RESOURCES - MRI
Multi-Site Study: Novel Deep Learning Super-Resolution Algorithm Allows Substantial Improvement of Neuro MRI Image Quality and Scan Time
Presented at ASNR 2023 The purpose of this multi-site study was to evaluate image quality, image resolution, and overall diagnostic sufficiency of low-resolution brain MRI images processed by a iQMR, a deep-learning (DL) resolution enhancement algorithm, compared to images acquired with higher resolution from multiple sites and MRI scanners.
MRI Novel Deep Learning Super Resolution Algorithm Allows Substantial Improvement of Brain MRI Image Quality and Scan Time in a Multi Site Study
Presented at RSNA 2022 The purpose of the MRI novel study was to evaluate image quality, image resolution, and overall diagnostic sufficiency of low resolution brain MRI images processed
Large-scale implementation of an Artificial Intelligence Solution to improve patient experience and enhance productivity in MRI departments. Alliar Brazil
Presented by Alliar, Brazil at RSNA 2022 The purpose of this study was to evaluate if the adoption of a proprietary Artificial Intelligence Software that
Machine Learning-based Iterative Image Reconstruction Algorithm Allows Significant Reduction in Brain MRI Scan Times
Collaborative work with Lawrence N. Tanenbaum MD FACR, RadNet PURPOSE This study aims to evaluate the scan time shortening potential of iQMR, a novel 3D
THE EFFECT OF A POST-SCAN PROCESSING DENOISING SYSTEM ON IMAGE QUALITY AND MORPHOMETRIC ANALYSIS
MR image quality and subsequent brain morphometric analysis are inevitably affected by noise. The purpose of this study was to evaluate the effectiveness of an artificial
Substantial Shortening of Neuro MR Scan Times Using Compressed-Sense and Machine Learning-assisted Image Enhancement Technology – Approved abstract ASNR 2021
To evaluate the ability to substantially reduce neuro MRI scans time by applying Compressed Sense (CS) with Machine Learning (ML) assisted image enhancement technology. Figure 1 Axial
Machine Learning-assisted Resolution Enhancement Algorithm Allows for Substantial Reduction of Brain MRI Scan Time – Approved abstract ASNR 2021
Authors: R Shreter , J Brace , M Goldfeld , I Page , G Stenoien , L Mori , T Aharoni , L Lucato PURPOSE To
Implementation of AI-assisted Technology to Enhance Service Quality and Productivity in Outpatient Diagnostic Centers
This report was conducted with Houston Medical Imaging’s (HMI) outpatient diagnostic centers. An increase in demand for MRI scans at HMI prompted a Productivity and
COMPARING ACCELERATION OF MRI BRAIN SCANS: COMPRESSED SENSING AND MACHINE LEARNING IMAGE PROCESSING TECHNOLOGIES
The report compared complete MRI brain scans exams that were acquired by the site’s routine scan (11 minutes 50 seconds) and by accelerated acquisition (5
MRI scans Substantial Shortening of Neuro Times
To evaluate the ability to substantially reduce neuro MRI scans time by applying Compressed Sense (CS) with Machine Learning (ML) assisted imageenhancement technology. Figure 1
Substantial shortening of brain and lumbar spine mr scan time using iqmr™ machine learning-based iterative image reconstruction algorithm: multi-site study
This study aims to evaluate the scan time shortening potential of iQMR, a novel 3D image enhancement algorithm, in brain and lumbar spine MRI. iQMR provides
Application of Machine Learning-based Iterative Image Reconstruction Algorithms for MRI Scan Time Reduction in Brain and Spine – Approved Abstract ASNR 2018
Collaborative work with Lawrence N. Tanenbaum MD FACR, RadNet PURPOSE To investigate whether accelerated brain and spine MR protocols enhanced with a machine-learning based algorithm
Machine Learning-based Iterative Image Reconstruction Algorithm Allows Significant Reduction in Brain MRI Scan Times – Approved abstract ASNR 2018
To evaluate image quality, grey-white (GW) matter differentiation and overall diagnostic sufficiency of brain MRI images acquired with reduced Brain MRI scan time protocols, processed with a
MRI CASE STUDIES AND REPORTS
Large-Scale Deployment of iQMR, an AI-Assisted Technology, Enabling Short Protocols on MRI Scanners – Case Report
Alliar, the third-largest diagnostic imaging provider in Brazil has more than 100 outpatient imaging centers across the country. provides imaging services to 10,000 people a month and performs more than 800,000 MRI exams per year with more than 100 scanners of various makers and types. As the demand
Medic Vision and Nuclear Light Industry Ltd. Deliver AdvancedMRI Services in South Korea
This case study describes the implementation of fast MRI protocols at the Wiltse Memorial Hospital radiology department in the Suwon city of South Korea.Medic Vision’s iQMR system allowed the hospital to accommodate more patients per day while maintaining or even increasing image quality for diagnostics.
RESOURCES - CT
COMPARISON OF RECONSTRUCTION APPROACHES FOR IMPROVING LOW DOSE CT IMAGES
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
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
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
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.
SUBMILLISIEVERT CHEST CT WITH FILTERED BACK PROJECTION AND ITERATIVE RECONSTRUCTION TECHNIQUES
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.
CT imaging: radiation risk reduction– real-life experience in a metropolitan outpatient imaging network
John O. Johnson, MD* , Jon M. Robins, MD. * Imaging Healthcare Specialists, Radiology Medical Group, San Diego, CA. Journal of American College of Radiology 2012, No.9, pp. 808-813.
Evaluation of Noise Reduction Techniques in Chest CT
H.Moriya MD, K.Hashimoto, S. Muramatsu, M.Suzuki, H.Chiba, Y. Nakajou, Y. Ohashi, S. Tsukagoshi, N. Kameda, T. Tanaka Ohara General Hospital, Fukushima, Japan Ziosoft Inc, Canon Medical Systems Coporation, NAGASE & Co, Ltd
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.
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.