Quantitative Ultrasound and Photoacoustic Imaging for the Assessment of Vascular Parameters

Medical imaging has been forever revolutionized by the technological and digital boom that has occurred over the last few decades. The idea of quantitative analysis of medical images by a computer was first reported in the 1960s (Lodwick et al, Radiology, 80(2):273–275, 1963, [1], Meyers et al, Radiology 83(6):1029–1034, 1964, [2], Winsberg et al, Radiology 89(2):211–215, 1967, [3], Kruger et al, IEEE Trans Biomed Eng, 3:174–186, 1972, [4], Kruger et al, IEEE Trans Syst Man Cybern, 1:40–49, 1974, [5], Toriwaki et al, Comput Graph Image Process, 2(3):252–271, 1973, [6]), and at that time it was generally assumed that computers could replace medical practitioners in detecting abnormalities, because computers and machines are better at performing certain tasks than human beings are. However, growth of this sector remained initially quite limited due to the fact that computers were not sufficiently powerful, advanced image-processing techniques were not available, and digital images were not easily accessible (Doi, Comput Med Imaging Graph, 31(4–5):198–211, 2007, [7]). Since those days, along with the evolution of technology and digital imaging in general, the idea of actually replacing medical practitioners has also evolved, bringing forth the idea of Computer Aided Diagnosis (CAD), in which the computer output can by utilized by medical practitioners, but not replace them. This field, which is based on the idea that digital medical images are analyzed quantitatively by computers, has spread widely and quickly, becoming one of the major research subjects in medical imaging. Therefore, the development of advanced image processing techniques is required in order to obtain quantitative information (Doi, Comput Med Imaging Graph, 31(4–5):198–211, 2007, [7]).

This Chapter focuses on the development and validation of quantitative techniques for the assessment of vascular morphological parameters that can be extracted from B-mode ultrasound longitudinal images of the common carotid artery. In particular, results from numerous past studies (Molinari et al., Comput. Methods Progr. Biomed. 108, 946–960, 2012, [1], Molinari et al., Software. Int. Angiol. 31(1), 42–53, 2012, [2], Ikeda et al., Int. Angiol. J. Int. Union Angiol. 32(3), 339–348, 2013, [3], Saba et al., Diabet. Res. Clin. Pract. 100(3), 348–353, 2013, [4], Molinari et al., Med. Phys. 39(1), 378–391, 2012, [5], Meiburger et al., GIMT: Generalized IMT measurement in carotid ultrasound images with plaque: an automated method, [6]) will be presented, ranging from the validation of techniques for correctly locating the CCA in B-mode ultrasound images, the development and implementation of novel completely automated techniques for the IMT measurement and plaque segmentation, and the validation and association of the automatically measured IMT value with clinical parameters.

This Chapter (The contents of this chapter build upon the paper: K.M. Meiburger, F. Molinari, J. Wong, L. Aguilar, D. Gallo, D.A. Steinman, U. Morbiducci, “Validation of the carotid intima-media thickness variability (IMTV): Can manual segmentations be trusted as Ground Truth?”, In: Ultrasound in Medicine and Biology, http://​dx.​doi.​org/​10.​1016/​j.​ultrasmedbio.​2016.​02.​004) continues the study on morphological vascular studies for quantitative imaging techniques with clinical B-mode ultrasound images and specifically focuses on the validation of the intima-media thickness variability parameter. Recent studies have shown that the irregularity of the IMT along the carotid artery wall has a stronger correlation with atherosclerosis than the IMT itself, so in this chapter, the Intima-Media Thickness Variability (IMTV), a parameter defined to assess the IMT irregularities along the wall, is studied and validated. In particular, whether or not manual segmentations of the lumen-intima and media-adventitia can be trusted as ground truth in the calculation of the IMTV parameter is analyzed. A total of 60 simulated ultrasound images with a priori IMT and IMTV values were used. The images were simulated using the Fast And Mechanistic Ultrasound Simulation (FAMUS) software and presented 5 different morphologies, 4 nominal IMT values and 3 different levels of variability along the carotid artery wall (no variability, small variability, and large variability). Three experts manually traced the lumen-intima (LI) and media-adventitia (MA) profiles and two automated algorithms were used to obtain the LI and MA profiles. One expert also re-traced the LI and MA profiles to test intra-reader variability. The average IMTV measurements of the ground truth profiles used to simulate the longitudinal B-mode images were equal to \(0.002\pm 0.002\) mm, \(0.149\pm 0.035\) mm, and \(0.286\pm 0.068\) mm for the cases of no variability, small variability, and large variability. The IMTV measurements of one of the automated algorithms showed statistically similar values (\(p>0.05\), Wilcoxon signed rank) when considering small and large variability, but non-significant values when considering no variability (\(p<0.05\), Wilcoxon signed rank). The second automated algorithm showed statistically similar values in the small variability case. Two Readers’ manual tracings, however, produced IMTV measurements with a statistically significant difference considering all three variability levels; the third Reader, on the other hand, showed a statistically significant difference in both the no variability and large variability case. Moreover, the error range between the Reader and automatic IMTV values was approximately 0.15 mm, which is on the same order of small IMTV values, showing how manual and automatic IMTV readings should be not used interchangeably in clinical practice. Thanks to the results found in this study, it can be concluded that expert manual tracings should not be considered reliable in the IMTV measurement and therefore should not be trusted as Ground Truth. On the other hand, the first automated algorithm was found to be more reliable, showing how automated techniques could therefore foster the analysis of the carotid artery intima-media thickness irregularity.

This Chapter (The contents of this chapter build upon the paper: F. Molinari, K. M. Meiburger, P. Giustetto, S. Rizzitelli, C. Boffa, M. Castano, and E. Terreno, “Quantitative Assessment of Cancer Vascular Architecture by Skeletonization of High Resolution 3D Contrast-Enhanced Ultrasound Images: Role of Liposomes and Microbubbles”, In: Technology in Cancer Research and Treatment, 13(6):541–550, 2014) opens the second section of this work, which is based on emphasizing quantitative imaging techniques for the assessment of architectural parameters of vasculature that can be extracted from 3D volumes. Using contrast-enhanced ultrasound (CEUS) imaging, it was demonstrated how the characterization and description of the vascular network of a cancer lesion in mouse models can be effectively determined using both traditional microbubbles and liposomes. Eight mice were administered both microbubbles and liposomes and 3D CEUS volumes were acquired. Vascular architectural descriptors were calculated after a skeletonization technique was applied. The accurate characterization and description of the vascular network of a cancer lesion is of critical importance in clinical practice and cancer research in order to improve diagnostic accuracy or to assess the effectiveness of a treatment. The aim of this study was to show the effectiveness of liposomes as an ultrasound contrast agent to describe the 3D vascular architecture of a tumor. Eight C57BL/6 mice grafted with syngeneic B16-F10 murine melanoma cells were injected with a bolus of 1, 2-Distearoyl-sn-glycero-3-phosphocoline (DSPC)-based non-targeted liposomes and with a bolus of microbubbles. 3D contrast-enhanced images of the tumor lesions were acquired pre-contrast, after the injection of microbubbles, and after the injection of liposomes. The 3D representation of the vascular architecture in these three conditions was obtained with a previously developed reconstruction and characterization image processing technique,. Six descriptive parameters of these networks were also computed: the number of vascular trees (NT), the vascular density (VD), the number of branches, the 2D curvature measure, the number of vascular flexes of the vessels, and the 3D curvature. Results showed that all the vascular descriptors obtained by liposome-based images were statistically equal to those obtained by using microbubbles, except the VD which was found to be lower for liposome images. All the six descriptors computed in pre-contrast conditions had values that were statistically lower than those computed in presence of contrast, both for liposomes and microbubbles. Liposomes have already been used in cancer therapy for the selective ultrasound-mediated delivery of drugs. This work demonstrated their effectiveness also as vascular diagnostic contrast agents, therefore proving that liposomes can be used as efficient “theranostic ”(i.e. therapeutic \(+\) diagnostic) ultrasound probes.

This chapter (the contents of this chapter build upon the paper: K.M. Meiburger, S.Y. Nam, E. Chung, L.J. Suggs, S.Y. Emelianov, and F. Molinari, “Skeletonization algorithm-based blood vessel quantification using in vivo 3D photoacoustic imaging”, In: Physics in Medicine and Biology, 61(22), 2016) closes the second section of this work, which is based on emphasizing quantitative imaging techniques for the assessment of architectural parameters of vasculature that can be extracted from 3D volumes. A skeletonization technique for the quantitative assessment of vascular architecture in burn wounds was developed and validated using completely non-invasive photoacoustic imaging, thus not requiring any contrast agent administration. It was shown how this technique can provide quantitative information about the vascular network from photoacoustic 3D images that can distinguish healthy from diseased tissue. Blood vessels are the only system to provide nutrients and oxygen to every part of the body. Many diseases have significant effects on blood vessel formation, so the vascular network can be a cue to assess malicious tumor and ischemic tissues. Various imaging techniques can visualize blood vessel structure, but their applications are often constrained by expensive costs, contrast agents, ionizing radiations, or a combination of the above. Photoacoustic imaging combines the high-contrast and spectroscopic-based specificity of optical imaging with the high spatial resolution of ultrasound imaging, and image contrast depends on optical absorption. This enables the detection of light absorbing chromophores such as hemoglobin with a greater penetration depth compared to purely optical techniques. A skeletonization algorithm for vessel architectural analysis using non-invasive photoacoustic 3D images acquired without the administration of any exogenous contrast agents is presented in this chapter. 3D photoacoustic images were acquired on rats (n \(=4\)) in two different time points: before and after a burn surgery. A skeletonization technique based on the application of a vesselness filter and medial axis extraction is proposed to extract the vessel structure from the image data and six vascular parameters (number of vascular trees (NT), vascular density (VD), number of branches (NB), 2D distance metric (DM), inflection count metric (ICM), and sum of angles metric (SOAM)) were calculated from the skeleton. The parameters were compared (1) in locations with and without the burn wound on the same day and (2) in the same anatomic location before (control) and after the burn surgery. Four out of the six descriptors were statistically different (VD, NB, DM, ICM, \(p<0.05\)) when comparing two anatomic locations on the same day and when considering the same anatomic location at two separate times (i.e., before and after burn surgery). The study demonstrates how it is possible to obtain quantitative characterization of the vascular network from 3D photoacoustic images without any exogenous contrast agent which can assess microenvironmental changes related to disease progression.