AIMC Topic: Radiosurgery

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

Medical physics
PURPOSE: Robotic radiosurgery offers the flexibility of a robotic arm to enable high conformity to the target and a steep dose gradient. However, treatment planning becomes a computationally challenging task as the search space for potential beam dir...

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

International journal of radiation oncology, biology, physics
PURPOSE: To investigate machine segmentation of pelvic anatomy in magnetic resonance imaging (MRI)-assisted radiosurgery (MARS) for prostate cancer using prostate brachytherapy MRIs acquired with different pulse sequences and image contrasts.

Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapy.

Medical physics
PURPOSE: Radiation therapy (RT) is prescribed for curative and palliative treatment for around 50% of patients with solid tumors. Radiation-induced toxicities of healthy organs accompany many RTs and represent one of the main limiting factors during ...

Deep Learning model for markerless tracking in spinal SBRT.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Stereotactic Body Radiation Therapy (SBRT), alternatively termed Stereotactic ABlative Radiotherapy (SABR) or Stereotactic RadioSurgery (SRS), delivers high dose with a sub-millimeter accuracy. It requires meticulous precautions on positioning, as sh...

Radiomics and deep learning in lung cancer.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
Lung malignancies have been extensively characterized through radiomics and deep learning. By providing a three-dimensional characterization of the lesion, models based on radiomic features from computed tomography (CT) and positron-emission tomograp...

Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data.

Radiation oncology (London, England)
INTRODUCTION: Deep learning-based algorithms have demonstrated enormous performance in segmentation of medical images. We collected a dataset of multiparametric MRI and contour data acquired for use in radiosurgery, to evaluate the performance of dee...

Computer-aided Detection of Brain Metastases in T1-weighted MRI for Stereotactic Radiosurgery Using Deep Learning Single-Shot Detectors.

Radiology
Background Brain metastases are manually identified during stereotactic radiosurgery (SRS) treatment planning, which is time consuming and potentially challenging. Purpose To develop and investigate deep learning (DL) methods for detecting brain meta...

Hypofractionated Robotic Stereotactic Radiosurgery for Vagal Paragangliomas: A Novel Treatment Strategy for Cranial Nerve Preservation.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: To provide the first description of hypofractionated stereotactic radiosurgery (SRS) and evaluate tumor control and safety for vagal paragangliomas (VPs), which begin at the skull base but often have significant extracranial extension.

Performance assessment of a new optimization system for robotic SBRT MLC-based plans.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: To assess the performance of a new optimization system, VOLO, for CyberKnife MLC-based SBRT plans in comparison with the existing Sequential optimizer.