Edward Stefanowicz and Bill O'Connell

Advanced Visualization
Hospital 3D printing: Part 2 -- A clear path to value capture
A key part of starting a 3D printing service or lab is demonstrating its value capture. Bill O'Connell of Materialise and Edward Stefanowicz of Geisinger Health System describe 12 potential value-capture areas associated with hospital 3D printing in part 2 of their series.
June 4, 2018
2017 04 21 14 08 45 960 3d Printing Ball 400
Advanced Visualization
Hospital 3D printing: Part 1 -- Starting a 3D printing service
Are you thinking about launching a 3D printing service or lab at your hospital? In the first of a two-part series on hospital 3D printing, Bill O'Connell of Materialise and Edward Stefanowicz of Geisinger Health System suggest measures you should take before getting started.
June 3, 2018
2017 04 21 14 08 45 960 3d Printing Ball 400
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The findings highlight the need for systematic dose tracking and rigorous justification of imaging.
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Images of a 39-year-old who underwent screening mammography. Right craniocaudal (left) and mediolateral oblique (middle) views from preoperative mammography show suspicious grouped fine pleomorphic calcifications (arrows). After biopsy showed ductal carcinoma of situ (DCIS), patient underwent breast-conserving surgery, yielding final diagnosis of ductal carcinoma in situ (intermediate grade, hormone receptor positive). (Right) Screenshot of an output of a commercial AI tool applied to preoperative mammography. AI detected right breast lesion with score of 95% on both views (concentric colored circles). Present study’s reviewing radiologist deemed AI marking to correspond with site of DCIS.
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Axial contrast-enhanced CT scan in a 28-year-old premenopausal woman shows an incidentally detected solid-appearing left ovarian lesion (solid arrow) anterior to the uterus (U), associated with trace ascites in the right adnexa and cul de sac (dashed arrows). At pathologic analysis, this was shown to be ovarian dysgerminoma. Two readers misdiagnosed this lesion, one as a leiomyoma and one as an ovarian fibroma.
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Images from two representative cases. (A-C) Images in an 82-year-old man with a calcified plaque in the proximal segment of the left anterior descending artery. (A, B) Curved planar reconstruction images from CCTA with (A) hybrid iterative reconstruction (HIR) and (B) super-resolution deep learning reconstruction (SR-DLR) show that the measured PDS was 66% for the HIR image and 42% for the SR-DLR image. (C) Quantitative analysis of the invasive coronary angiography (ICA) image (reference standard) revealed a PDS of 34% (arrow). (D-F) Images in a 37-year-old woman with a noncalcified plaque in the middle segment of the left circumflex artery. (D, E) Curved planar reconstruction images from CCTA with (D) HIR and (E) SR-DLR show that the measured PDS was 47% for the HIR image and 80% for the SR-DLR image. (F) Quantitative analysis of the ICA image revealed a PDS of 72% (arrow). Blue, red, and yellow lines in A, B, D, and E represent the proximal reference point, stenosed lesion, and distal reference point, respectively, and the insets show the corresponding cross-sectional images.
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