Accurate Measurement of Cross-Sectional Area of Femoral Artery on MRI Sequences of Transcontinental Ultramarathon Runners Using Optimal Parameters Selection

In clinics an accurate vessel segmentation method is important to quantize the vessel volume change with respect to time for artery elasticity measurement. This study proposes a modified version on 3D–expanded dynamic programming to find an optimal surface in a 3D matrix. The aim of this study is to discover the robustness against noises in measuring the cross-sectional area of the femoral artery on MRI datasets of ultra-endurance runners as accurately as possible. To do this, we use phantom images with different added noises and different image contrasts to find out the optimal parameters using grid search. The contrast between the vessel lumen and its background in phantom study is changed to simulate the real MRI dataset. We also add a plaque in phantom images to test the accuracy of the proposed algorithm in dealing pathologic cases. The phantom studies and grid search on selecting optimal parameters can offer an alternative way on parameter selection. In application to MRI, the accuracy is performed via comparisons between the manual tracings of experts and automated results. The mean relative error is 2.1 % ± 2.1 % on testing 11 MRI datasets (total 550 images). The phantom studies and grid search on selecting optimal parameters can offer an alternative way on parameter selection.

[1]  Shing-Hong Liu,et al.  Automated Detection of the Arterial Inner Walls of the Common Carotid Artery Based on Dynamic B-Mode Signals , 2010, Sensors.

[2]  Lihua Li,et al.  A Semi-Automatic Coronary Artery Segmentation Framework Using Mechanical Simulation , 2015, Journal of Medical Systems.

[3]  Marios Politis,et al.  Clinical application of stem cell therapy in Parkinson's disease , 2012, BMC Medicine.

[4]  Donald Geman,et al.  Boundary Detection by Constrained Optimization , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Javad Rahebi,et al.  Retinal Blood Vessel Segmentation with Neural Network by Using Gray-Level Co-Occurrence Matrix-Based Features , 2014, Journal of Medical Systems.

[6]  Da-Chuan Cheng,et al.  Automated localisation and boundary identification of superficial femoral artery on MRI sequences , 2013, Computer methods in biomechanics and biomedical engineering.

[7]  H. Gudbjartsson,et al.  The rician distribution of noisy mri data , 1995, Magnetic resonance in medicine.

[8]  Milan Sonka,et al.  Segmentation of intravascular ultrasound images: a knowledge-based approach , 1995, IEEE Trans. Medical Imaging.

[9]  Hans Burkhardt,et al.  Using snakes to detect the intimal and adventitial layers of the common carotid artery wall in sonographic images , 2002, Comput. Methods Programs Biomed..

[10]  J. Machann,et al.  The Transeurope Footrace Project: longitudinal data acquisition in a cluster randomized mobile MRI observational cohort study on 44 endurance runners at a 64-stage 4,486km transcontinental ultramarathon , 2012, BMC Medicine.

[11]  Francis K. H. Quek,et al.  A review of vessel extraction techniques and algorithms , 2004, CSUR.

[12]  Petia Radeva,et al.  Reliable and Accurate Calcium Volume Measurement in Coronary Artery Using Intravascular Ultrasound Videos , 2016, Journal of Medical Systems.

[13]  Shing-Hong Liu,et al.  Elliptic Shape Prior Dynamic Programming for Accurate Vessel Segmentation in MRI Sequences with Automated Optimal Parameter Selection , 2016, Journal of Medical and Biological Engineering.

[14]  Shing-Hong Liu,et al.  Automatic detection of the carotid artery boundary on cross-sectional MR image sequences using a circle model guided dynamic programming , 2011, Biomedical engineering online.

[15]  Djemel Ziou,et al.  Edge Detection Techniques-An Overview , 1998 .

[16]  Da-Chuan Cheng,et al.  Three-Dimensional Expansion of a Dynamic Programming Method for Boundary Detection and Its Application to Sequential Magnetic Resonance Imaging (MRI) , 2012, Sensors.

[17]  A. Beckett,et al.  AKUFO AND IBARAPA. , 1965, Lancet.

[18]  Suzanne Kieffer,et al.  Feature extraction and selection for objective gait analysis and fall risk assessment by accelerometry , 2011, Biomedical engineering online.

[19]  D. Altman,et al.  STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.

[20]  Francis K. H. Quek,et al.  Vessel extraction techniques and algorithms: a survey , 2003, Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings..

[21]  Xiaoyi Jiang,et al.  Detections of Arterial Wall in Sonographic Artery Images Using Dual Dynamic Programming , 2008, IEEE Transactions on Information Technology in Biomedicine.

[22]  Tomas Gustavsson,et al.  A multiscale dynamic programming procedure for boundary detection in ultrasonic artery images , 2000, IEEE Transactions on Medical Imaging.

[23]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.