TY - JOUR
T1 - Carotid artery wall motion analysis from B-mode ultrasound using adaptive block matching
T2 - In silico evaluation and in vivo application
AU - Gastounioti, A.
AU - Golemati, S.
AU - Stoitsis, J. S.
AU - Nikita, K. S.
PY - 2013/12/21
Y1 - 2013/12/21
N2 - Valid risk stratification for carotid atherosclerotic plaques represents a crucial public health issue toward preventing fatal cerebrovascular events. Although motion analysis (MA) provides useful information about arterial wall dynamics, the identification of motion-based risk markers remains a significant challenge. Considering that the ability of a motion estimator (ME) to handle changes in the appearance of motion targets has a major effect on accuracy in MA, we investigated the potential of adaptive block matching (ABM) MEs, which consider changes in image intensities over time. To assure the validity in MA, we optimized and evaluated the ABM MEs in the context of a specially designed in silico framework. ABMFIRF2, which takes advantage of the periodicity characterizing the arterial wall motion, was the most effective ABM algorithm, yielding a 47% accuracy increase with respect to the conventional block matching. The in vivo application of ABMFIRF2 revealed five potential risk markers: low movement amplitude of the normal part of the wall adjacent to the plaques in the radial (RMAPWL) and longitudinal (LMA PWL) directions, high radial motion amplitude of the plaque top surface (RMAPTS), and high relative movement, expressed in terms of radial strain (RSIPL) and longitudinal shear strain (LSSI PL), between plaque top and bottom surfaces. The in vivo results were reproduced by OFLK(WLS) and ABMKF-K2, MEs previously proposed by the authors and with remarkable in silico performances, thereby reinforcing the clinical values of the markers and the potential of those MEs. Future in vivo studies will elucidate with confidence the full potential of the markers.
AB - Valid risk stratification for carotid atherosclerotic plaques represents a crucial public health issue toward preventing fatal cerebrovascular events. Although motion analysis (MA) provides useful information about arterial wall dynamics, the identification of motion-based risk markers remains a significant challenge. Considering that the ability of a motion estimator (ME) to handle changes in the appearance of motion targets has a major effect on accuracy in MA, we investigated the potential of adaptive block matching (ABM) MEs, which consider changes in image intensities over time. To assure the validity in MA, we optimized and evaluated the ABM MEs in the context of a specially designed in silico framework. ABMFIRF2, which takes advantage of the periodicity characterizing the arterial wall motion, was the most effective ABM algorithm, yielding a 47% accuracy increase with respect to the conventional block matching. The in vivo application of ABMFIRF2 revealed five potential risk markers: low movement amplitude of the normal part of the wall adjacent to the plaques in the radial (RMAPWL) and longitudinal (LMA PWL) directions, high radial motion amplitude of the plaque top surface (RMAPTS), and high relative movement, expressed in terms of radial strain (RSIPL) and longitudinal shear strain (LSSI PL), between plaque top and bottom surfaces. The in vivo results were reproduced by OFLK(WLS) and ABMKF-K2, MEs previously proposed by the authors and with remarkable in silico performances, thereby reinforcing the clinical values of the markers and the potential of those MEs. Future in vivo studies will elucidate with confidence the full potential of the markers.
UR - http://www.scopus.com/inward/record.url?scp=84890422320&partnerID=8YFLogxK
U2 - 10.1088/0031-9155/58/24/8647
DO - 10.1088/0031-9155/58/24/8647
M3 - Article
C2 - 24256708
AN - SCOPUS:84890422320
SN - 0031-9155
VL - 58
SP - 8647
EP - 8661
JO - Physics in medicine and biology
JF - Physics in medicine and biology
IS - 24
ER -