Observatorio de I+D+i UPM

Memorias de investigación
Research Publications in journals:
Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis
Research Areas
  • Biomedicine,
  • Engineering,
  • Electronic technology and of the communications
Images acquired during free breathing using first-pass gadolinium-enhanced myocardial perfusion magnetic resonance imaging (MRI) exhibit a quasiperiodic motion pattern that needs to be compensated for if a further automatic analysis of the perfusion is to be executed. In this work, we present a method to compensate this movement by combining independent component analysis (ICA) and image registration: First, we use ICA and a time?frequency analysis to identify the motion and separate it from the intensity change induced by the contrast agent. Then, synthetic reference images are created by recombining all the independent components but the one related to the motion. Therefore, the resulting image series does not exhibit motion and its images have intensities similar to those of their original counterparts. Motion compensation is then achieved by using a multi-pass image registration procedure. We tested our method on 39 image series acquired from 13 patients, covering the basal, mid and apical areas of the left heart ventricle and consisting of 58 perfusion images each. We validated our method by comparing manually tracked intensity profiles of the myocardial sections to automatically generated ones before and after registration of 13 patient data sets (39 distinct slices). We compared linear, non-linear, and combined ICA based registration approaches and previously published motion compensation schemes. Considering run-time and accuracy, a two-step ICA based motion compensation scheme that first optimizes a translation and then for non-linear transformation performed best and achieves registration of the whole series in 32 ± 12 s on a recent workstation. The proposed scheme improves the Pearsons correlation coefficient between manually and automatically obtained time?intensity curves from .84 ± .19 before registration to .96 ± .06 after registration
Med. Image Anal
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Lugar en el Área ?Computer Science, Interdisciplinary Applications?: 2/99 (D1) Lugar en el Área ?Computer Science, Artificial Intelligence?: 3/111 (D1) Lugar en el Área ?Engineering, Biomedical?: 4/72 (D1) Lugar en el Área ?Radiology, Nuclear Medicine & Medical Imaging?: 10/116 (D1)
  • Autor: Gert Wöllny . (UPM)
  • Autor: Peter Kellmna (Laboratory of Cardiac Energetics, National Heart, Lung and Blood Institute, National Institutes of Health, DHHS, Bethesda, MD, USA)
  • Autor: Andres de Santos Lleo (UPM)
  • Autor: Maria Jesus Ledesma Carbayo (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Tecnología de imágenes biomédicas
  • Centro o Instituto I+D+i: Centro de tecnología Biomédica CTB
  • Departamento: Ingeniería Electrónica
S2i 2020 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)