Women's Cancer Center, Dana-Farber/Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
Published: September 2015
AI Article Synopsis
Article Abstract
Radio-guided localization (RGL) for identifying occult breast lesions has been widely accepted as an alternative technique to other localization methods, including those using wire guidance. An appropriate phantom model would be an invaluable tool for practitioners interested in learning the technique of RGL prior to clinical application. The aim of this study was to devise an inexpensive and reproducible training phantom model for RGL. We developed a simple RGL phantom model imitating an occult breast lesion from inexpensive supplies including a pimento olive, a green pea and a turkey breast. The phantom was constructed for a total cost of less than $20 and prepared in approximately 10 min. After the first model's construction, we constructed approximately 25 additional models and demonstrated that the model design was easily reproducible. The RGL phantom is a time- and cost-effective model that accurately simulates the RGL technique for non-palpable breast lesions. Future studies are warranted to further validate this model as an effective teaching tool.
Improved surgical skill is generally associated with improved patient outcomes, although assessment is subjective, labour intensive, and requires domain-specific expertise. Automated data-driven metrics can alleviate these difficulties, as demonstrated by existing machine learning instrument tracking models. However, these models are tested on limited datasets of laparoscopic surgery, with a focus on isolated tasks and robotic surgery.
We report a method to directly 3-D print complex heterogeneous optical phantoms with programmable tissue-mimicking absorption and scattering properties. The proposed approach utilizes commercially available multi-color mixing extruders and off-the-shelf polylactic acid (PLA) filaments, making this technique low-cost and broadly accessible. We systematically characterized optical properties, including both absorption and reduced scattering coefficients, at a wide range of mixing ratios of gray, white and translucent filaments and validated our hypothesis of a linear-mixing model between the filament mixing ratios and the resulting optical properties.
Vacuum-assisted delivery (VAD) uses a vacuum cup on the fetal scalp to apply traction during uterine contractions, assisting complicated vaginal deliveries. Despite its widespread use, VAD presents a higher risk of neonatal morbidity compared to natural vaginal delivery and biomechanical evidence for safe VAD traction forces is still limited. The aim of this study is to develop and assess the feasibility of an experimental VAD testing setup, and investigate the impact of traction forces on fetal brain deformation.
Portable head CT images often suffer motion artifacts due to the prolonged scanning time and critically ill patients who are unable to hold still. Image-domain motion correction is attractive for this application as it does not require CT projection data. This paper describes and evaluates a generative model based on conditional diffusion to correct motion artifacts in portable head CT scans.
Background: Due to the low signal-to-noise ratio (SNR) and the limited number of b-values, precise parameter estimation of intravoxel incoherent motion (IVIM) imaging remains an open issue to date, especially for brain imaging where the relatively small difference between D and D easily leads to outliers and obvious graininess in estimated results.
Purpose: To propose a synthetic data driven supervised learning method (SDD-IVIM) for improving precision and noise robustness in IVIM parameter estimation without relying on real-world data for neural network training.
Methods: On account of the absence of standard IVIM parametric maps from real-world data, a novel model-based method for generating synthetic human brain IVIM data was introduced.
