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Deep inspiration breath hold (DIBH) with surface supervising is a common technique for cardiac dose reduction in left breast cancer radiotherapy. Surface supervision accuracy relies on the characteristics of surface region. In this study, a convolutional neural network (CNN) based automatic region-of-interest (ROI) selection method was proposed to select an optimal surface ROI for DIBH surface monitoring. The curvature entropy and the normal of each vertex on the breast cancer patient surface were calculated and formed as representative maps for ROI selection learning. 900 ROIs were randomly extracted from each patient's surface representative map, and the corresponding rigid ROI registration errors (REs) were calculated. The VGG-16 (a 16-layer network structure developed by Visual Geometry Group(VGG) from University of Oxford) pre-trained on a large natural image database ImageNet were fine-tuned using 27 thousand extracted ROIs and the corresponding REs from thirty patients. The RE prediction accuracy of the trained model was validated on additional ten patients. Satisfactory RE predictive accuracies were achieved with the root mean square error (RMSE)/mean absolute error (MAE) smaller than 1 mm/0.7 mm in translations and 0.45°/0.35° in rotations, respectively. The REs of the model selected ROIs on ten testing cases is close to the minimal predicted RE with mean RE differences  <1 mm and  <0.5° for translation and rotation, respectively. The proposed RE predictive model can be utilized for selecting a quasi-optimal ROI in left breast cancer DIBH radiotherapy (DIBH-RT). [ABSTRACT FROM AUTHOR]

Accurate dose delivery in stereotactic partial breast irradiation (S-PBI) is challenging because of the target position uncertainty caused by breast deformation, the target volume changes caused by lumpectomy cavity shrinkage, and the target delineation uncertainty on simulation computed tomography (CT) images caused by poor soft tissue contrast. We have developed a volumetric ultrasound tomography (UST) image guidance system for prone position S-PBI. The system is composed of a novel 3D printed rotation water tank, a patient-specific resin breast immobilization cup, and a 1D array ultrasound transducer. Coronal 2D US images were acquired in 5° increments over a 360° range, and planes were acquired every 2 mm in elevation. A super-compounding technique was used to reconstruct the image volume. The image quality of UST was evaluated with a BB-1 breast phantom and BioZorb surgical marker, and the results revealed that UST offered better soft tissue contrast than CT and similar image quality to MR. In the evaluated plane, the size and location of five embedded objects were measured and compared to MR, which is considered as the ground truth. Objects’ diameters and the distances between objects in UST differ by approximately 1 to 2 mm from those in MR, which showed that UST offers the image quality required for S-PBI. In future work we will develop a robotic system that will be ultimately implemented in the clinic. [ABSTRACT FROM AUTHOR]