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Fully automatic left ventricular myocardial strain estimation in 2D short-axis tagged magnetic resonance imaging
KULeuven University of Leuven, Belgium; ICVS 3Bs PT Govt Associate Lab, Portugal; University of Porto, Portugal; University of Minho, Portugal.
KULeuven University of Leuven, Belgium; ICVS 3Bs PT Govt Associate Lab, Portugal; University of Minho, Portugal.
KULeuven University of Leuven, Belgium.
Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för kardiovaskulär medicin. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Hjärt- och Medicincentrum, Fysiologiska kliniken US. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
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2017 (Engelska)Ingår i: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 62, nr 17, s. 6899-6919Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Cardiovascular diseases are among the leading causes of death and frequently result in local myocardial dysfunction. Among the numerous imaging modalities available to detect these dysfunctional regions, cardiac deformation imaging through tagged magnetic resonance imaging (t-MRI) has been an attractive approach. Nevertheless, fully automatic analysis of these data sets is still challenging. In this work, we present a fully automatic framework to estimate left ventricular myocardial deformation from t-MRI. This strategy performs automatic myocardial segmentation based on B-spline explicit active surfaces, which are initialized using an annular model. A non-rigid image-registration technique is then used to assess myocardial deformation. Three experiments were set up to validate the proposed framework using a clinical database of 75 patients. First, automatic segmentation accuracy was evaluated by comparing against manual delineations at one specific cardiac phase. The proposed solution showed an average perpendicular distance error of 2.35 +/- 1.21 mm and 2.27 +/- 1.02 mm for the endo- and epicardium, respectively. Second, starting from either manual or automatic segmentation, myocardial tracking was performed and the resulting strain curves were compared. It is shown that the automatic segmentation adds negligible differences during the strain-estimation stage, corroborating its accuracy. Finally, segmental strain was compared with scar tissue extent determined by delay-enhanced MRI. The results proved that both strain components were able to distinguish between normal and infarct regions. Overall, the proposed framework was shown to be accurate, robust, and attractive for clinical practice, as it overcomes several limitations of a manual analysis.

Ort, förlag, år, upplaga, sidor
IOP PUBLISHING LTD , 2017. Vol. 62, nr 17, s. 6899-6919
Nyckelord [en]
tagged magnetic resonance imaging; fully automatic segmentation; non-rigid image registration; strain estimation
Nationell ämneskategori
Medicinsk bildbehandling
Identifikatorer
URN: urn:nbn:se:liu:diva-140036DOI: 10.1088/1361-6560/aa7dc2ISI: 000407311400002PubMedID: 28783715OAI: oai:DiVA.org:liu-140036DiVA, id: diva2:1136718
Anmärkning

Funding Agencies|FCT-Fundacao para a Ciencia e a Tecnologia, Portugal [SFRH/BD/95438/2013, SFRH/BD/93443/2013]; European Social Found, European Union [SFRH/BD/95438/2013, SFRH/BD/93443/2013]; Programa Operacional Regional do Norte, Quadro de Referencia Estrategico Nacional, through Fundo Europeu de Desenvolvimento Regional (FEDER) [NORTE-07-0124-FEDER-000017, NORTE-01-0145-FEDER-000013]; EU (FP7) framework program [223615]

Tillgänglig från: 2017-08-29 Skapad: 2017-08-29 Senast uppdaterad: 2017-08-29

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Engvall, Jan
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Avdelningen för kardiovaskulär medicinMedicinska fakultetenFysiologiska kliniken USCentrum för medicinsk bildvetenskap och visualisering, CMIV
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Physics in Medicine and Biology
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