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Evaluation of Six Phase Encoding Based Susceptibility Distortion Correction Methods for Diffusion MRI
Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
2019 (Engelska)Ingår i: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 13, artikel-id 76Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Purpose: Susceptibility distortions impact diffusion MRI data analysis and is typically corrected during preprocessing. Correction strategies involve three classes of methods: registration to a structural image, the use of a fieldmap, or the use of images acquired with opposing phase encoding directions. It has been demonstrated that phase encoding based methods outperform the other two classes, but unfortunately, the choice of which phase encoding based method to use is still an open question due to the absence of any systematic comparisons.

Methods: In this paper we quantitatively evaluated six popular phase encoding based methods for correcting susceptibility distortions in diffusion MRI data. We employed a framework that allows for the simulation of realistic diffusion MRI data with susceptibility distortions. We evaluated the ability for methods to correct distortions by comparing the corrected data with the ground truth. Four diffusion tensor metrics (FA, MD, eigenvalues and eigenvectors) were calculated from the corrected data and compared with the ground truth. We also validated two popular indirect metrics using both simulated data and real data. The two indirect metrics are the difference between the corrected LR and AP data, and the FA standard deviation over the corrected LR, RL, AP, and PA data.

Results: We found that DR-BUDDI and TOPUP offered the most accurate and robust correction compared to the other four methods using both direct and indirect evaluation metrics. EPIC and HySCO performed well in correcting b0 images but produced poor corrections for diffusion weighted volumes, and also they produced large errors for the four diffusion tensor metrics. We also demonstrate that the indirect metric (the difference between corrected LR and AP data) gives a different ordering of correction quality than the direct metric.

Conclusion: We suggest researchers to use DR-BUDDI or TOPUP for susceptibility distortion correction. The two indirect metrics (the difference between corrected LR and AP data, and the FA standard deviation) should be interpreted together as a measure of distortion correction quality. The performance ranking of the various tools inferred from direct and indirect metrics differs slightly. However, across all tools, the results of direct and indirect metrics are highly correlated indicating that the analysis of indirect metrics may provide a good proxy of the performance of a correction tool if assessment using direct metrics is not feasible.

Ort, förlag, år, upplaga, sidor
Frontiers Media S.A., 2019. Vol. 13, artikel-id 76
Nyckelord [en]
susceptibility distortion, diffusion MRI, opposing phase encoding, diffusion tensor, diffusion MRI simulation
Nationell ämneskategori
Medicinsk bildbehandling
Identifikatorer
URN: urn:nbn:se:liu:diva-162639DOI: 10.3389/fninf.2019.00076ISI: 000503496300001PubMedID: 31866847OAI: oai:DiVA.org:liu-162639DiVA, id: diva2:1377532
Forskningsfinansiär
Vetenskapsrådet, 2017-04889Vinnova
Anmärkning

Funding agencies:  Swedish Research CouncilSwedish Research Council [2017-04889]; Linkoping University Center for Industrial Information Technology (CENIIT); ITEA3/VINNOVA

Tillgänglig från: 2019-12-12 Skapad: 2019-12-12 Senast uppdaterad: 2020-01-09Bibliografiskt granskad

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Gu, XuanEklund, Anders
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