Resources - CT
- CT IMAGING: RADIATION RISK REDUCTION-- REAL-LIFE EXPERIENCE IN A METROPOLITAN OUTPATIENT IMAGING NETWORK 2017-12-05
John O. Johnson, MD* , Jon M. Robins, MD. * Imaging Healthcare Specialists, Radiology Medical Group, San Diego, CA. Journal of American College of Radiology2012, No.9, pp. 808-813. Read Abstract
- Evaluation of Noise Reduction Techniques in Chest CT 2017-12-05
H.Moriya MD, K.Hashimoto, S. Muramatsu, M.Suzuki, H.Chiba, Y. Nakajou, Y. Ohashi, S. Tsukagoshi, N. Kameda, T. TanakaOhara General Hospital, Fukushima, JapanZiosoft Inc, Canon Medical Systems Coporation, NAGASE & Co, Ltd Read Abstract
- SUBMILLISIEVERT CHEST CT WITH FILTERED BACK PROJECTION AND ITERATIVE RECONSTRUCTION TECHNIQUES 2017-11-05
Atul Padole1, Sarabjeet Singh, Jeanne B. Ackman, Carol Wu, Synho Do, Sarvenaz Pourjabbar, Ranish Deedar Ali Khawaja, Alexi Otrakji, Subba Digumarthy, Jo-Anne Shepard, Mannudeep Kalra, AJR:203, October 2014. Read Abstract
- ROLE OF IMAGE-BASED ITERATIVE RECONSTRUCTION TECHNIQUE FOR LOW RADIATION DOSE CHEST CT 2016-12-05
S. Pourjabbar, MD*; R.D.A. Khawaja, MD; S. Singh, MD; A. Padole, MD; D. Lira, MD; M. Kalra, MD. *Dept. of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA. Radiological Case Review, Applied Radiology, July 2013. Read Abstract
- CO-REGISTERED IMAGE QUALITY COMPARISON IN HYBRID ITERATIVE RECONSTRUCTION TECHNIQUES: SAFIRE AND SAFECT 2016-11-05
Seungwan Lee*, Aran Shima, Sarabjeet Singh, Mannudeep K. Kalra, Hee-Joung Kim, Synho Do *Department of Radiology, Massachusetts General Hospital, Boston, MA. Medical Imaging 2013:Physics of Medical Imaging, edited by Robert M. Nishikawa, Bruce R. Whiting, and Christoph Hoeschen, Proc. of SPIE, Vol. 8668, 86683G-1. Read Abstract
- PROSPECTIVE CLINICAL STUDY TO ASSESS IMAGE BASED ITERATIVE RECONSTRUCTION FOR ABDOMINAL CT ACQUIRED AT THREE RADIATION DOSE LEVELS 2014-11-05
S. Pourjabbar, MD* , S. Singh, MD, R. Perez Johnston. MD, A.S. Shenoy-Bhangle, MD, S. Do, PhD, Shima Aran, MD, Michael Blake, MD, Anders Persson, MD, Mannudeep Kalra, MD. * Massachusetts General Hospital, Boston, MA. RSNA Annual Meeting, Chicago, IL, November 2012. Read Abstract
- COMPARISON OF RECONSTRUCTION APPROACHES FOR IMPROVING LOW DOSE CT IMAGES 2012-11-05
C.R. Deible*, K. Tung, J.M. Lacomis, C.R. Fuhrman, R. Jarosz, D.Gur. *University of Pittsburgh Medical Center, Pittsburgh, PA. RSNA Annual Meeting, Chicago, IL, November 2012. Read Abstract
Resources - MRI
2018 AuntMinnie.com Roadie Award:
Hottest Artificial Intelligence Abstract
In this presentation, Dr. Lawrence N. Tanenbaum, M.D, FACR demonstrates the capabilities of iQMR, a machine learning-based image reconstruction algorithm allowing to sharply lower scanning times for brain and lumbar MRI studies. The algorithm enables fast MR scans to be performed by a lower cumulative signal, which reduces MRI scanning time by approximately 30% while producing equivalent overall image quality.
APPLICATION OF MACHINE LEARNING-BASED ITERATIVE IMAGE RECONSTRUCTION ALGORITHMS FOR MRI SCAN TIME REDUCTION IN BRAIN AND SPINE
LAWRENCE N. TANENBAUM, MD, FACR
ASNR ANNUAL MEETING 2019, BOSTON
The study aims to evaluate the fast MRI potential of iQMR, a 3D image enhancement algorithm, in brain, lumbar spine and cervical spine MRI. iQMR provides image enhancement and increased Signal-to-Noise Ratio (SNR), thus permitting substantial scan time reduction, without changing the imaging contrast, resolution or scanner hardware or software.
The study reports findings of 73 patients (37 brain, 19 lumbar spine, 17 cervical spine) who underwent MRI scans on 4 different scanners at 3 sites. Image quality, diagnostic quality, artifacts’ appearance and brain grey-white (GW) matter differentiation of iQMR-processed images acquired by fast MRI protocols were compared to images acquired by the site’s standard MRI protocols.