Therapeutic Radiology

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 CyberKnife (CK) plans. The purpose of this study is to perform a complexity analysis of CK MLC plans, adapting and computing complexity indices previously defined for IMRT plans. Metrics were used to compare the complexity of plans created by two optimizati...

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 consistent detection. In this study, we aim to improve the accuracy of automated brain metastasis (BM) detection methods using a novel asymmetric UNet (asym-UNet) architecture. An end-to-end asymmetric 3D-UNet architecture, with two down-sampling arms ...

Performance of deep learning synthetic CTs for MR-only brain radiation therapy.

PURPOSE: To evaluate the dosimetric and image-guided radiation therapy (IGRT) performance of a novel generative adversarial network (GAN) generated sy...

Deep learning-based digitization of prostate brachytherapy needles in ultrasound images.

PURPOSE: To develop, and evaluate the performance of, a deep learning-based three-dimensional (3D) convolutional neural network (CNN) artificial intel...

Automatic multi-needle localization in ultrasound images using large margin mask RCNN for ultrasound-guided prostate brachytherapy.

Multi-needle localization in ultrasound (US) images is a crucial step of treatment planning for US-guided prostate brachytherapy. However, current com...

Integration of AI and Machine Learning in Radiotherapy QA.

The use of machine learning and other sophisticated models to aid in prediction and decision making has become widely popular across a breadth of disc...

Multi-view radiomics and dosiomics analysis with machine learning for predicting acute-phase weight loss in lung cancer patients treated with radiotherapy.

We propose a multi-view data analysis approach using radiomics and dosiomics (R&D) texture features for predicting acute-phase weight loss (WL) in lun...

Automatic IMRT planning via static field fluence prediction (AIP-SFFP): a deep learning algorithm for real-time prostate treatment planning.

The purpose of this work was to develop a deep learning (DL) based algorithm, Automatic intensity-modulated radiotherapy (IMRT) Planning via Static Fi...

Deep learning-based metal artifact reduction using cycle-consistent adversarial network for intensity-modulated head and neck radiation therapy treatment planning.

PURPOSE: To develop a deep learning-based metal artifact reduction (DL-MAR) method using unpaired data and to evaluate its dosimetric impact in head a...

Knowledge Models as Teaching Aid for Training Intensity Modulated Radiation Therapy Planning: A Lung Cancer Case Study.

Artificial intelligence (AI) employs knowledge models that often behave as a black-box to the majority of users and are not designed to improve the s...

Radiomic analysis of magnetic resonance imaging predicts brain metastases velocity and clinical outcome after upfront radiosurgery.

BACKGROUND: Brain metastasis velocity (BMV) predicts outcomes after initial distant brain failure (DBF) following upfront stereotactic radiosurgery (S...

Deep learning-based survival analysis for brain metastasis patients with the national cancer database.

PURPOSE: Prognostic indices such as the Brain Metastasis Graded Prognostic Assessment have been used in clinical settings to aid physicians and patien...

A knowledge-based intensity-modulated radiation therapy treatment planning technique for locally advanced nasopharyngeal carcinoma radiotherapy.

BACKGROUND: To investigate the feasibility of a knowledge-based automated intensity-modulated radiation therapy (IMRT) planning technique for locally ...

Using deep learning to predict beam-tunable Pareto optimal dose distribution for intensity-modulated radiation therapy.

PURPOSE: Many researchers have developed deep learning models for predicting clinical dose distributions and Pareto optimal dose distributions. Models...

Needle tip force estimation by deep learning from raw spectral OCT data.

PURPOSE: Needle placement is a challenging problem for applications such as biopsy or brachytherapy. Tip force sensing can provide valuable feedback f...

A deep learning method for real-time intraoperative US image segmentation in prostate brachytherapy.

PURPOSE: This paper addresses the detection of the clinical target volume (CTV) in transrectal ultrasound (TRUS) image-guided intraoperative for perma...

Deep learning applications in automatic needle segmentation in ultrasound-guided prostate brachytherapy.

PURPOSE: High-Dose-Rate (HDR) brachytherapy is one of the most effective ways to treat the prostate cancer, which is the second most common cancer in ...

Feasibility and analysis of CNN-based candidate beam generation for robotic radiosurgery.

PURPOSE: Robotic radiosurgery offers the flexibility of a robotic arm to enable high conformity to the target and a steep dose gradient. However, trea...

A new multimodal, image-guided, robot-assisted, interstitial brachytherapy for the treatment of head and neck tumors-A preliminary study.

BACKGROUND: Interstitial brachytherapy (BT) is becoming an accepted treatment option for head and neck cancer patients for whom surgery poses high ris...

Machine Segmentation of Pelvic Anatomy in MRI-Assisted Radiosurgery (MARS) for Prostate Cancer Brachytherapy.

PURPOSE: To investigate machine segmentation of pelvic anatomy in magnetic resonance imaging (MRI)-assisted radiosurgery (MARS) for prostate cancer us...