AI Medical Compendium Topic:
Radiotherapy Planning, Computer-Assisted

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Dosimetric Implications of Residual Tracking Errors During Robotic SBRT of Liver Metastases.

International journal of radiation oncology, biology, physics
PURPOSE: Although the metric precision of robotic stereotactic body radiation therapy in the presence of breathing motion is widely known, we investigated the dosimetric implications of breathing phase-related residual tracking errors.

Evaluation of a Machine-Learning Algorithm for Treatment Planning in Prostate Low-Dose-Rate Brachytherapy.

International journal of radiation oncology, biology, physics
PURPOSE: This work presents the application of a machine learning (ML) algorithm to automatically generate high-quality, prostate low-dose-rate (LDR) brachytherapy treatment plans. The ML algorithm can mimic characteristics of preoperative treatment ...

A machine learning tool for re-planning and adaptive RT: A multicenter cohort investigation.

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 predict patients who would benefit from adaptive radiotherapy (ART) and re-planning intervention based on machine learning from anatomical and dosimetric variations in a retrospective dataset.

Robotic ultrasound-guided SBRT of the prostate: feasibility with respect to plan quality.

International journal of computer assisted radiology and surgery
PURPOSE: Advances in radiation therapy delivery systems have enabled motion compensated SBRT of the prostate. A remaining challenge is the integration of fast, non-ionizing volumetric imaging. Recently, robotic ultrasound has been proposed as an intr...

High resolution ion chamber array delivery quality assurance for robotic radiosurgery: Commissioning and validation.

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: High precision radiosurgery demands comprehensive delivery-quality-assurance techniques. The use of a liquid-filled ion-chamber-array for robotic-radiosurgery delivery-quality-assurance was investigated and validated using several test scena...

Inverse treatment planning for spinal robotic radiosurgery: an international multi-institutional benchmark trial.

Journal of applied clinical medical physics
Stereotactic radiosurgery (SRS) is the accurate, conformal delivery of high-dose radiation to well-defined targets while minimizing normal structure doses via steep dose gradients. While inverse treatment planning (ITP) with computerized optimization...

Liver vessel segmentation based on extreme learning machine.

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)
Liver-vessel segmentation plays an important role in vessel structure analysis for liver surgical planning. This paper presents a liver-vessel segmentation method based on extreme learning machine (ELM). Firstly, an anisotropic filter is used to remo...

A dosimetric comparison of real-time adaptive and non-adaptive radiotherapy: A multi-institutional study encompassing robotic, gimbaled, multileaf collimator and couch tracking.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PURPOSE: A study of real-time adaptive radiotherapy systems was performed to test the hypothesis that, across delivery systems and institutions, the dosimetric accuracy is improved with adaptive treatments over non-adaptive radiotherapy in the presen...

A machine learning approach to the accurate prediction of multi-leaf collimator positional errors.

Physics in medicine and biology
Discrepancies between planned and delivered movements of multi-leaf collimators (MLCs) are an important source of errors in dose distributions during radiotherapy. In this work we used machine learning techniques to train models to predict these disc...