Background: This paper presents an improved radar-based imaging system for breast cancer detection that features p-slot ultrawideband antennae in a 32-array set-up. The improved reconstruction algorithm incorporates the phase coherence factor (PCF) into the conventional delay and sum (DAS) beamforming algorithm, thus effectively suppressing noise arising from the side- and gratinglobe interferences.
Methods: The system is tested by using several breast models fabricated from chemical mixtures formulated on the basis of realistic human tissues. Each model is placed in a hemispherical breast radome that was fabricated from polylactide material and surrounded by 32 p-slot antennae mounted in four concentric layers. These antennae are connected to an 8.5 GHz vector network analyser through two 16-channel multiplexers that automatically switch different combinations of transmitter and receiver pairs in a sequential manner.
Results: The system can accurately detect 5 mm tumours in a complex and homogeneously dense 3D breast model with an average signal-to-clutter ratio and full-width half-maximum of 7.0 dB and 2.3 mm, respectively. These values are more competitive than the values of other beamforming algorithms, even with contrasts as low as 1:2.
Conclusion: The proposed PCF-weighted DAS is the best-performing algorithm amongst the tested beamforming techniques. This research paves the way for a clinical trial involving human subjects. Our laboratory is planning such a trial as part of future work.
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http://dx.doi.org/10.2174/1573405618666220304093447 | DOI Listing |
J Correct Health Care
January 2025
Departments of Medicine and Pediatrics, Warren Alpert Medical School at Brown University, Providence, Rhode Island, USA.
Limited data exist on cancer screening in carceral facilities. This study evaluates the feasibility and outcomes of a population-based lung cancer screening initiative in a carceral setting. This is a retrospective review of a lung cancer screening event at the Rhode Island Department of Corrections.
View Article and Find Full Text PDFJAMA Netw Open
January 2025
Ahmanson Translational Theranostics Division, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles.
Importance: The phase 3 randomized EMBARK trial evaluated enzalutamide with or without leuprolide in high-risk nonmetastatic hormone-sensitive prostate cancer. Eligibility relied on conventional imaging, which underdetects metastatic disease compared with prostate-specific membrane antigen-positron emission tomography (PSMA-PET).
Objective: To describe the staging information obtained by PSMA-PET/computed tomography (PSMA-PET/CT) in a patient cohort eligible for the EMBARK trial.
Int J Comput Assist Radiol Surg
January 2025
Department of Radiology, University of Chicago, Chicago, IL, USA.
Purpose: Thyroid nodules are common, and ultrasound-based risk stratification using ACR's TIRADS classification is a key step in predicting nodule pathology. Determining thyroid nodule contours is necessary for the calculation of TIRADS scores and can also be used in the development of machine learning nodule diagnosis systems. This paper presents the development, validation, and multi-institutional independent testing of a machine learning system for the automatic segmentation of thyroid nodules on ultrasound.
View Article and Find Full Text PDFAnn Surg Oncol
January 2025
Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA.
Background: Flat epithelial atypia (FEA), a rare breast proliferative lesion, is often diagnosed following core biopsy (CB) of mammographic microcalcifications. In the prospective multi-institution TBCRC 034 trial, we investigate the upgrade rate to ductal carcinoma in situ (DCIS) or invasive cancer following excision for patients diagnosed with FEA on CB.
Patients And Methods: Patients with a breast imaging reporting and data system (BI-RADS) ≤ 4 imaging abnormality and a concordant CB diagnosis of FEA were identified for excision.
Front Optoelectron
January 2025
Institution of Physics, Saratov State University, Saratov, 410012, Russia.
Current study presents an advanced method for improving the visualization of subsurface blood vessels using laser speckle contrast imaging (LSCI), enhanced through principal component analysis (PCA) filtering. By combining LSCI and laser speckle entropy imaging with PCA filtering, the method effectively separates static and dynamic components of the speckle signal, significantly improving the accuracy of blood flow assessments, even in the presence of static scattering layers located above and below the vessel. Experiments conducted on optical phantoms, with the vessel depths ranging from 0.
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