Publications by authors named "M Grazia Anastasio"
In certain three-dimensional (3D) applications of photoacoustic computed tomography (PACT), including \textit{in vivo} breast imaging, hemispherical measurement apertures that enclose the object within their convex hull are employed for data acquisition. Data acquired with such measurement geometries are referred to as \textit{half-scan} data, as only half of a complete spherical measurement aperture is employed. Although previous studies have demonstrated that half-scan data can uniquely and stably reconstruct the sought-after object, no closed-form reconstruction formula for use with half-scan data has been reported.
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- qPACT is an advanced medical imaging technique that aims to provide detailed images of important physiological metrics like hemoglobin levels and oxygen saturation, but its image reconstruction involves complex, non-linear mathematical challenges.
- There is currently no standardized design for qPACT systems, leading to uncertainty about which system designs are optimal for different medical applications.
- This research introduces a new computational method for optimizing the design of qPACT systems using the Bayesian Cramér-Rao bound, demonstrating its effectiveness through numerical simulations, marking a significant advancement in the field of imaging governed by partial differential equations.
Background: The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report.
Purpose: The goal of this challenge was to promote the development of deep generative models for medical imaging and to emphasize the need for their domain-relevant assessments via the analysis of relevant image statistics.
Methods: As part of this Grand Challenge, a common training dataset and an evaluation procedure was developed for benchmarking deep generative models for medical image synthesis.
Article Synopsis
- Traditional metrics for measuring digital mammography and tomosynthesis image quality often fail to predict clinical performance, leading to the use of a more realistic breast phantom with randomized microcalcifications and deep learning for evaluation.
- The research focused on developing a methodology that combines an anthropomorphic breast phantom, a specific microcalcification detection task, and a convolutional neural network for automated performance assessment.
- Results showed that the ability to detect microcalcifications varied with the amount of radiation exposure, indicating that the new method is effective for evaluating different mammography technologies.
Objective: To summarize practice patterns and outcomes among patients with non-myoinvasive high-grade (formerly stage IA, now stage IC) endometrial cancer.
Methods: We conducted a systematic search using MEDLINE, Embase, Cochrane, Web of Science, and ClinicalTrials.gov databases from inception to May 8, 2024 to identify studies reporting on treatment and outcomes of non-myoinvasive high-grade endometrial cancer.