Lei Ren

Journal Article Med Phys · October 2024 BACKGROUND: Protoacoustic (PA) imaging has the potential to provide real-time 3D dose verification of proton therapy. However, PA images are susceptible to severe distortion due to limited angle acquisition. Our previous studies showed the potential of usi ... Full text Link to item Cite

Machine learning predicts conventional imaging metastasis-free survival (MFS) for oligometastatic castration-sensitive prostate cancer (omCSPC) using prostate-specific membrane antigen (PSMA) PET radiomics.

Journal Article Radiother Oncol · October 2024 PURPOSE: This study investigated imaging biomarkers derived from PSMA-PET acquired pre- and post-metastasis-directed therapy (MDT) to predict 2-year metastasis-free survival (MFS), which provides valuable early response assessment to improve patient outcom ... Full text Link to item Cite

Enhanced Electroacoustic Tomography with Supervised Learning for Real-time Electroporation Monitoring

Journal Article Precision Radiation Oncology · September 1, 2024 Background: Nanosecond pulsed electric fields (nsPEF)-based electroporation is a new therapy modality potentially synergized with radiation therapy to improve treatment outcomes. To verify its treatment accuracy intraoperatively, electroacoustic tomography ... Full text Cite

A review on 4D cone-beam CT (4D-CBCT) in radiation therapy: Technical advances and clinical applications.

Journal Article Med Phys · August 2024 Cone-beam CT (CBCT) is the most commonly used onboard imaging technique for target localization in radiation therapy. Conventional 3D CBCT acquires x-ray cone-beam projections at multiple angles around the patient to reconstruct 3D images of the patient in ... Full text Link to item Cite

Prostate-Specific Membrane Antigen PET Response Associates with Metastasis-Free Survival After Stereotactic Ablative Radiation in Oligometastatic Prostate Cancer

Journal Article Advances in Radiation Oncology · July 1, 2024 Purpose: Emerging data suggest that metastasis-directed therapy (MDT) improves outcomes in patients with oligometastatic castration-sensitive prostate cancer (omCSPC). Prostate-specific membrane antigen positron emission tomography (PSMA-PET) can detect oc ... Full text Cite

Hybrid-supervised deep learning for domain transfer 3D protoacoustic image reconstruction.

Journal Article Phys Med Biol · April 3, 2024 Objective. Protoacoustic imaging showed great promise in providing real-time 3D dose verification of proton therapy. However, the limited acquisition angle in protoacoustic imaging induces severe artifacts, which impairs its accuracy for dose verification. ... Full text Link to item Cite

TP53 structure-function relationships in metastatic castrate-sensitive prostate cancer and the impact of APR-246 treatment.

Journal Article Prostate · January 2024 PURPOSE: Despite well-informed work in several malignancies, the phenotypic effects of TP53 mutations in metastatic castration-sensitive prostate cancer (mCSPC) progression and metastasis are not clear. We characterized the structure-function and clinical ... Full text Link to item Cite

Radiomic biomarkers of locoregional recurrence: prognostic insights from oral cavity squamous cell carcinoma preoperative CT scans

Journal Article Frontiers in Oncology · January 1, 2024 Introduction: This study aimed to identify CT-based imaging biomarkers for locoregional recurrence (LR) in Oral Cavity Squamous Cell Carcinoma (OSCC) patients. Methods: Computed tomography scans were collected from 78 patients with OSCC who underwent surgi ... Full text Cite

Identification of CT-based non-invasive radiomic biomarkers for overall survival prediction in oral cavity squamous cell carcinoma.

Journal Article Sci Rep · December 8, 2023 This study addresses the limited non-invasive tools for Oral Cavity Squamous Cell Carcinoma (OSCC) survival prediction by identifying Computed Tomography (CT)-based biomarkers to improve prognosis prediction. A retrospective analysis was conducted on data ... Full text Link to item Cite

Clinical and Genomic Differences Between Advanced Molecular Imaging-detected and Conventional Imaging-detected Metachronous Oligometastatic Castration-sensitive Prostate Cancer.

Journal Article Eur Urol · December 2023 In metastatic castration-sensitive prostate cancer (mCSPC), disease volume plays an integral role in guiding treatment recommendations, including selection of docetaxel therapy, metastasis-directed therapy, and radiation to the prostate. Although there are ... Full text Link to item Cite

Radiation-induced acoustic signal denoising using a supervised deep learning framework for imaging and therapy monitoring.

Journal Article Phys Med Biol · November 29, 2023 Radiation-induced acoustic (RA) imaging is a promising technique for visualizing the invisible radiation energy deposition in tissues, enabling new imaging modalities and real-time therapy monitoring. However, RA imaging signal often suffers from poor sign ... Full text Link to item Cite

Interpretable AI-assisted clinical decision making (CDM) for dose prescription in radiosurgery of brain metastases.

Journal Article Radiother Oncol · October 2023 PURPOSE: AI modeling physicians' clinical decision-making (CDM) can improve the efficiency and accuracy of clinical practice or serve as a surrogate to provide initial consultations to patients seeking secondary opinions. In this study, we developed an int ... Full text Link to item Cite

On the correction of respiratory motion-induced image reconstruction errors in positron-emission tomography-guided radiation therapy

Journal Article Physics and Imaging in Radiation Oncology · April 1, 2023 Background and purpose: Free breathing (FB) positron emission tomography (PET) images are routinely used in radiotherapy for lung cancer patients. Respiration-induced artifacts in these images compromise treatment response assessment and obstruct clinical ... Full text Cite

A feasibility study of enhanced prompt gamma imaging for range verification in proton therapy using deep learning.

Journal Article Phys Med Biol · March 20, 2023 Background and objective. Range uncertainty is a major concern affecting the delivery precision in proton therapy. The Compton camera (CC)-based prompt-gamma (PG) imaging is a promising technique to provide 3Din vivorange verification. However, the convent ... Full text Link to item Cite

AI-assisted clinical decision making (CDM) for dose prescription in radiosurgery of brain metastases using three-path three-dimensional CNN

Journal Article Clinical and Translational Radiation Oncology · March 1, 2023 Purpose: AI modeling physicians’ clinical decision-making (CDM) can improve the efficiency and accuracy of clinical practice or serve as a surrogate to provide initial consultations to patients seeking secondary opinions. In this study, we developed an AI ... Full text Cite

Accelerating Real-Time Imaging for Radiotherapy: Leveraging Multi-GPU Training with PyTorch

Conference Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023 · January 1, 2023 Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mit-igate unnecessary radiation exposure in healthy tissues. Real-time imaging of prompt ga ... Full text Cite

3Din vivodose verification in prostate proton therapy with deep learning-based proton-acoustic imaging.

Journal Article Phys Med Biol · October 27, 2022 Dose delivery uncertainty is a major concern in proton therapy, adversely affecting the treatment precision and outcome. Recently, a promising technique, proton-acoustic (PA) imaging, has been developed to provide real-timein vivo3D dose verification. Howe ... Full text Link to item Cite

Fast four-dimensional cone-beam computed tomography reconstruction using deformable convolutional networks.

Journal Article Med Phys · October 2022 BACKGROUND: Although four-dimensional cone-beam computed tomography (4D-CBCT) is valuable to provide onboard image guidance for radiotherapy of moving targets, it requires a long acquisition time to achieve sufficient image quality for target localization. ... Full text Link to item Cite

Patient-specific synthetic magnetic resonance imaging generation from cone beam computed tomography for image guidance in liver stereotactic body radiation therapy.

Journal Article Precis Radiat Oncol · June 2022 OBJECTIVE: Despite its prevalence, cone beam computed tomography (CBCT) has poor soft-tissue contrast, making it challenging to localize liver tumors. We propose a patient-specific deep learning model to generate synthetic magnetic resonance imaging (MRI) ... Full text Link to item Cite

Dual-Intended Deep Learning Model for Breast Cancer Diagnosis in Ultrasound Imaging

Journal Article Cancers · June 1, 2022 Automated medical data analysis demonstrated a significant role in modern medicine, and cancer diagnosis/prognosis to achieve highly reliable and generalizable systems. In this study, an automated breast cancer screening method in ultrasound imaging is pro ... Full text Cite

A geometry-guided multi-beamlet deep learning technique for CT reconstruction.

Journal Article Biomed Phys Eng Express · May 13, 2022 Purpose. Previous studies have proposed deep-learning techniques to reconstruct CT images from sinograms. However, these techniques employ large fully-connected (FC) layers for projection-to-image domain transformation, producing large models requiring sub ... Full text Link to item Cite

Applications of Machine Learning to Improve the Clinical Viability of Compton Camera Based in vivo Range Verification in Proton Radiotherapy

Journal Article Frontiers in Physics · April 11, 2022 We studied the application of a deep, fully connected Neural Network (NN) to process prompt gamma (PG) data measured by a Compton camera (CC) during the delivery of clinical proton radiotherapy beams. The network identifies 1) recorded “bad” PG events aris ... Full text Cite

Patient-specific deep learning model to enhance 4D-CBCT image for radiomics analysis.

Journal Article Phys Med Biol · April 1, 2022 Objective.4D-CBCT provides phase-resolved images valuable for radiomics analysis for outcome prediction throughout treatment courses. However, 4D-CBCT suffers from streak artifacts caused by under-sampling, which severely degrades the accuracy of radiomic ... Full text Link to item Cite

Virtual imaging for abdominal image-guided radiotherapy

Chapter · February 28, 2022 Full text Cite

Real-time Markerless Tracking of Lung Tumors based on 2-D Fluoroscopy Imaging using Convolutional LSTM.

Journal Article IEEE Trans Radiat Plasma Med Sci · February 2022 PURPOSE: To investigate the feasibility of tracking targets in 2D fluor images using a novel deep learning network. METHODS: Our model design aims to capture the consistent motion of tumors in fluoroscopic images by neural network. Specifically, the model ... Full text Link to item Cite

The markerless lung target tracking AAPM Grand Challenge (MATCH) results.

Journal Article Med Phys · February 2022 PURPOSE: Lung stereotactic ablative body radiotherapy (SABR) is a radiation therapy success story with level 1 evidence demonstrating its efficacy. To provide real-time respiratory motion management for lung SABR, several commercial and preclinical markerl ... Full text Link to item Cite

Enhancement of 4-D Cone-Beam Computed Tomography (4D-CBCT) Using a Dual-Encoder Convolutional Neural Network (DeCNN).

Journal Article IEEE Trans Radiat Plasma Med Sci · February 2022 4D-CBCT is a powerful tool to provide respiration-resolved images for the moving target localization. However, projections in each respiratory phase are intrinsically under-sampled under the clinical scanning time and imaging dose constraints. Images recon ... Full text Link to item Cite

Prior image-guided cone-beam computed tomography augmentation from under-sampled projections using a convolutional neural network.

Journal Article Quant Imaging Med Surg · December 2021 BACKGROUND: Acquiring sparse-view cone-beam computed tomography (CBCT) is an effective way to reduce the imaging dose. However, images reconstructed by the conventional filtered back-projection method suffer from severe streak artifacts due to the projecti ... Full text Link to item Cite

Multi-contrast four-dimensional magnetic resonance imaging (MC-4D-MRI): Development and initial evaluation in liver tumor patients.

Journal Article Med Phys · December 2021 PURPOSE: To develop a novel multi-contrast four-dimensional magnetic resonance imaging (MC-4D-MRI) technique that expands single image contrast 4D-MRI to a spectrum of native and synthetic image contrasts and to evaluate its feasibility in liver tumor pati ... Full text Link to item Cite

A geometry-guided deep learning technique for CBCT reconstruction.

Journal Article Phys Med Biol · July 30, 2021 Purpose.Although deep learning (DL) technique has been successfully used for computed tomography (CT) reconstruction, its implementation on cone-beam CT (CBCT) reconstruction is extremely challenging due to memory limitations. In this study, a novel DL tec ... Full text Link to item Cite

A generative adversarial network (GAN)-based technique for synthesizing realistic respiratory motion in the extended cardiac-torso (XCAT) phantoms.

Journal Article Phys Med Biol · May 31, 2021 Objective. Synthesize realistic and controllable respiratory motions in the extended cardiac-torso (XCAT) phantoms by developing a generative adversarial network (GAN)-based deep learning technique.Methods. A motion generation model was developed using bic ... Full text Link to item Cite

A phantom study of three-dimensional conformal radiation therapy and sliding window intensity-modulated radiation therapy based on 4D dose distribution

Journal Article Chinese Journal of Radiation Oncology · March 15, 2021 Objective In this paper, based on the 4D dose distribution of the treatment plan, the effects of respiratory movement on the dose distribution of three-dimensional conformal radiation therapy (3DCRT) and sliding window intensity-modulated radiation therapy ... Full text Cite

4D radiomics: impact of 4D-CBCT image quality on radiomic analysis.

Journal Article Phys Med Biol · February 11, 2021 PURPOSE: To investigate the impact of 4D-CBCT image quality on radiomic analysis and the efficacy of using deep learning based image enhancement to improve the accuracy of radiomic features of 4D-CBCT. MATERIAL AND METHODS: In this study, 4D-CT data from 1 ... Full text Link to item Cite

Building a patient-specific model using transfer learning for four-dimensional cone beam computed tomography augmentation.

Journal Article Quant Imaging Med Surg · February 2021 BACKGROUND: We previously developed a deep learning model to augment the quality of four-dimensional (4D) cone-beam computed tomography (CBCT). However, the model was trained using group data, and thus was not optimized for individual patients. Consequentl ... Full text Link to item Cite

Respiratory deformation registration in 4D-CT/cone beam CT using deep learning.

Journal Article Quant Imaging Med Surg · February 2021 BACKGROUND: To investigate the feasibility of using a supervised convolutional neural network (CNN) to register phase-to-phase deformable vector field of lung 4D-CT/4D-cone beam CT for 4D dose accumulation, contour propagation, motion modeling, or target v ... Full text Link to item Cite

Enhancing digital tomosynthesis (DTS) for lung radiotherapy guidance using patient-specific deep learning model.

Journal Article Phys Med Biol · January 26, 2021 Digital tomosynthesis (DTS) has been proposed as a fast low-dose imaging technique for image-guided radiation therapy (IGRT). However, due to the limited scanning angle, DTS reconstructed by the conventional FDK method suffers from significant distortions ... Full text Link to item Cite

Intensity non-uniformity correction in MR imaging using residual cycle generative adversarial network.

Journal Article Physics in medicine and biology · November 2020 Correcting or reducing the effects of voxel intensity non-uniformity (INU) within a given tissue type is a crucial issue for quantitative magnetic resonance (MR) image analysis in daily clinical practice. Although having no severe impact on visual diagnosi ... Full text Cite

Adaptive respiratory signal prediction using dual multi-layer perceptron neural networks.

Journal Article Phys Med Biol · September 14, 2020 To improve the prediction accuracy of respiratory signals by adapting the multi-layer perceptron neural network (MLP-NN) model to changing respiratory signals. We have previously developed an MLP-NN to predict respiratory signals obtained from a real-time ... Full text Link to item Cite

Development of realistic multi-contrast textured XCAT (MT-XCAT) phantoms using a dual-discriminator conditional-generative adversarial network (D-CGAN).

Journal Article Phys Med Biol · March 19, 2020 Develop a machine learning-based method to generate multi-contrast anatomical textures in the 4D extended cardiac-torso (XCAT) phantom for more realistic imaging simulations. As a pilot study, we synthesize CT and CBCT textures in the chest region. For tra ... Full text Link to item Cite

Motion robust 4D-MRI sorting based on anatomic feature matching: A digital phantom simulation study.

Journal Article Radiat Med Prot · March 2020 PURPOSE: Motion artifacts induced by breathing variations are common in 4D-MRI images. This study aims to reduce the motion artifacts by developing a novel, robust 4D-MRI sorting method based on anatomic feature matching and applicable in both cine and seq ... Full text Link to item Cite

Volumetric cine magnetic resonance imaging (VC-MRI) using motion modeling, free-form deformation and multi-slice undersampled 2D cine MRI reconstructed with spatio-temporal low-rank decomposition.

Journal Article Quant Imaging Med Surg · February 2020 BACKGROUND: The purpose of this study is to improve on-board volumetric cine magnetic resonance imaging (VC-MRI) using multi-slice undersampled cine images reconstructed using spatio-temporal k-space data, patient prior 4D-MRI, motion modeling (MM) and fre ... Full text Link to item Cite

A multi-scale framework with unsupervised joint training of convolutional neural networks for pulmonary deformable image registration.

Journal Article Phys Med Biol · January 13, 2020 To achieve accurate and fast deformable image registration (DIR) for pulmonary CT, we proposed a Multi-scale DIR framework with unsupervised Joint training of Convolutional Neural Network (MJ-CNN). MJ-CNN contains three models at multi-scale levels for a c ... Full text Link to item Cite

Clinical implementation of AI technologies will require interpretable AI models.

Journal Article Med Phys · January 2020 Full text Link to item Cite

Liver synthetic CT generation based on dense-cyclegan for MRI-only treatment planning

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2020 The application of MRI significantly improves the accuracy and reliability of target delineation for many disease sites in radiotherapy due to its superior soft tissue contrast as compared to CT. However, MRI data do not contain the electron density inform ... Full text Cite

Augmentation of CBCT Reconstructed From Under-Sampled Projections Using Deep Learning.

Journal Article IEEE Trans Med Imaging · November 2019 Edges tend to be over-smoothed in total variation (TV) regularized under-sampled images. In this paper, symmetric residual convolutional neural network (SR-CNN), a deep learning based model, was proposed to enhance the sharpness of edges and detailed anato ... Full text Link to item Cite

Daily edge deformation prediction using an unsupervised convolutional neural network model for low dose prior contour based total variation CBCT reconstruction (PCTV-CNN).

Journal Article Biomed Phys Eng Express · October 2019 PURPOSE: Previously we developed a PCTV method to enhance the edge sharpness for low-dose CBCT reconstruction. However, the iterative deformable registration method used for deforming edges from planning-CT to on-board CBCT is time-consuming and user-depen ... Full text Link to item Cite

SPARE: Sparse-view reconstruction challenge for 4D cone-beam CT from a 1-min scan.

Journal Article Med Phys · September 2019 PURPOSE: Currently, four-dimensional (4D) cone-beam computed tomography (CBCT) requires a 3-4 min full-fan scan to ensure usable image quality. Recent advancements in sparse-view 4D-CBCT reconstruction have opened the possibility to reduce scan time and do ... Full text Link to item Cite

Quantifying Imaging Radiation to Patient from Optimized Procedures with LIVE System during SBRT Lung Treatment

Conference International Journal of Radiation Oncology*Biology*Physics · September 2019 Full text Cite

PET Radiotherapy Response Assessment Using Encoder-Decoder Convolutional Neural Network and Pre-treatment Information: A Feasibility of Oropharynx Cancer IMRT

Conference International Journal of Radiation Oncology*Biology*Physics · September 2019 Full text Cite

Predicting real-time 3D deformation field maps (DFM) based on volumetric cine MRI (VC-MRI) and artificial neural networks for on-board 4D target tracking: a feasibility study.

Journal Article Phys Med Biol · August 21, 2019 To predict real-time 3D deformation field maps (DFMs) using Volumetric Cine MRI (VC-MRI) and adaptive boosting and multi-layer perceptron neural network (ADMLP-NN) for 4D target tracking. One phase of a prior 4D-MRI is set as the prior phase, MRIprior. Pri ... Full text Link to item Cite

MRI-based treatment planning for liver stereotactic body radiotherapy: validation of a deep learning-based synthetic CT generation method.

Journal Article Br J Radiol · August 2019 OBJECTIVE: The purpose of this work is to develop and validate a learning-based method to derive electron density from routine anatomical MRI for potential MRI-based SBRT treatment planning. METHODS: We proposed to integrate dense block into cycle generati ... Full text Link to item Cite

MRI-based treatment planning for proton radiotherapy: dosimetric validation of a deep learning-based liver synthetic CT generation method.

Journal Article Phys Med Biol · July 16, 2019 Magnetic resonance imaging (MRI) has been widely used in combination with computed tomography (CT) radiation therapy because MRI improves the accuracy and reliability of target delineation due to its superior soft tissue contrast over CT. The MRI-only trea ... Full text Link to item Cite

Enhancing liver tumor localization accuracy by prior-knowledge-guided motion modeling and a biomechanical model.

Journal Article Quant Imaging Med Surg · July 2019 BACKGROUND: Pre-treatment liver tumor localization remains a challenging task for radiation therapy, mostly due to the limited tumor contrast against normal liver tissues, and the respiration-induced liver tumor motion. Recently, we developed a biomechanic ... Full text Link to item Cite

Low dose cone-beam computed tomography reconstruction via hybrid prior contour based total variation regularization (hybrid-PCTV).

Journal Article Quant Imaging Med Surg · July 2019 BACKGROUND: Previously, we developed a prior contour based total variation (PCTV) method to use edge information derived from prior images for edge enhancement in low-dose cone-beam computed tomography (CBCT) reconstruction. However, the accuracy of edge e ... Full text Link to item Cite

Technical Note: Imaging dose resulting from optimized procedures with limited-angle intrafractional verification system during stereotactic body radiation therapy lung treatment.

Journal Article Med Phys · June 2019 PURPOSE: The limited-angle intrafractional verification (LIVE) system was developed to track tumor movement during stereotactic body radiation therapy (SBRT). However, the four-dimensional (4D) MV/kV imaging procedure results in additional radiation dose t ... Full text Link to item Cite