method to automatically d...
The hippocampus plays a crucial role in memory and cognition. Because of the associated toxicity from whole brain radiotherapy, more advanced treatment planning techniques prioritize hippocampal avoidance, which depends on an accurate segmentation of the small and complexly shaped hippocampus. To achieve accurate segmentation of the anterior and po...
Background and purpose
Magnetic resonance imaging (MRI)‐based synthetic computed tomography (sCT) simplifies radiation therapy treatment planning by eliminating the need for CT simulation and error‐prone image registration, ultimately reducing patient radiation dose and setup uncertainty. In this work, we propose a MRI‐to‐CT transformer‐based impro...
Recent advances in MRI‐guided radiation therapy (MRgRT) and deep learning techniques encourage fully adaptive radiation therapy (ART), real‐time MRI monitoring, and the MRI‐only treatment planning workflow. Given the rapid growth and emergence of new state‐of‐the‐art methods in these fields, we systematically review 197 studies written on or before...
Background
An automated, accurate, and efficient lung four‐dimensional computed tomography (4DCT) image registration method is clinically important to quantify respiratory motion for optimal motion management.
Purpose
The purpose of this work is to develop a weakly supervised deep learning method for 4DCT lung deformable image registration (DIR)....
Background
Daily or weekly cone‐beam computed tomography (CBCT) scans are commonly used for accurate patient positioning during the image‐guided radiotherapy (IGRT) process, making it an ideal option for adaptive radiotherapy (ART) replanning. However, the presence of severe artifacts and inaccurate Hounsfield unit (HU) values prevent its use for q...
To reduce the risks associated with ionizing radiation, a reduction of radiation exposure in PET imaging is needed. However, this leads to a detrimental effect on image contrast and quantification. High-quality PET images synthesized from low-dose data offer a solution to reduce radiation exposure. We introduce a diffusion-model-based approach for...
Dual-energy computed tomography (DECT) is a promising technology that has shown a number of clinical advantages over conventional X-ray CT, such as improved material identification, artifact suppression, etc. For proton therapy treatment planning, besides material-selective images, maps of effective atomic number (Z) and relative electron density t...
Dual-energy computed tomography (DECT) is a promising technology that has shown a number of clinical advantages over conventional X-ray CT, such as improved material identification, artifact suppression, etc. For proton therapy treatment planning, besides material-selective images, maps of effective atomic number (Z) and relative electron density t...
In this work, we demonstrate a method for rapid synthesis of high-quality CT images from unpaired, low-quality CBCT images, permitting CBCT-based adaptive radiotherapy. We adapt contrastive unpaired translation (CUT) to be used with medical images and evaluate the results on an institutional pelvic CT dataset. We compare the method against cycleGAN...
Background: The hippocampus plays a crucial role in memory and cognition. Because of the associated toxicity from whole brain radiotherapy, more advanced treatment planning techniques prioritize hippocampal avoidance, which depends on an accurate segmentation of the small and complexly shaped hippocampus. Purpose: To achieve accurate segmentation o...
Background:
The hippocampus plays a crucial role in memory and cognition. Because of the associated toxicity from whole brain radiotherapy, more advanced treatment planning techniques prioritize hippocampal avoidance, which depends on an accurate segmentation of the small and complexly shaped hippocampus.
Purpose:
To achieve accurate segmentatio...
Magnetic resonance imaging (MRI)-based synthetic computed tomography (sCT) simplifies radiation therapy treatment planning by eliminating the need for CT simulation and error-prone image registration, ultimately reducing patient radiation dose and setup uncertainty. We propose an MRI-to-CT transformer-based denoising diffusion probabilistic model (...
Artificial intelligence (AI) methods have gained popularity in medical imaging research. The size and scope of the training image datasets needed for successful AI model deployment does not always have the desired scale. In this paper, we introduce a medical image synthesis framework aimed at addressing the challenge of limited training datasets fo...
The advent of computed tomography significantly improves patient health regarding diagnosis, prognosis, and treatment planning and verification. However, tomographic imaging escalates concomitant radiation doses to patients, inducing potential secondary cancer. We demonstrate the feasibility of a data-driven approach to synthesize volumetric images...
Purpose:
Proton vertebral body sparing craniospinal irradiation (CSI) treats the thecal sac while avoiding the anterior vertebral bodies in an effort to reduce myelosuppression and growth inhibition. However, robust treatment planning needs to compensate for proton range uncertainty, which contributes unwanted doses within the vertebral bodies. Th...
This study aims to develop a novel Cycle-guided Denoising Diffusion Probability Model (CG-DDPM) for cross-modality MRI synthesis. The CG-DDPM deploys two DDPMs that condition each other to generate synthetic images from two different MRI pulse sequences. The two DDPMs exchange random latent noise in the reverse processes, which helps to regularize...
The advent of computed tomography significantly improves patient health regarding diagnosis, prognosis, and treatment planning and verification. However, tomographic imaging escalates concomitant radiation doses to patients, inducing potential secondary cancer. We demonstrate the feasibility of a data-driven approach to synthesize volumetric images...
CBCTs in image-guided radiotherapy provide crucial anatomy information for patient setup and plan evaluation. Longitudinal CBCT image registration could quantify the inter-fractional anatomic changes, e.g. tumor shrinkage, daily OAR variation throughout the course of treatment. The purpose of this study is to propose an unsupervised deep learning b...
Purpose
Radiation damage on neurovascular bundles (NVBs) may be the cause of sexual dysfunction after radiotherapy for prostate cancer. However, it is challenging to delineate NVBs as organ‐at‐risks from planning CTs during radiotherapy. Recently, the integration of MR into radiotherapy made NVBs contour delineating possible. In this study, we aim...
Purpose
The long acquisition time of CBCT discourages repeat verification imaging, therefore increasing treatment uncertainty. In this study, we present a fast volumetric imaging method for lung cancer radiation therapy using an orthogonal 2D kV/MV image pair.
Methods
The proposed model is a combination of 2D and 3D networks. The proposed model co...
MRI-guided radiation therapy (MRgRT) offers a precise and adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematically reviewed. MRI-guided radiation therapy offers a precise, adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT...
MRI-guided radiation therapy (MRgRT) offers a precise and adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematically reviewed. MRI-guided radiation therapy offers a precise, adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT...
Background: Daily or weekly cone-beam computed tomography (CBCT) scans are commonly used for accurate patient positioning during the image-guided radiotherapy (IGRT) process, making it an ideal option for adaptive radiotherapy (ART) replanning. However, the presence of severe artifacts and inaccurate Hounsfield unit (HU) values prevent its use for...
In this work, we propose an adversarial attack-based data augmentation method to improve the deep-learning-based segmentation algorithm for the delineation of Organs-At-Risk (OAR) in abdominal Computed Tomography (CT) to facilitate radiation therapy. We introduce Adversarial Feature Attack for Medical Image (AFA-MI) augmentation, which forces the s...
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...
Purpose/Objective(s)
The application of MRI significantly improves the accuracy and reliability of target delineation for many disease sites in radiotherapy (RT) due to its superior soft tissue contrast as compared with CT. One common practice in acquiring the MR images is to use thick slices while keeping the in-plane resolution, especially for th...
Purpose/Objective(s)
Molecular imaging with novel PET radiotracers has been shown to significantly impact radiotherapy decision making, target definition, and cancer control in the setting of prostate cancer (PCa) recurrent after prostatectomy. We propose a deep learning-based method to automatically segment pelvic node and prostate bed lesions on...
Purpose: Proton vertebral body sparing craniospinal irradiation (VBS CSI) treats the thecal sac while avoiding the anterior vertebral bodies in effort to reduce myelosuppression and growth inhibition. However, robust treatment planning needs to compensate proton range uncertainty, contributing unwanted doses within the vertebral bodies. This work a...
Purpose: Dual-energy CT (DECT) has been shown to derive a stopping-power-ratio (SPR) map with higher accuracy than
conventional single-energy CT (SECT) by obtaining energy dependence of photon interactions. However, DECT is not as
widely implemented as SECT in proton radiotherapy simulation. This work presents a learning-based method to synthesize...
Objective:
This work aims to develop an automated segmentation method for the prostate and its surrounding organs-at-risk (OAR) in pelvic computed tomography to facilitate prostate radiation treatment planning.
Approach:
In this work, we propose a novel deep-learning algorithm combining a U-shaped convolutional neural network (CNN) and vision tr...
Background
Multimodality positron emission tomography/computed tomography (PET/CT) imaging combines the anatomical information of CT with the functional information of PET. In the diagnosis and treatment of many cancers, such as non‐small cell lung cancer (NSCLC), PET/CT imaging allows more accurate delineation of tumor or involved lymph nodes for...