Using a triple wavelength (670 nm, 785 nm, 843/884 nm) scanning laser-pulse mammograph we recorded craniocaudal and mediolateral projection optical mammograms of 154 patients, suspected of having breast cancer. From distributions of times of flight of photons recorded at typically 1000-2000 scan positions, optical mammograms were derived displaying (inverse) photon counts in selected time windows, absorption and reduced scattering coefficients or total haemoglobin concentration and blood oxygen saturation. Optical mammograms were analysed by comparing them with x-ray and MR mammograms, including results of histopathology, attributing a subjective visibility score to each tumour assessed. Out of 102 histologically confirmed tumours, 72 tumours were detected retrospectively in both optical projection mammograms, in addition 20 cases in one projection only, whereas 10 tumours were not detectable in any projection. Tumour contrast and contrast-to-noise ratios of mammograms of the same breast, but derived from measured DTOFs by various methods were quantitatively compared. On average, inverse photon counts in selected time windows, including total photon counts, provide highest tumour contrast and contrast-to-noise ratios. Based on the results of the present study we developed a multi-wavelength, multi-projection scanning time-domain optical mammograph with improved spectral and spatial (angular) sampling, that allows us to record entire mammograms simultaneously at various offsets between the transmitting fibre and receiving fibre bundle and provides first results for illustration.
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http://dx.doi.org/10.1088/0031-9155/50/11/001 | DOI Listing |
Clin Imaging
November 2024
University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK; National Institute for Health and Care Research Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK; Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK. Electronic address:
J Biomed Opt
November 2024
HTW - University of Applied Sciences Berlin, Faculty I - Health Electronics, Biomedical Electronics and Applied Research (BEAR) Labs, Berlin, Germany.
Significance: Optical mammography as a promising tool for cancer diagnosis has largely fallen behind expectations. Modern machine learning (ML) methods offer ways to improve cancer detection in diffuse optical transmission data.
Aim: We aim to quantitatively evaluate the classification of cancer-positive versus cancer-negative patients using ML methods on raw transmission time series data from bilateral breast scans during subjects' rest.
Comput Biol Med
December 2024
Department of Computer Engineering, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr, 7516913817, Iran.
Breast cancer ranks as the second most prevalent cancer in women, recognized as one of the most dangerous types of cancer, and is on the rise globally. Regular screenings are essential for early-stage treatment. Digital mammography (DM) is the most recognized and widely used technique for breast cancer screening.
View Article and Find Full Text PDFRadiology
October 2024
From the Departments of Medical Imaging (J.J.J.G., S.D.V., I.S.) and IQ Health (M.J.M.B.), Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands; Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, the Netherlands (K.M.D.); Department of Radiology and Nuclear Medicine, Haga Teaching Hospital, Den Haag, the Netherlands (J.K.v.R.); Department of Radiology, Gelre Hospitals, Apeldoorn, the Netherlands (A.F.v.R.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands (J.B.H.); Department of Radiology, Diakonessenhuis, Utrecht, the Netherlands (D.B.N.); Department of Radiology, Canisius Wilhelmina Hospital, Nijmegen, the Netherlands (L.E.M.D.); Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, Calif (C.K.A.); Department of Psychology, University of Nevada, Reno, Nev (M.A.W.); Dutch Expert Centre for Screening, Nijmegen, the Netherlands (M.J.M.B., I.S.); and Technical Medicine Center, University of Twente, Enschede, the Netherlands (I.S.).
IEEE Trans Med Imaging
September 2024
Medical image segmentation has seen great progress in recent years, largely due to the development of deep neural networks. However, unlike in computer vision, high-quality clinical data is relatively scarce, and the annotation process is often a burden for clinicians. As a result, the scarcity of medical data limits the performance of existing medical image segmentation models.
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