PUBLICATIONS

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.

PROSPECTIVE CLINICAL STUDY TO ASSESS IMAGE BASED ITERATIVE RECONSTRUCTION FOR ABDOMINAL CT ACQUIRED AT THREE RADIATION DOSE LEVELS

December 5, 2014

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.

ROLE OF IMAGE-BASED ITERATIVE RECONSTRUCTION TECHNIQUE FOR LOW RADIATION DOSE CHEST CT

December 5, 2016

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.

CO-REGISTERED IMAGE QUALITY COMPARISON IN HYBRID ITERATIVE RECONSTRUCTION TECHNIQUES: SAFIRE AND SAFECT

December 5, 2016

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.

PRESENTATIONS

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.

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.

PUBLICATIONS

RESOURCES - MRI

Machine Learning-based Iterative Image Reconstruction Algorithm Allows Significant Reduction in Brain MRI Scan Times

THE EFFECT OF A POST-SCAN PROCESSING DENOISING SYSTEM ON IMAGE QUALITY AND MORPHOMETRIC ANALYSIS

Substantial Shortening of Neuro MR Scan Times Using Compressed-Sense and Machine Learning-assisted Image Enhancement Technology – Approved abstract ASNR 2021

Machine Learning-assisted Resolution Enhancement Algorithm Allows for Substantial Reduction of Brain MRI Scan Time – Approved abstract ASNR 2021

Implementation of AI-assisted Technology to Enhance Service Quality and Productivity in Outpatient Diagnostic Centers

COMPARING ACCELERATION OF MRI BRAIN SCANS: COMPRESSED SENSING AND MACHINE LEARNING IMAGE PROCESSING TECHNOLOGIES

Substantial Shortening of Neuro MR Scan Times Using Compressed-Senseand Machine Learning-assisted Image Enhancement Technology

Substantial shortening of brain and lumbar spine mr scan time using iqmr™ machine learning-based iterative image reconstruction algorithm: multi-site study

Application of Machine Learning-based Iterative Image Reconstruction Algorithms for MRI Scan Time Reduction in Brain and Spine

Application of Machine Learning-based Iterative Image Reconstruction Algorithms for MRI Scan Time Reduction in Brain and Spine – Approved Abstract ASNR 2018

Machine Learning-based Iterative Image Reconstruction Algorithm Allows Significant Reduction in Brain MRI Scan Times – Approved abstract ASNR 2018

MRI CASE STUDIES AND REPORTS

Large-Scale Deployment of iQMR, an AI-Assisted Technology, Enabling Short Protocols on MRI Scanners – Case Report

Medic Vision and Nuclear Light Industry Ltd. Deliver AdvancedMRI Services in South Korea

RESOURCES - CT

COMPARISON OF RECONSTRUCTION APPROACHES FOR IMPROVING LOW DOSE CT IMAGES

PROSPECTIVE CLINICAL STUDY TO ASSESS IMAGE BASED ITERATIVE RECONSTRUCTION FOR ABDOMINAL CT ACQUIRED AT THREE RADIATION DOSE LEVELS

ROLE OF IMAGE-BASED ITERATIVE RECONSTRUCTION TECHNIQUE FOR LOW RADIATION DOSE CHEST CT

CO-REGISTERED IMAGE QUALITY COMPARISON IN HYBRID ITERATIVE RECONSTRUCTION TECHNIQUES: SAFIRE AND SAFECT

SUBMILLISIEVERT CHEST CT WITH FILTERED BACK PROJECTION AND ITERATIVE RECONSTRUCTION TECHNIQUES

CT imaging: radiation risk reduction– real-life experience in a metropolitan outpatient imaging network

Evaluation of Noise Reduction Techniques in Chest CT

PRESENTATIONS

2018 AUNTMINNIE.COM ROADIE AWARD: HOTTEST ARTIFICIAL INTELLIGENCE ABSTRACT

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

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