Accounting for uncertainties in the position of anatomical barriers used to define the clinical target volume.

The definition of the clinical target volume (CTV) is becoming the weakest link in the radiotherapy chain. CTV definition consensus guidelines include the geometric expansion beyond the visible gross tumor volume, while avoiding anatomical barriers. In a previous publication we described how to implement these consensus guidelines using deep learning and graph search techniques in a computerized CTV auto-delineation process.

Automatic Segmentation Using Deep Learning to Enable Online Dose Optimization During Adaptive Radiation Therapy of Cervical Cancer.

Purpose: This study investigated deep learning models for automatic segmentation to support the development of daily online dose optimization strategies, eliminating the need for internal target volume expansions and thereby reducing toxicity events of intensity modulated radiation therapy for cervical cancer.

Methods And Materials: The cervix-uterus, vagina, parametrium, bladder, rectum, sigmoid, femoral heads, kidneys, spinal cord, and bowel bag were delineated on 408 computed tomography (CT) scans from patients treated at MD Anderson Cancer Center (n = 214), Polyclinique Bordeaux Nord Aquitaine (n = 30), and enrolled in a Medical Image Computing & Computer Assisted Intervention challenge (n = 3). The data were divided into 255 training, 61 validation, 62 internal test, and 30 external test CT scans.

Life of superoxide in aprotic Li-O₂ battery electrolytes: simulated solvent and counter-ion effects.

Li-air batteries ideally make use of oxygen from the atmosphere and metallic lithium to reversibly drive the reaction 2Li + O2↔ Li2O2. Conceptually, energy throughput is high and material use is efficient, but practically many material challenges still remain. It is of particular interest to control the electrolyte environment of superoxide (O2*(-)) to promote or hinder specific reaction mechanisms.