Olender Max L, Athanasiou Lambros S, Michalis Lampros K, Fotiadis Dimitris I, Edelman Elazer R
Department of Mechanical Engineering and the Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139 USA.
Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139 USA; Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115 USA.
IEEE J Sel Top Signal Process. 2020 Oct;14(6):1210-1220. doi: 10.1109/jstsp.2020.3002385. Epub 2020 Jun 15.
Intravascular ultrasound (IVUS) imaging is widely used for diagnostic imaging in interventional cardiology. The detection and quantification of atherosclerosis from acquired images is typically performed manually by medical experts or by virtual histology IVUS (VH-IVUS) software. VH-IVUS analyzes backscattered radio frequency (RF) signals to provide a color-coded tissue map, and is the method of choice for assessing atherosclerotic plaque . However, a significant amount of tissue cannot be analyzed in reasonable time because the method can be applied just once per cardiac cycle. Furthermore, only hardware and software compatible with RF signal acquisition and processing may be used. We present an image-based tissue characterization method that can be applied to entire acquisition sequences for the assessment of diseased vessels. The pixel-based method utilizes domain knowledge of arterial pathology and physiology, and leverages technological advances of convolutional neural networks to segment diseased vessel walls into the same tissue classes as virtual histology using only grayscale IVUS images. The method was trained and tested on patches extracted from VH-IVUS images acquired from several patients, and achieved overall accuracy of 93.5% for all segmented tissue. Imposing physically-relevant spatial constraints driven by domain knowledge was key to achieving such strong performance. This enriched approach offers capabilities akin to VH-IVUS without the constraints of RF signals or limited once-per-cycle analysis, offering superior potential information acquisition speed, reduced hardware and software requirements, and more widespread applicability. Such an approach may well yield promise for future clinical and research applications.
血管内超声（IVUS）成像在介入心脏病学中广泛用于诊断成像。从获取的图像中检测和量化动脉粥样硬化通常由医学专家手动进行，或通过虚拟组织学IVUS（VH-IVUS）软件进行。VH-IVUS分析反向散射射频（RF）信号以提供彩色编码的组织图，是评估动脉粥样硬化斑块的首选方法。然而，由于该方法每个心动周期只能应用一次，因此大量组织无法在合理时间内进行分析。此外，只能使用与RF信号采集和处理兼容的硬件和软件。我们提出了一种基于图像的组织表征方法，该方法可应用于整个采集序列以评估病变血管。基于像素的方法利用动脉病理学和生理学的领域知识，并利用卷积神经网络的技术进步，仅使用灰度IVUS图像将病变血管壁分割成与虚拟组织学相同的组织类别。该方法在从多名患者获取的VH-IVUS图像中提取的斑块上进行训练和测试，所有分割组织的总体准确率达到93.5%。施加由领域知识驱动的物理相关空间约束是实现如此强大性能的关键。这种丰富的方法提供了类似于VH-IVUS的功能，而没有RF信号的限制或每个周期有限的分析，提供了卓越的潜在信息获取速度、降低的硬件和软件要求以及更广泛的数据适用性。这种方法很可能为未来的临床和研究应用带来希望。
