Therapeutic Radiology

Personalized brachytherapy dose reconstruction using deep learning.

BACKGROUND AND PURPOSE: Accurate calculation of the absorbed dose delivered to the tumor and normal tissues improves treatment gain factor, which is the major advantage of brachytherapy over external radiation therapy. To address the simplifications of TG-43 assumptions that ignore the dosimetric impact of medium heterogeneities, we proposed a deep learning (DL)-based approach, which improves the ...

Applications of machine and deep learning to patient-specific IMRT/VMAT quality assurance.

In order to deliver accurate and safe treatment to cancer patients in radiation therapy using advanced techniques such as intensity modulated radiation therapy (IMRT) and volumetric-arc radiation therapy (VMAT), patient specific quality assurance (QA) should be performed before treatment. IMRT/VMAT dose measurements in a phantom using various devices have been clinically adopted as standard method...

A hierarchical deep reinforcement learning framework for intelligent automatic treatment planning of prostate cancer intensity modulated radiation therapy.

We have previously proposed an intelligent automatic treatment planning (IATP) framework that builds a virtual treatment planner network (VTPN) to ope...

Independent verification of brachytherapy treatment plan by using deep learning inference modeling.

This study aims to develop a deep learning-based strategy for treatment plan check and verification of high-dose rate (HDR) brachytherapy. A deep neur...

Male pelvic multi-organ segmentation on transrectal ultrasound using anchor-free mask CNN.

PURPOSE: Current prostate brachytherapy uses transrectal ultrasound images for implant guidance, where contours of the prostate and organs-at-risk are...

A novel multi-DoF surgical robotic system for brachytherapy on liver tumor: Design and control.

PURPOSE: Radioactive seed implantation is an effective invasive treatment method for malignant liver tumors in hepatocellular carcinomas. However, cha...

Artificial intelligence in brachytherapy: a summary of recent developments.

Artificial intelligence (AI) applications, in the form of machine learning and deep learning, are being incorporated into practice in various aspects ...

Deep learning-based auto-segmentation of organs at risk in high-dose rate brachytherapy of cervical cancer.

BACKGROUND AND PURPOSE: Delineation of organs at risk (OARs), such as the bladder, rectum and sigmoid, plays an important role in the delivery of opti...

Image-guided robots for low dose rate prostate brachytherapy: Perspectives on safety in design and use.

BACKGROUND: Image-guided brachytherapy (BT) robots can be used to assist urologists during seed implantation, thereby improving therapeutic effects. H...

Deep learning dose prediction for IMRT of esophageal cancer: The effect of data quality and quantity on model performance.

PURPOSE: To investigate the effect of data quality and quantity on the performance of deep learning (DL) models, for dose prediction of intensity-modu...

DeepDose: a robust deep learning-based dose engine for abdominal tumours in a 1.5 T MRI radiotherapy system.

We present a robust deep learning-based framework for dose calculations of abdominal tumours in a 1.5 T MRI radiotherapy system. For a set of patient ...

Robotic assisted CyberKnife radiosurgery for the treatment of choroidal metastasis.

PURPOSE: Choroidal metastases occur in many patients with systemic cancer and limit quality of life due to visual deterioration or pain. The limited p...

Deep learning-augmented radiotherapy visualization with a cylindrical radioluminescence system.

This study aims to demonstrate a low-cost camera-based radioluminescence imaging system (CRIS) for high-quality beam visualization that encourages acc...

Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery.

Artificial intelligence (AI) has been applied with considerable success in the fields of radiology, pathology, and neurosurgery. It is expected that A...

Simplifying Tumor Volume Estimation from Linear Dimensions for Intra-Cranial Lesions Treated with Stereotactic Radiosurgery.

AIMS: This study aims to derive simple yet robust formula(s) for the calculation of cranial tumor volume using linear tumor dimensions in anterioposte...

Machine learning and registration for automatic seed localization in 3D US images for prostate brachytherapy.

PURPOSE: New radiation therapy protocols, in particular adaptive, focal or boost brachytherapy treatments, require determining precisely the position ...

Detecting MLC modeling errors using radiomics-based machine learning in patient-specific QA with an EPID for intensity-modulated radiation therapy.

PURPOSE: We sought to develop machine learning models to detect multileaf collimator (MLC) modeling errors with the use of radiomic features of fluenc...

Application of deep learning for automatic segmentation of brain tumors on magnetic resonance imaging: a heuristic approach in the clinical scenario.

PURPOSE: Accurate brain tumor segmentation on magnetic resonance imaging (MRI) has wide-ranging applications such as radiosurgery planning. Advances i...

Robotic MLC-based plans: A study of plan complexity.

PURPOSE: The utility of complexity metrics has been assessed for IMRT and VMAT treatment plans, but this analysis has never been performed for CyberKn...

Automatic detection and segmentation of multiple brain metastases on magnetic resonance image using asymmetric UNet architecture.

Detection of brain metastases is a paramount task in cancer management due both to the number of high-risk patients and the difficulty of achieving co...