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Titlebook: Digital Mammography; IWDM 2002 — 6th Inte Heinz-Otto Peitgen (Professor of Mathematics and B Conference proceedings 2003 Springer-Verlag Be

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51#
發(fā)表于 2025-3-30 08:39:41 | 只看該作者
Evaluation of light collection in digital indirect detection x-ray imagers: Monte Carlo simulations ly below the x-ray interaction point and 41% in the 8 nearest neighbor pixels) was collected by the photodetector for Imager 2 compared with only 56% (28% in the central pixel and 28% by the nearest neighbor pixels) for Imager 1.
52#
發(fā)表于 2025-3-30 15:47:25 | 只看該作者
53#
發(fā)表于 2025-3-30 19:22:16 | 只看該作者
54#
發(fā)表于 2025-3-30 23:39:47 | 只看該作者
Communications and Control Engineeringn Rose’s criterion (SNR≥5), that breast CT can produce excellent image quality at mean glandular doses comparable to mammography. The potential to identify smaller lesions may improve early detection performance, which in turn would likely result in a reduction of breast cancer mortality.
55#
發(fā)表于 2025-3-31 02:54:40 | 只看該作者
56#
發(fā)表于 2025-3-31 07:30:10 | 只看該作者
Digital mammographic application of a single photon counting pixel detectorick a matrix of 64 x 64 square pixels with a dimension side of 170 μm. The active area is about 1.2 cm.. The photon counting chip matches the geometry of the detector so it has 4096 asynchronous read-out cells, each containing a charge preamplifier, a leading edge comparator and a pseudorandom count
57#
發(fā)表于 2025-3-31 11:47:50 | 只看該作者
58#
發(fā)表于 2025-3-31 16:19:08 | 只看該作者
59#
發(fā)表于 2025-3-31 18:51:05 | 只看該作者
Digital Mammography vs. Screen-Film Mammography: a Phantom Studyuji imaging plates system (photostimulable phosphors, 50 micron pixel size), implemented on a conventional mammography unit. We conducted a first comparison between laser printed images and 4 different high contrast conventional screen-film combinations. Three specific mammographic phantoms were use
60#
發(fā)表于 2025-3-31 22:59:02 | 只看該作者
Mammography Taxonomy for the Improvement of Lesion Detection Ratesh automatically classifies breast parenchyma. The classification engine is an artificial neural network which successfully forms narrow classes to capture the subtleties in parenchyma variation. This paper presents the result of a series of experiments that digitized 628 mammograms at 50 μm from the
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