Clinical Integration of Automated Processing for Brain Quantitative Susceptibility Mapping: Multi‐Site Reproducibility and Single‐Site Robustness. Issue 6 (4th August 2019)
- Record Type:
- Journal Article
- Title:
- Clinical Integration of Automated Processing for Brain Quantitative Susceptibility Mapping: Multi‐Site Reproducibility and Single‐Site Robustness. Issue 6 (4th August 2019)
- Main Title:
- Clinical Integration of Automated Processing for Brain Quantitative Susceptibility Mapping: Multi‐Site Reproducibility and Single‐Site Robustness
- Authors:
- Spincemaille, Pascal
Liu, Zhe
Zhang, Shun
Kovanlikaya, Ilhami
Ippoliti, Matteo
Makowski, Marcus
Watts, Richard
de Rochefort, Ludovic
Venkatraman, Vijay
Desmond, Patricia
Santin, Mathieu D.
Lehéricy, Stéphane
Kopell, Brian H.
Péran, Patrice
Wang, Yi - Abstract:
- ABSTRACT: BACKGROUND AND PURPOSE: Quantitative susceptibility mapping (QSM) of the brain has become highly reproducible and has applications in an expanding array of diseases. To translate QSM from bench to bedside, it is important to automate its reconstruction immediately after data acquisition. In this work, a server system that automatically reconstructs QSM and exchange images with the scanner using the DICOM standard is demonstrated using a multi‐site, multi‐vendor reproducibility study and a large, single‐site, multi‐scanner image quality review study in a clinical environment. METHODS: A single healthy subject was scanned with a 3D multi‐echo gradient echo sequence at nine sites around the world using scanners from three manufacturers. A high‐resolution (HiRes, .5 × .5 × 1 mm 3 reconstructed) and standard‐resolution (StdRes, .5 × .5 × 3 mm 3 ) protocol was performed. ROI analysis of various white matter and gray matter regions was performed to investigate reproducibility across sites. At one institution, a retrospective multi‐scanner image quality review was carried out of all clinical QSM images acquired consecutively in 1 month. RESULTS: Reconstruction times using a GPU were 29 ± 22 seconds (StdRes) and 55 ± 39 seconds (HiRes). ROI standard deviation across sites was below 24 ppb (StdRes) and 17 ppb (HiRes). Correlations between ROI averages across sites were on average .92 (StdRes) and .96 (HiRes). Image quality review of 873 consecutive patients revealedABSTRACT: BACKGROUND AND PURPOSE: Quantitative susceptibility mapping (QSM) of the brain has become highly reproducible and has applications in an expanding array of diseases. To translate QSM from bench to bedside, it is important to automate its reconstruction immediately after data acquisition. In this work, a server system that automatically reconstructs QSM and exchange images with the scanner using the DICOM standard is demonstrated using a multi‐site, multi‐vendor reproducibility study and a large, single‐site, multi‐scanner image quality review study in a clinical environment. METHODS: A single healthy subject was scanned with a 3D multi‐echo gradient echo sequence at nine sites around the world using scanners from three manufacturers. A high‐resolution (HiRes, .5 × .5 × 1 mm 3 reconstructed) and standard‐resolution (StdRes, .5 × .5 × 3 mm 3 ) protocol was performed. ROI analysis of various white matter and gray matter regions was performed to investigate reproducibility across sites. At one institution, a retrospective multi‐scanner image quality review was carried out of all clinical QSM images acquired consecutively in 1 month. RESULTS: Reconstruction times using a GPU were 29 ± 22 seconds (StdRes) and 55 ± 39 seconds (HiRes). ROI standard deviation across sites was below 24 ppb (StdRes) and 17 ppb (HiRes). Correlations between ROI averages across sites were on average .92 (StdRes) and .96 (HiRes). Image quality review of 873 consecutive patients revealed diagnostic or excellent image quality in 96% of patients. CONCLUSION: Online QSM reconstruction for a variety of sites and scanner platforms with low cross‐site ROI standard deviation is demonstrated. Image quality review revealed diagnostic or excellent image quality in 96% of 873 patients. … (more)
- Is Part Of:
- Journal of neuroimaging. Volume 29:Issue 6(2019)
- Journal:
- Journal of neuroimaging
- Issue:
- Volume 29:Issue 6(2019)
- Issue Display:
- Volume 29, Issue 6 (2019)
- Year:
- 2019
- Volume:
- 29
- Issue:
- 6
- Issue Sort Value:
- 2019-0029-0006-0000
- Page Start:
- 689
- Page End:
- 698
- Publication Date:
- 2019-08-04
- Subjects:
- Quantitative susceptibility mapping -- software -- clinic
Diagnostic imaging -- Periodicals
Nervous system -- Diseases -- Diagnosis -- Periodicals
Imagerie pour le diagnostic -- Périodiques
Système nerveux -- Maladies -- Diagnostic -- Périodiques
Imagerie médicale
Neuroimagerie
Neurologie
Système nerveux
Périodique électronique (Descripteur de forme)
Ressource Internet (Descripteur de forme)
616.804754 - Journal URLs:
- http://jon.sagepub.com/ ↗
http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1552-6569 ↗
http://www.ingentaconnect.com/content/bpl/jon ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1111/jon.12658 ↗
- Languages:
- English
- ISSNs:
- 1051-2284
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5021.548000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 11919.xml