Literature DB >> 25963225

Toward Quantifying the Prevalence, Severity, and Cost Associated With Patient Motion During Clinical MR Examinations.

Jalal B Andre1, Brian W Bresnahan2, Mahmud Mossa-Basha2, Michael N Hoff2, C Patrick Smith3, Yoshimi Anzai2, Wendy A Cohen2.   

Abstract

PURPOSE: To assess the prevalence, severity, and cost estimates associated with motion artifacts identified on clinical MR examinations, with a focus on the neuroaxis.
METHODS: A retrospective review of 1 randomly selected full calendar week of MR examinations (April 2014) was conducted for the detection of significant motion artifacts in examinations performed at a single institution on 3 different MR scanners. A base-case cost estimate was computed from recently available institutional data, and correlated with sequence time and severity of motion artifacts.
RESULTS: A total of 192 completed clinical examinations were reviewed. Significant motion artifacts were identified on sequences in 7.5% of outpatient and 29.4% of inpatient and/or emergency department MR examinations. The prevalence of repeat sequences was 19.8% of total MRI examinations. The base-case cost estimate yielded a potential cost to the hospital of $592 per hour in lost revenue due to motion artifacts. Potential institutional average costs borne (revenue forgone) of approximately $115,000 per scanner per year may affect hospitals, owing to motion artifacts (univariate sensitivity analysis suggested a lower bound of $92,600, and an upper bound of $139,000).
CONCLUSIONS: Motion artifacts represent a frequent cause of MR image degradation, particularly for inpatient and emergency department patients, resulting in substantial costs to the radiology department. Greater attention and resources should be directed toward providing practical solutions to this dilemma.
Copyright © 2015 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  MRI; cost estimate; economics; motion; motion correction

Mesh:

Year:  2015        PMID: 25963225     DOI: 10.1016/j.jacr.2015.03.007

Source DB:  PubMed          Journal:  J Am Coll Radiol        ISSN: 1546-1440            Impact factor:   5.532


  51 in total

1.  Synthetic MRI for Clinical Neuroimaging: Results of the Magnetic Resonance Image Compilation (MAGiC) Prospective, Multicenter, Multireader Trial.

Authors:  L N Tanenbaum; A J Tsiouris; A N Johnson; T P Naidich; M C DeLano; E R Melhem; P Quarterman; S X Parameswaran; A Shankaranarayanan; M Goyen; A S Field
Journal:  AJNR Am J Neuroradiol       Date:  2017-04-27       Impact factor: 3.825

2.  Evaluation of 3D fat-navigator based retrospective motion correction in the clinical setting of patients with brain tumors.

Authors:  Carl Glessgen; Daniel Gallichan; Manuela Moor; Nicolin Hainc; Christian Federau
Journal:  Neuroradiology       Date:  2019-01-23       Impact factor: 2.804

3.  MRI quality assurance based on 3D FLAIR brain images.

Authors:  Juha I Peltonen; Teemu Mäkelä; Eero Salli
Journal:  MAGMA       Date:  2018-08-17       Impact factor: 2.310

4.  Patient specific prospective respiratory motion correction for efficient, free-breathing cardiovascular MRI.

Authors:  Michael A Bush; Rizwan Ahmad; Ning Jin; Yingmin Liu; Orlando P Simonetti
Journal:  Magn Reson Med       Date:  2019-02-14       Impact factor: 4.668

5.  Effect of team training on improving MRI study completion rates and no-show rates.

Authors:  Alexander Norbash; Kent Yucel; William Yuh; Gheorghe Doros; Amna Ajam; Elvira Lang; Stephen Pauker; Nina Mayr
Journal:  J Magn Reson Imaging       Date:  2016-04-06       Impact factor: 4.813

6.  Correction of Motion Artifacts Using a Multiscale Fully Convolutional Neural Network.

Authors:  K Sommer; A Saalbach; T Brosch; C Hall; N M Cross; J B Andre
Journal:  AJNR Am J Neuroradiol       Date:  2020-02-13       Impact factor: 3.825

7.  Network Accelerated Motion Estimation and Reduction (NAMER): Convolutional neural network guided retrospective motion correction using a separable motion model.

Authors:  Melissa W Haskell; Stephen F Cauley; Berkin Bilgic; Julian Hossbach; Daniel N Splitthoff; Josef Pfeuffer; Kawin Setsompop; Lawrence L Wald
Journal:  Magn Reson Med       Date:  2019-05-02       Impact factor: 4.668

8.  Ultimate MRI.

Authors:  Lawrence L Wald
Journal:  J Magn Reson       Date:  2019-07-09       Impact factor: 2.229

9.  Prospective motion correction using coil-mounted cameras: Cross-calibration considerations.

Authors:  Julian Maclaren; Murat Aksoy; Melvyn B Ooi; Benjamin Zahneisen; Roland Bammer
Journal:  Magn Reson Med       Date:  2017-07-19       Impact factor: 4.668

Review 10.  High-resolution intracranial vessel wall imaging: imaging beyond the lumen.

Authors:  Matthew D Alexander; Chun Yuan; Aaron Rutman; David L Tirschwell; Gerald Palagallo; Dheeraj Gandhi; Laligam N Sekhar; Mahmud Mossa-Basha
Journal:  J Neurol Neurosurg Psychiatry       Date:  2016-01-08       Impact factor: 10.154

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