目的: 相位對比磁振造影常應用在血壓波的測量，評估動脈硬化的程度。然而，觀察血壓波的傳遞時常需要耗時地手動圈選血管上的多個區段，以便作流速以及距離上的分析。我們的目標是結合影像分割的技術和血流的特性，實作一個能夠自動追蹤血管路徑，在血管上自動圈選多個區段的工具，並由流速影像所提供的速度資訊來做血壓波的分析。
方法: 由於相位對比磁振造影的影像直接反映出了流速資訊，因此血管相較於靜態組織更容易呈現在影像上。在這項研究中，我們以頸動脈以及它的下游血管為主來測量血壓波。藉由水平集影像分割演算法能夠將影像上的血管分割出來，而血管的流速包含了流向資訊。因此，我們在分割出來的血管上遊選定了一個區段，並由該區段的流向資訊來決定下一個區段的位置，重複相同的步驟，便能完成一條血管路徑。在路徑上選定好區段之後，由血壓波在每一個區段所經過的距離以及所耗費的時間，來計算出血壓波的傳遞速度。我們在左右頸動脈各測量一次血壓波，並和原始的流速影像比較血壓波傳遞的延遲。
結果: 追蹤出來的血管路徑幾乎與實際血管一致，能夠包含大部分的頸動脈，最遠甚至能夠到中腦動脈。在大部分的受試者中，在流速影像上左右頸動脈的傳遞延遲也能夠反應在計算出來的血壓波上。
結論: 從結果中可以看出，透過這個方法可以使頸動脈的血壓波測量上達到接近自動化，因此很有機會使其他血管路徑，例如從脊椎動脈到基底動脈，獲試其他的流速影像受益。

Purpose: Phase contrast MRI (PC-MRI) has been widely applied to pulse wave velocity (PWV) measurement to detect the arterial stiffness. However, in order to observe PWV propagation, manual ROI selection for many regions of vessel, which is time-consuming, is often required for the flow velocity and distance analysis. Our purpose is develop a tool that can trace vascular paths automatically for the multiple regions selection, based on an image segmentation technique and blood characteristics, and acquire the velocity information from the velocity maps to assess PWV.
Method: Since PC-MRI images directly reflect the information of flow velocity, vessels are more visible than the static tissue. In this study, we mainly focus on PWV measurement of bilateral internal carotid arteries (ICA) and their downstream. Vessel shape can be extracted from the original images by using level set image segmentation, containing the information of flow direction. As a result, we define a region in the upstream vessel, and define the next region according to the flow direction of the previous region. By repeating the same step, a vascular path can be traced. After determining each region on the vascular path, based on the travelling distance for pulse wave to reach each region and the corresponding time-cost, PWV can be computed. We perform PWV measurement on both right and left ICA to compare their latency in propagation to the original velocity maps.
Result: Traced vascular paths almost match to the actual vessels, containing most of ICA, or even reaching middle cerebral arteries (MCA). In most subjects, latency between right and left ICA of the original velocity maps can be reflected on the measured PWV.
Conclusion: Since the result shows that it is feasible to measure the PWV propagation in ICA nearly automatically, it is believed that this approach can benefit other vascular path such as from vertebral artery to the basilar artery, and the other velocity images.

Reference

[1] Andrew L. Wentland, Thomas M. Grist, Oliver Wieben, “Review of MRI-based measurements of pulse wave velocity: a biomarker of arterial stiffness”, Cardiovascular Diagnosis and Therapy, vol. 4, no. 5, pp.193-206, 2014
[2] Arun A. Joseph, Martin Fasshauser, Klaus-Dietmar Merboldt and Jens Frahm, “Aortic Pulse Wave Velocities Using Real-Time Phase-Contrast MRI”, The Open Medical Imaging Journal, vol. 9, pp.9-14, 2015
[3] Samuel W. Fielden, Brandon K. Fornwalt, Michael Jerosch-Herold, Robert L. Eisner, Arthur E. Stillman, and John N. Oshinski, “A New Method for the Determination of Aortic Pulse Wave Velocity Using Cross-Correlation on 2D PCMR Velocity Data”, Journal of Magnetic Resonance Imaging, vol. 27, pp.1382-1387, 2008
[4] Chenyang Xu, Dzung L. Pham, and Jerry L. Prince, “Image Segmentation Using Deformable Models”, Handbook of Medical Imaging, vol. 2, Medical Image Processing and Analysis, 2000
[5] Markus Persson, Jan Erik Solem, Karin Markenroth, Jonas Svensson, and Anders Heyden, “Phase Contrast MRI Segmentation Using Velocity and Intensity”, Scale Space and PDE Methods in Computer Vision: 5th International Conference, Scale-Space 2005, pp. 119-130, 2005
[6] Chunming Li, Chenyang Xu, Changfeng Gui, and Martin D. Fox,
“Distance Regularized Level Set Evolution and Its Application to Image Segmentation”, IEEE Transactions on Image Processing, vol. 19, no. 12, December 2010
[7] Michael Markl, “Techniques in the Assessment of Cardiovascular Blood Flow and Velocity”, Cardiovascular Magnetic Resonance Imaging, Chapter 8, 2008
[8] Joachim Lotz, Christian Meier, Andreas Leppert, Michael Galanski, “Cardiovascular Flow Measurement with Phase-Contrast MR Imaging: Basic Facts and Implementation”, RadioGraphics, vol. 22, pp.651– 671, 2002
[9] Jeffrey Tsao, Peter Boesiger, and Klaas P. Pruessmann, “k-t BLAST and k-t SENSE: Dynamic MRI with High Frame Rate Exploiting Spatiotemporal Correlations”, Magnetic Resonance in Medicine, vol. 50, no. 5, pp. 1031–1042, 2003
[10] Sen Jia and Ran Yang , “On Analysis of k-t BLAST for Dynamic MRI”, 2011 9th IEEE International Conference on Control and Automation (ICCA), December , 2011
[11] V Kamalaveni, R Anitha Rajalakshmia, K A Narayanankutty, “Image Denoising using Variations of Perona-Malik Model with different Edge Stopping Functions”, Procedia Computer Science, vol. 58, pp. 673-682, 2015
[12] Michael Markl, Wolf Wallis, Stefanie Brendecke, Jan Simon, Alex Frydrychowicz, and Andreas Harloff, “Estimation of Global Aortic Pulse Wave Velocity by Flow-Sensitive 4D MRI”, Magnetic Resonance in Medicine, vol. 63, pp. 1575-1582, 2010
[13] Samuel W. Fielden, BS, Brandon K. Fornwalt, BS, Michael Jerosch-Herold, Robert L. Eisner, PhD, Arthur E. Stillman, and John N. Oshinski, “A New Method for the Determination of Aortic Pulse Wave Velocity Using Cross-Correlation on 2D PCMR Velocity Data”, Journal of Magnetic Resonance Imaging, vol. 227, pp.1382-1387, 2008