Brain Cancer

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 synthetic CT (synCT) in the brain and compare its performance for clinical use including conventional brain radiotherapy, cranial stereotactic radiosurgery (SRS), planar, and volumetric IGRT.

Diagnostic value of deep learning reconstruction for radiation dose reduction at abdominal ultra-high-resolution CT.

OBJECTIVES: We evaluated lower dose (LD) hepatic dynamic ultra-high-resolution computed tomography (U-HRCT) images reconstructed with deep learning reconstruction (DLR), hybrid iterative reconstruction (hybrid-IR), or model-based IR (MBIR) in comparison with standard-dose (SD) U-HRCT images reconstructed with hybrid-IR as the reference standard to identify the method that allowed for the greatest ...

Data-driven dose calculation algorithm based on deep U-Net.

Accurate and efficient dose calculation is an important prerequisite to ensure the success of radiation therapy. However, all the dose calculation alg...

Feasibility of automated planning for whole-brain radiation therapy using deep learning.

PURPOSE: The purpose of this study was to develop automated planning for whole-brain radiation therapy (WBRT) using a U-net-based deep-learning model ...

A physics-guided modular deep-learning based automated framework for tumor segmentation in PET.

An important need exists for reliable positron emission tomography (PET) tumor-segmentation methods for tasks such as PET-based radiation-therapy plan...

Deep learning-based real-time volumetric imaging for lung stereotactic body radiation therapy: a proof of concept study.

Due to the inter- and intra- variation of respiratory motion, it is highly desired to provide real-time volumetric images during the treatment deliver...

Perceptions of Canadian radiation oncologists, radiation physicists, radiation therapists and radiation trainees about the impact of artificial intelligence in radiation oncology - national survey.

BACKGROUND: Artificial Intelligence (AI) is making a continuous progression into the field of Radiation Oncology in Canada and globally. While this fi...

Weight-bearing CT in foot and ankle pathology.

Cone-beam scanners (CBCT) enable CT to be performed under weight-bearing - notably for the foot and ankle. The technology is not new: it has been used...

Improved Glioma Grading Using Deep Convolutional Neural Networks.

BACKGROUND AND PURPOSE: Accurate determination of glioma grade leads to improved treatment planning. The criterion standard for glioma grading is inva...

Impacts of speciation and extinction measured by an evolutionary decay clock.

The hypothesis that destructive mass extinctions enable creative evolutionary radiations (creative destruction) is central to classic concepts of macr...

Analyzing magnetic resonance imaging data from glioma patients using deep learning.

The quantitative analysis of images acquired in the diagnosis and treatment of patients with brain tumors has seen a significant rise in the clinical ...

Meningioma Consistency Can Be Defined by Combining the Radiomic Features of Magnetic Resonance Imaging and Ultrasound Elastography. A Pilot Study Using Machine Learning Classifiers.

BACKGROUND: The consistency of meningioma is a factor that may influence surgical planning and the extent of resection. The aim of our study is to dev...

Predicting spatial esophageal changes in a multimodal longitudinal imaging study via a convolutional recurrent neural network.

Acute esophagitis (AE) occurs among a significant number of patients with locally advanced lung cancer treated with radiotherapy. Early prediction of ...

Estimating Local Cellular Density in Glioma Using MR Imaging Data.

BACKGROUND AND PURPOSE: Increased cellular density is a hallmark of gliomas, both in the bulk of the tumor and in areas of tumor infiltration into sur...

Artificial intelligence in image reconstruction: The change is here.

Innovations in CT have been impressive among imaging and medical technologies in both the hardware and software domain. The range and speed of CT scan...

Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning.

Differentiating pseudoprogression from true tumor progression has become a significant challenge in follow-up of diffuse infiltrating gliomas, particu...

Improving Image Quality and Reducing Radiation Dose for Pediatric CT by Using Deep Learning Reconstruction.

Background CT deep learning reconstruction (DLR) algorithms have been developed to remove image noise. How the DLR affects image quality and radiation...

Dose-dependent effects of ultrasound therapy on hepatocellular carcinoma.

Non-invasive ischemic cancer therapy requires reduced blood flow whereas drug delivery and radiation therapy require increased tumor perfusion for a b...

Using Auto-Segmentation to Reduce Contouring and Dose Inconsistency in Clinical Trials: The Simulated Impact on RTOG 0617.

PURPOSE: Contouring inconsistencies are known but understudied in clinical radiation therapy trials. We applied auto-contouring to the Radiation Thera...

Machine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopy.

Complete resection of the tumor is important for survival in glioma patients. Even if the gross total resection was achieved, left-over micro-scale ti...