921
Dr. Jasjit Suri, is an innovator, scientist, a visionary, industrialist and an internationally known world leader in Biomedical Devices and Biomedical Imaging Sciences – applied to Diagnostics and Therapeutics. He worked as Scientist, Manager; Sr. Director, Vice President and Chief Technology Officer (CTO) level positions with several million dollar industries like IBM, Siemens Medical, Phillips Medicals, Fisher and Eigen Inc., companies.
He has written over 300 publications, 60 innova-tions (patents), 4 FDA clearances, over 20 books in medical imaging and biotechnologies (diagnostic and therapeutic) and has lead as leadership role in releasing products in the men’s and women’s mar-ket applied to the fields of: Cardiology, Neurology (Image Guided Brain Surgery and Spinal Surgery), Urology (Image Guided Prostate Biopsy and HIFU for BPH), Vascular (Atherosclerosis- MR and Ultrasound), Ophthalmology (Thermal Imaging) and Breast Cancer (MR, X-ray-Ultrasound Fusion Guidance) markets.
He received his MS in Neurological MRI from Univ. of Illinois, Chicago, USA, PhD in Cardiac Imaging from University of Washington, Seattle, Washington, USA, and MBA from Ivy League Weatherhead School of Management, Case Western Reserve University, Cleveland, USA. He was crowed with President’s Gold Model and Fellow of American Institute of Medical and Biological Engineering by National Academy of Sciences, DC. He has won over 50 awards during his career. Dr. Suri is also Strategic Advisory Board Member of over half a dozen industries and International Journals in Biomedical Imaging and Technologies. He main interests are cancer imaging for diagnosis and therapeutic applications for men’s and women’s market.
Editor Biographies
Jasjit S. Suri et al. (eds.), Atherosclerosis Disease Management, DOI 10.1007/978-1-4419-7222-4, © Springer Science+Business Media, LLC 2011
922 Editor Biographies
Dr. Chirinjeev Kathuria
Dr. Kathuria holds a Bachelor of Science (B.Sc.) degree and specialized in US Health Care Policy and Administration and a Doctor of Medicine (M.D.) from Brown University. He also holds a Master’s in Business Administration (M.B.A.) from Stanford University. Dr. Chirinjeev Kathuria, M.D., M.B.A. has measurable success in building businesses that impact world economies and shift business models. Dr. Kathuria has cofounded and helped build many businesses, which have gener-ated shareholder wealth and jobs. Dr. Kathuria and affiliated companies have been featured in many TV shows and media publications. Dr. Kathuria has extensive experience in the health-care industry and has consulted to a broad range of organizations in the USA, Europe, and Asia. He helped develop Arthur D. Little biotechnology and health-care policy practice in Europe. He conducted a compara-tive analysis of the European and US biotechnology industries resulting in a paper entitled “Biotechnology in the Uncommon Market” which was published in Biotechnology magazine in December 1992 which helped change at that time the current thinking of biotechnology development. Dr. Kathuria’s coauthored papers include “Selectivity Heat Sensitivity of Cancer Cells,” “Avascular Cartilage as an Inhibitor to Tumor Invasion,” and “Segmentation of aneurysms via connectivity from MRA brain data” the latter was published in the Proceedings of the International Society for Optical Engineering in 1993.
Dr. Filippo Molinari
Dr. Filippo Molinari received the Italian Laurea and the Ph.D. in Electrical Engineering from the Politecnico di Torino, Torino, Italy, in 1997 and 2000, respectively. Since 2002, he has been an assistant professor on the faculty of the Department di Electronics, Politecnico di Torino, where he teaches biomedical signal processing, biomedical image processing, and instrumentation for medical imaging. On March 2009 he was visiting professor at the University of Nagoya, Japan. He is the responsible for the image processing group at the BioLab of the Politecnico di Torino. Dr. Molinari’s main research interests include the analysis of
923Editor Biographies
strongly nonstationary biomedical signals and the medical imaging applied to the computer-aided diagnosis. Dr. Molinari developed several signal and image pro-cessing algorithms, especially in the field of neurology, neurosciences, and in the functional assessment of disabled subjects. Specific interests of Dr. Molinari’s research are early diagnosis, therapy, and rehabilitation. In the last 5 years, Dr. Molinari’s activity was focused on ultrasound imaging in the field of neurology and cardiology. Dr. Molinari is on the Editorial Board of the Journal of NeuroEngineering and Rehabilitation and acts regularly as reviewer for more than 20 international journals in the field of biomedical engineering and medicine. He has published more than 20 technical papers and has written a collaborative book on advances in diagnostic and therapeutic ultrasound. He is member of the Italian Group of Bioengineering, of the IEEE Engineering in Medicine and Biology Society (EMBS) and of the American Institute of Ultrasound in Medicine (AIUM).
925
AAbbott, A.L., 504Accelerated atherosclerosis.
See Iatrogenic conditionAccurate unsupervised segmentation
cylinder matching, 4133D connectivity filter, 414deformable model, 412Gibbs model, 413modified EM
conditional Lagrange maximization, 426
definition, 413DG weights, 426log-likelihood, 425, 426misclassification rate, 427relaxation MM-process, 426
multiscale filtering, 400natural TOF-and PC-MRA images
Chung–Noble’s segmentation, 417, 420–422
3-class LCDG model, 417, 419Gaussian mixtures, 416, 417scaled-up absolute deviations,
417, 418total absolute difference, 420Wilson–Noble’s segmentation, 417,
420–421phantoms
3D geometrical phantoms, 422, 423
erroneous voxels, 422error statistics, 423, 424ground truth, 421, 422qualitative visual analysis, 421Wilson–Noble’s and Chung–Noble’s
segmentation, 422–424Picker 1.5T Edge MRI scanner, 416scale-space filtering, 412
sequential EM-based initialization, 424–425
slice-wise segmentation, LCDG modelsBayesian probability, 415cumulative Gaussian probability
function, 414K-modal, 414, 415probability distribution, 414–415Q-ary intensities, 414segmentation algorithm, 415–416
ACEIs. See Angiotensin-converting enzyme inhibitors
Activated clotting time (ACT), 538Active contours (snakes)-based
segmentationbrightness normalization and
despeckling, 299damping force, 297global energy function definition,
296–297lumen–intima and media–adventitia
layers, 298MSE, 299multiresolution analysis, 297parametric contour representation, 296
Acute myocardial infarction, 377Agaston, A.S., 393Ajduk, M., 391Alberola-Lòpez, C., 312Allam, A.H., 5Altaf, N., 508Angiotensin-converting enzyme
(ACE), 605Angiotensin-converting enzyme inhibitors
(ACEIs), 607, 608Angiotensin II type 1 receptor blockers
(ARBs), 607, 608Anitschkow, N., 26Annexin A5 scintigraphy, 513–514
Index
926 Index
Antihypertensive drugsblood pressure lowering, 610–612cIMT, 613–615first-line antihypertensive drugs
ACEIs and ARBs, 607, 608Ca2+ channel blockers, 608CCBs and BBs, 607, 608health outcomes, 609–610HOPE trial, 608international guidelines, clinical
management, 607hypertension and atherogenesis, 602–604renin-angiotensin system,
antiatherosclerotic drugsACE2, 605angiotensinogen, 604–605AT1 and AT2, 605–606bradykinin, 606–607
Antoniades, C., 282Aoki, S., 472Arauz, A., 512ARBs. See Angiotensin II type 1 receptor
blockersArrhythmias, 43, 44Arterial calcifications detection
arterial plaques detection, in vivo, 684–687contrast enhanced vibro-acoustography,
687–688excised human carotid arteries, 681–682normal arteries, in vivo imaging, 682–684
Arterial plaque characterization techniquesCT plaque imaging, 197IVUS imaging, 198MRI, 197–198
Aschoff, L., 26Atherosclerotic carotid plaque, 382Atherosclerotic disease
complicated atheromasic injuriescalcification, 80ulcers and plaque rupture, 81–82
elementary atheromasic injuriesfatty streak diagram, 75, 76fibroatherosclerotic plaque, 78fibrous plaques, 75intimal hyperplasia, 75–77ulceration, 75, 77
epidemiology, 72etiopathogenetic theories, 82–83fibrous capsule and prognostic significance,
relationship, 79–80Mönckeberg sclerosis, 71normal anatomy, arterial vascular wall,
73–74risk factors, 72–73
ATL HDI-3000 ultrasound scanner, 165
BBalloon, 182, 183Bank, A.J., 89Barker, A.J., 910Bartlett, E.S., 376Bassiouny, H.S., 20, 389b-blockers (BBs), 607, 608Beard, P.C., 806Beck, J., 134Bernoulli’s equation, 91–92Beswick, J.P., 46Biologic nanoparticles and vascular disease
arterial calcification, 749biochemical characterization, 751–753history, 750–751infection, 750microparticles, 757origin and life forms, 753–754transmissible cause of disease, 754–756
B-mode ultrasonographyintima/media thickness, 460–461limitations, 460molecular contrast-enhanced
ultrasonography, 462–463plaque echogenicity, 461plaque irregularity, 462primary screening tool, 460
Boissel, J.P., 576Brathwaite, A., 198Briley-Saebo, K.C., 481Brusseau, E., 770Burckhardt, C.B., 154Butterworth filter, 141, 161
CCABG. See Coronary artery bypass graftingCai, J.M., 389, 443, 444, 464, 506Calcified nodule, 15–16Calcium-channel blockers (CCBs), 607, 608CALEXia. See Completely automated layers
extraction based on integrated approach
Callahan, R.J., 365Capineri, L., 890Cappendijk, V.C., 507Cardiovascular disease (CVD), 564–565
assessment, 283atherosclerosis, 282complement system, 649–651IMT monitoring, risk marker, 286plaque analysis, 286risk assessment, 222
Cardiovascular riskasymptomatic CVD, 39
927Index
carotid diseaseCAD vs. CVD, 38subclinical atherosclerosis, 39
carotid endarterectomy, 41–43controversies, CHD testing, 43–45IMT
cardiac events, 45–46coronary angiography, 45
patients with TIA/stroke, 39–41Carotid artery
B-mode ultrasound images, 254, 256, 265imaging
diagnostic flowchart, 365–366DSA, 366–367FDG-PET, 371–372MRA, 369–371scintigraphy, 373SPECT, 372–373US-ECD, 367–369
pathology and stroke riskdistal embolization, 374hypo-perfusion, 374left mono ocular symptoms, right
hemiplegy and dysesthesia, 373MDCTA, 375–376MRA, 375NASCET, ECST and ACAS evaluation,
374–375TIA/minor stroke, 377US-ECD, 375
ultrasound image segmentationinter-greedy performance samples,
269–272LI performance evaluation, 266–268MA performance evaluation, 266, 268–270system errors, 266
wall thickness, 395–396Carotid artery atherosclerosis. See Carotid
artery stenosisCarotid artery bifurcation ultrasound images,
despeckle filteringadditive noise, 157–158anisotropic diffusion filtering (DsFad), 162–163application, 187–188atherosclerotic plaque characterization, 166coherent nonlinear anisotropic diffusion
filtering (DsFnldif), 163–164evaluation protocol, 187geometric filtering (DsFgf4d), 160–161homomorphic filtering (DsFhomo), 161–162image quality evaluation metrics, 178, 179intima media complex and plaque segmentation
CCA, 181–183longitudinal ultrasound B-mode image,
183, 184
local statistics filteringfirst order statistics filtering
(DsFlsmv, DsFwiener), 158–159homogeneous mask area filtering
(DsFlsminsc), 159–160maximum homogeneity, pixel neighborhood
filtering (DsFhomog), 160median filtering (DsFmedian), 160methodology
despeckle filtering, 165distance measures, 166–167image quality evaluation metrics,
167–169material, 165statistical kNN classifier, 167texture analysis, 166ultrasound images recording, 165univariate statistical analysis, 167visual evaluation, 169–170
speckle definition, 154speckle noise model, 157, 158speckle reduction techniques, 155symptomatic ultrasound image and cardiac
image, 170–172texture analysis
distance measures, 171–176kNN classifier, 173, 177–178univariate statistical analysis, 173–176
validation result, 183, 185visual evaluation, 178, 180–181wavelet filtering (DsFwaveltc), 164–165
Carotid artery longitudinal ultrasound images2-D B-mode ultrasound image, 222–223CALEXia
advantages, real image database, 244–245
CCA automatic recognition (see Common carotid artery)
IMT measurement strategy, 234–237non-perfect adventitial tracings,
conditions, 245–246performance improvement,
247–248performance limiting factors, 247
CULEXsa, 224performance evaluation and
benchmarkingCA automated tracing, 240–242carotid wall segmentation and IMT
measurement, 242–244performance metric design
image database, 236–237IMT metric, 239–240mean system error, 239PDM, 237–239
928 Index
Carotid artery stenosisclinical symptoms
postsurgery evaluation, 820presurgery evaluation, 819–820
ex vivo molecular staining techniquesDNA microarray, 852, 853MMPs, 851protein microarray, 852
limitations, 866, 868multimodal molecular imaging
atherosclerosis and angiogenesis imaging, 860–861
color mapping techniques, 854concepts, 852, 8543D echographic data segmentation,
866, 8673D imaging, nanoparticles, 861–863FDG-PET/CT and MRI, 854imaging principles and techniques,
854–857MALDI imaging technique, 862–865microfluidics, 865–866nanoparticles, 858–860
nanotechnology, 868postsurgery evaluation
carotid artery tissue processing, 846CEA procedure, 845–846endarterectomy specimen, 839, 842–845histopathology and MRI, 849–851MRI (1.5T), ex vivo, 847–849MRS (9.4T), ex vivo, 847, 848plaque components, discriminative
analysis, 849–850plaque histopathology classification, 845surgical procedures, 841–842
presurgery evaluationACC/ACR classification, 826, 828–830aggressive statin treatment, 840–841blood biomarkers, 820–821boundary detection, 829, 838–839CAD evaluation components, 823, 824carotid endarterectomy, 830, 831dyslipidemia, 820imaging modalities, 826, 827lesion components and values, 837–838MRM and MRI (1.5T), in vivo, 834–835multiple contrast technique, 835patient selection criteria, 819, 821, 832plaque, atherosclerosis process, 821plaque development process, 821–822post-surgery diagnostic criteria, 821segmentation, 836TE and TR selection, 836–837T1-w/T2-w/PD-w techniques, 839–840
in vivo imaging techniques, 823–826in vivo MRI images, statistical methods,
832–833stroke, 818
Carotid atherosclerosiscarotid endarterectomy specimen, 5–6carotid vs. coronary disease, 17–18classification
AHA classification scheme, 7–8limitation, AHA, 8–10
imaging modalitiesCT angiography, 23digital subtraction angiography, 22Doppler ultrasound, 22–23inflammation, 25–27MRI, 23–25
ischemic stroke, 4pathologic features
advanced symptomatic lesions, 11–16early, asymptomatic lesions, 10–11lesions with thrombi, 14–16stable atherosclerotic plaque, 16–17
plaque localization, 6–7quantification
total plaque volume (TPV), 331, 332vessel wall volume (VWV),
331, 333, 334regression monitoring
3D and 2D carotid map generation, 337–340
mapping spatial and temporal changes, 340–343
stroke risk, 331TPA measurements, intensive statin
treatment, 334–336VWV measurements, intensive statin
treatment, 335–337risk factors, 18–19symptomatic vs. asymptomatic patients,
19–22Carotid bifurcation, 6–7Carotid duplex ultrasonography (CDUS),
824–825Carotid endarterectomy (CEA), 5, 41–43
anesthesiological technique, 535, 537CABG, 546–547carotid artery pathology and stroke risk, 374postsurgery
carotid artery tissue processing, 846endarterectomy specimen evaluation,
839, 842–845histopathology and MRI, 849–851MRI (1.5T), ex vivo, 847–849MRS (9.4T), ex vivo, 847, 848
929Index
plaque components, discriminative analysis, 849–850
plaque histopathology classification, 845procedure, 845–846selection criteria, 842–843
surgical technique, 540, 541symptomatic and asymptomatic carotid
stenosis, 534Carotid intima–media thickness (cIMT)
antihypertensive drugs, 613–615biomarkers and surrogate endpoints, 578inter-greedy technique
atherosclerotic process, 254CALEXia architecture, 258–259CALEXia, CULEXsa, WS, and IG
algorithm, 266, 274–275cardiovascular disorders, risk marker, 254CULEXsa architecture, 256–258EPV, 273–274IG performance samples, 269–273lumen-intima (LI) performance
evaluation, 266–268MA and LI tracing accuracy, 255media-adventitia (MA) performance
evaluation, 266, 268–270multiple image processing boundary
fusion, 262–264performance evaluation metrics and
image dataset, 264–265system errors, 266WS transform, 259–262
ultrasound trials, 585–587Carotid plaque enhancement (CPE), 394–395Carotid stenosis treatment
anesthesiological techniqueACT, 538CEA, 535, 537cerebral perfusion, 536EEG, 535–536intraoperative stump pressure measure,
538, 539jugular mixed venous O
2 saturation, 536
local anaesthesia, 538NIRS, 536transcranial Doppler, 536
CEA/CABG, 546–547computed tomography angiography, 533duplex scan
anechoic plaque, 530, 531calcific plaque, 530, 531definition, 530echolucency and echogenicity, 530hypoechoic plaque, 531, 532irregular/ulcerated plaque, 531, 533
endovascular techniquecarotid stenting technique, 548–549clinical results, 555common carotid artery access, 549–550diagnostic catheter, 549interdisciplinary collaboration, 555perioperative complications, 555–556pharmaceutical protocol, 553–555protection systems, 550–552stent implantation, 552–553vascular access, 549
magnetic resonance imaging, 532NASCET, ECST and ACAS, 530, 533post-traumatic depression, 529quality check, 543–544shunt, 543surgery results, 547–548surgical technique
CCA, 539–540ECA and ICA, 539–540eversion technique, 542–543IJV, 539SCM, 539standard CEA, 540, 541
symptomatic and asymptomatic carotid stenosis
angiography, spiral CT and angioMR scan, 534, 536
artery morphology and plaques, 534, 535carotid plaque types, 535, 537CEA, 534
TIA, 530, 534urgent surgery, 544–546
Carotid ultrasound images, intima-media thickness measurement
carotid wall evolution, 286–287carotid wall segmentation
active contours (snakes)-based segmentation, 296–299
3-D segmentation methods, 306–307dynamic programming techniques,
295–296edge tracking and gradient–based
techniques, 291, 293–295HT, 304–305instrumental variability, 289integrated approach, 305–306IVUS techniques, 411–413local statistics and snakes, 299–302Nakagami modeling, 302–304noise sources, 289–291normal and pathology, biological
variability, 288–289CCA, 282–283
930 Index
Carotid ultrasound images, intima-media thickness measurement (cont.)
computer measurements and CVD, 286human tracings, correlation
HD, 310MAD, 309–310manual and computer-measured IMT, 313PDM, 311–312percent statistic test, 312–313
supra-aortic circulation, 283vessel wall segmentation, 283–286
3D Carotid ultrasound imagingcarotid atherosclerosis (see Carotid
atherosclerosis)IMT, 326–327manual segmentation, 20scanning technique
cube view approach, 330image reconstruction, 330magnetically-tracked free-hand
scanners, 327mechanical linear scanners, 327–329TPA and VWV, 327
Cassius, Dio, 4CCA. See Common carotid arteryCCBs. See Calcium-channel blockersCDUS. See Carotid duplex ultrasonographyCEA. See Carotid endarterectomyCerebral oximetry, 536Cerebrovascular disease (CVD), 37CE US. See Contrast-enhanced
ultrasonographyCFM. See Color flow mappingChalana, V., 312CHD. See Coronary heart diseaseCheng, D.C., 243, 292, 297, 298, 313Cheng, G.C., 88, 89, 91Chin, 182–184, 186Chiu, B., 341Chlamidia pneumnoniae, 83Chronic total occlusion, 17Chu, B., 467Chung, A.C.S., 413Chung–Noble’s segmentation, 417, 420–424cIMT. See Carotid intima–media thicknessCinthio, M., 777–780Coli, S., 502Color flow mapping (CFM), 886–888Common carotid artery (CCA), 93, 499, 500,
539–540anatomical view, 223automatic recognition
column-wise approach, 225line segments (see Line segments)seed points selection, 225–228
B-Mode image, 300Hough transform, 304–305integrated approach, 305–306Nakagami modeling, 302–304snake-based segmentation techniques,
297–299vessel wall segmentation, 283–286wall points identification, 291, 293
Completely automated layers extraction based on integrated approach (CALEXia)
advantages, real image databasemedia-adventitia (MA) segmentation
error, 244real-time implementation, 245suitability, carotid morphologies, 244user independence, 244
architecture, 258–259CCA automatic recognition (see
Common carotid artery)IMT measurement strategy, 234–236
EPV, 273–274LI segmentation technique, 267–268MA segmentation technique, 268–270mean IMT measurement error, 266,
274–275mean system error, 264non-perfect adventitial tracings, 245–246performance improvement, 247–248performance limiting factors, 247
Completely user-independent layers extraction (CULEX)
algorithm, automated segmentation, 211ceUS image processing, 203–204segmentation and GT comparison, 205–207ultrasound images segmentation strategy,
201–202Completely user-independent layers extraction
algorithm based on signal analysis (CULEXsa), 224
architecture, 256–258EPV, 273–274IMT measurement errors, 266, 274–275mean system error, 264
Computational fluid dynamics (CFD), 98–99Computed tomography (CT)
angiography, 23plaque imaging, 197
Computed tomography angiography (CTA)advanced vascular imaging, 353carotid artery (see Carotid artery)image reconstruction software, 354plaque (see Plaque)post processing techniques
contrast material, 364CPR, 358–359, 361
931Index
MIP and MPR, 358–360opacity, 363projectional and perspective
methods, 357radiation dose, 364–365raycasting, 364transverse and in-plane resolution, 357voxel selection, 363VR, 361–362
principles4-detector-row scanners, 355mathematical image reconstruction, 354MDCTA, 355–356scanning parameters, 356single detector-row scanners, 355third-generation geometry, 355
spatial and temporal resolution, 3543D Connectivity filter, 414Continuous wave (CW) Doppler, 881–883Contrast-enhanced ultrasonography
(CE US), 502advantage, 213B-mode imaging
color-coded image, after analysis and tissue characterization, 197–199
CULEX and manual segmentation comparison, 205–206
CULEX segmentation, plaque, 204–205image after analysis and tissue
characterization, 205–206image enhancement, 204–205processing strategy, 203–204wall tissue enhancement, 203
CULEX automated segmentation, 211limitation, 212plaque characterization and histology
plaque with calcium deposits, 207–209soft unstable plaque, 209–211
Coronary artery bypass grafting (CABG), 546–547
Coronary artery disease (CAD), 37Coronary atherosclerosis, 38Coronary heart disease (CHD), 37
CVD, 565fibrates, 594, 595lipoprotein cholesterol retention, arterial
intima, 572Corti, R., 467, 475, 478, 589CPE. See Carotid plaque enhancementC-reactive protein (CRP), 18Crimmins, T.R., 185Cross-validation approach, 146CTA. See Computed tomography angiographyCULEX. See Completely user-independent
layers extraction
CULEXsa. See Completely user-independent layers extraction algorithm based on signal analysis
CVD. See Cardiovascular disease
DDaubenchies Symlet wavelet, 164Daugman, J.G., 138Davies, J.R., 512DeBakey, M.E., 530De Korte, C.L., 767Delsanto, S., 202, 292, 299–301, 313Destrempes, F., 243, 292, 302, 3104-Detector-row scanners, 355Devereaux, P.J., 44de Weert, T.T., 379, 382, 384, 504, 505Digital subtraction angiography (DSA), 23,
366–367, 458Discrete wavelet packet frames (DWPF), 1343D MRA. See Accurate unsupervised
segmentationDNA microarray, 852, 853Donoho, D.L., 156, 164, 186Doppler, J.C., 880Doppler ultrasound, 22–23Drug therapy, atherosclerosis
antihypertensive drugs (see Antihypertensive drugs)
apoptosis, plaque rupture, and thrombus formation, 574–575
artery diseases, 563–564atherogenesis, 568atheroma lesions, 566atherosclerotic plaques, 567atherothrombosis, 567–568biomarkers and surrogate endpoints
carotid B-mode ultrasound, 578cIMT, 577clinical and statistical characteristics, 576coronary intravascular ultrasound,
578–579gold standard, 575MRI, 579–580plaque volume, 577QCA, 577
cardiovascular morbidity and mortality, 564CVD, 564–565endothelial dysfunction
cardiovascular risk factors, 568–569characteristics, 569gold standard test, 570noninvasive tests, 570ROS, 569shear stress, 568
932 Index
Drug therapy, atherosclerosis (cont.)hypolipidemic drugs (see Hypolipidemic
drugs)lipoprotein cholesterol retention, arterial
intimaCHD, 572cholesterol transport and metabolism,
570, 571chylomicrons, 570–571HDL-C levels, 573hypercholesterolemia, 570LDL-C levels, 572–573lipid triad, 573VLDL, 570–571
primary and secondary prevention, 566proinflammatory oxidized LDL, 573–574risk factors, 565–566
DSA. See Digital subtraction angiographyDunmire, B., 889Duplex ultrasonography, 45
EECA. See External carotid arteryEliasziw, M., 375EPV. See Error per vertexErosive endothelial damage, 82Error per vertex (EPV), 273–274Error summation, Minkowski metric, 168Espeland, M.A., 576, 585External carotid artery (ECA), 539–540
FFaita, F., 274, 292, 295, 315Fast Fourier transform (FFT), 156, 161FDG. See Fluorine-18-labeled 2-deoxy-d-
glucoseFDG-PET. See [18F]-fluorodeoxyglucose
positron emission tomographyFDTA. See Fractal dimension texture analysisFeasby, T.E., 548Fell, G., 368Fenster, A., 306, 307[18F]-fluorodeoxyglucose positron emission
tomography (FDG-PET), 371–372, 512–513
FFT. See Fast Fourier transformFibroatherosclerotic plaque, 78Fibrocalcific plaques, 17Fibrous cap atheroma, 11Fibrous capsule, 78Fibrous plaques, 76Finite element method (FEM), 89
First-order absolute moment edge operator (FOAM), 295
First-principle stress (FPS), 107–108
Fisher, C.M., 457Fluid structure interaction
vs. 3D structure-analysis, 91–92simulation and boundary conditions
CCA, 97fluid flow parameters, 98–99
stress analysisblood flow patterns and wall stress, 90lipid core volume and fibrous cap
thickness, 108–110with multiple patients, 99–106with TIA patients, 106–108
Fluorine-18-labeled 2-deoxy-d-glucose (FDG), 476–477, 483
Fluoroscopic X-ray system, 125Folk, R., 750Fourier power spectrum (FPS), 166, 178Fractal dimension texture analysis (FDTA),
166, 178Frayne, R., 908, 909Frost, V.S., 154–157Frydrychowicz, A., 910Füst, George, 649
GGAE. See Geometric average errorGeertinger, P., 649Geometric average error (GAE),
168, 178Geroulakos, G., 45Giannoni, M.F., 502Glagov, S., 724GLDS. See Gray level difference statisticsGlomset, J.M., 635Golemati, S., 223, 304Golledge, J., 21, 389Gongora-Rivera, F., 38Goodman, J.W., 154Gould, A.L., 591Gradenigo Hospital, 199–200, 213, 265Gray level difference statistics (GLDS),
166, 178Gray-scale median (GSM), 499–500Groen, H.C., 113Grogan, W.E., 530Grønholdt, M.L., 499Grotenhuis, H.B., 910GSM. See Gray-scale medianGutierrez, M.A., 292, 297
933Index
HHaider, N., 739Hansen, H.H.G., 775Hansen, K.L., 898Haralick, R.M., 166Hardie, A.D., 393Harloff, A., 910Hatsukami, T.S., 445, 464Hausdorff distance (HD), 310HDL. See High-density lipoproteinsHealed plaque ruptures (HPRs), 16Heart and coronary arteries, 122, 123Heart Outcome Prevention Evaluation
(HOPE), 608Heat shock proteins (HSP), 641Hematoxylin and eosin (H&E), 126Hemodynamics, cardiovascular systems
echo PIVbasic principle, 899carotid bifurcation model, 900, 902hemodynamics quantification, 901–903microbubbles, 899–901optical PIV, 904–905vortex flow parameters, 904waveforms comparison, 900–902
PC-MRIcarotid, local flow imaging, 911–912developments, 910–911global flow parameters, 907–909local flow parameters, 909methodology, 905–907
speckle trackingcorrelation search algorithm, 892cross-correlation method, 893PWE, 893–895
transverse oscillation, 896–898ultrasound Doppler
color flow mapping, 886–888continuous wave Doppler, 881–883pulsed wave Doppler, 883–886vector Doppler, 888–892
WSS, 879–880Hermans, M.M., 285High-density lipoproteins (HDL),
570–571, 573High-resolution multicontrast magnetic
resonance imagingclassification, 443–445computer-based three-dimensional
analysis, 450fibrous cap status and lipid core,
444–447hemorrhage, 447–449image resolution, 450
limitations, 451MRI 3D surface rendering, 450, 451USPIO, 450
Hill, J.H., 648Hill’s criteria, 756, 758Hodgson, Joseph, 25Holzapfel, G.A., 91Hough transform (HT), 304–305HT. See Hough transformHyperfibrinogenemia, 19Hyperhomocysteinemia, 72Hyperlipemia, 72Hypolipidemic drugs
bile acid sequestrants, 599–600characteristics, 580, 581cholesterol absorption inhibitors,
600–602fibrates
atherogenic dyslipidemia, 593CHD, 594, 595chylomicronemia, 594gallstones, 596HDL-C and LDL-C levels, 593VA-HIT trial, 594
nicotinic acidARBITER 2 trial, 598dyslipidemia, 599FFA levels, 596GPR109A, 596, 598GPR109B, 596HATS trial, 598hyperglycemia, 599LDL and HDL levels, 596, 598multiple tissue enzymes and receptors,
596, 597VLDL levels, 596
statinsangiographic trials, 584–585clinical outcomes, 582, 583coronary intravascular ultrasound, 585,
588, 589LDL receptors, 580magnetic resonance imaging,
589–590pleiotropic effects, 590–593primary prevention, 584secondary prevention, 582, 584ultrasound trials, cIMT biomarkers,
585–587
IIatrogenic condition, 75ICA. See Internal carotid artery
934 Index
Image analysisfluorescence images, 740image intensity, 741lesion regression, 739optical imaging, 740PET imaging, 740–741ultrasound imaging, 740
Imaging modalitiesCT and MR imaging, 739nuclear and optical imaging, 738resolution and sensitivity, 736, 737ultrasound imaging, 736
Imparato, A.M., 458IMT. See Intima-media thickness (IMT)Inflammation control, atherosclerosis
preventionanimal models, 635–636complement system
alternative pathway, 645C3a, C4a, and C5a anaphylatoxins,
647–648cascade, 642–644C57BL mouse model, 651–653classical pathway, 643–645complement inhibitors, 646CVD, 649–651historical notes, 648–649lectin pathway, 645membrane attack complex, 646mode of action, 654–656myocardial infarction, 648reperfusion injury, 656–658VCP, 647
HSP, 641initiation and progression, 636–637LDL and lipid transport, 639–641lesion development stages, 635myocardial infarction, 637–638pathogenesis, 633–634risk factors, 638–639
Internal carotid artery (ICA), 499, 500, 539–540
Internal jugular vein (IJV), 539Inter-slice distance (ISD), 340Intimal hyperplasia, 75–77Intimal xanthoma, 10–11Intima-media thickness (IMT), 45–46, 59
artery wall segmentation, 284–285cardiovascular and cerebrovascular risk
indicator, 282carotid artery sample, echographic
appearance, 285–2863D carotid ultrasound imaging, 326–327cerebrovascular events, 436, 438
computer-assisted automatic measurement, 435, 437
CVD risk assessment, 222definition, 435leading edge method, 435, 436measurement, 181–182
Gaussian kernel, 234image segmentation, 235–236schematic representation, segmentation,
234–235progression and regression, 436, 437risk factor-modifying therapy, 436young populations, 437
Intraplaque hemorrhage (IPH), 466–467Intravascular photoacoustic (IVPA) imaging
angioscopy, 789benchtop imaging system, 789, 790combined IVUS/IVPA imaging, 790–791,
805–810ex vivo artery imaging, 789integrated catheter design
Beard’s probe design, 806light delivery system, 805–808optical fiber bundle design, 808, 809phantom images, 808, 809prototypes, 806–807ultrasound array and fire fiber, 808–810
laser fluence, 789molecular and cellular specific
IVPA imagingatherosclerosis-related biomarkers, 795contrast agents, 796macrophages, atherosclerosis animal
model, 799–801macrophages, Au NPs, 796–798
optical absorption coefficient, 789spectroscopic IVPA imaging
correlation based approaches, 795first derivative, 792–793lesions composition, 793–794multi-wavelength, photoacoustic
response, 793optical absorption spectra, 792rabbit aorta samples, 794
stent deployment3D image construction, 802, 805malapposition, 801MRI, CT and OCT, 802rabbit aorta, 802–805stenting procedure and
post-surgery, 801stents vs. vessel structure, 803, 804tissue-mimicking phantom, 802–803
vessel-mimicking phantom, 790, 791
935Index
Intravascular ultrasound (IVUS) imaging, 577–579, 788–789
arterial plaque characterization techniques, 198
atherosclerotic tissue characterization algorithms
blood flow, 144–145consistency among PH images, 142, 143histological image interpretation,
145–146pressure change, 142, 144tissue classification, 146–147tissue signatures variability, 141–142
carotid wall segmentation, 411–413combined IVUS/IVPA imaging, 805–810neurological evaluation and
management, 56spectral and RF based approaches
IVUS-IB, 129–133IVUS-VH, 129–131spatial autocorrelation function, 128spectral signature, 128
techniques, 307–309texture based approaches
IVUS-ECOC, 136–138IVUS-IBH, 138–140IVUS-PH, 134–136
therapeutic procedure, 121ultrasound virtual histology, 287in vitro set-up and specimen preparation
histology matching problem, 124ROIs, 127
in vivo acquisition, 122–124IPH. See Intraplaque hemorrhageIVPA. See Intravascular photoacoustic
imagingIVUS. See Intravascular ultrasound imagingIVUS elastography (IVE), 131–133IVUS-error correcting output codes
(IVUS-ECOC), 136–138IVUS-image based histology (IVUS-IBH),
138–140IVUS-integrated backscatter (IVUS-IB)
color-coded maps, 130–132in vitro IVUS grayscale images, 131vs. IVUS-VH, 131, 133
IVUS-prognosis histology (IVUS-PH), 134–136IVUS-virtual histology (IVUS-VH), 129–131
JJahromi, A.S., 368Jensen, J.A., 896Jeremias, A., 136
KKafetzakis, A., 45Kanai, H., 772, 775Kasai, C., 887Katz, J., 832Kaufmann, B.A., 733, 736, 739Kelly, K.A., 733Kerwin, W.S., 509Kietselaer, B.L., 373, 513Kim, D.I., 380Kim, K., 774Kitamura, A., 462k-nearest-neighbour (kNN), 167, 173, 177–178Koch’s Postulates, 755–756, 758Kooi, M.E., 474, 510, 511Kovanen, P.T., 282Kuan, D.T., 154–156Kwee, R.M., 499–501, 503
LLai, 182–184, 186Lal, B.K., 204, 212LaMuraglia, G.M., 547Lancelot, E., 481Landesberg, G., 43Laplacian pyramid-based nonlinear diffusion
(LPND), 290Laufer, E.M., 282Law, M.R., 611Laws texture energy measures, 166, 177–178LCDG. See Linear combination of discrete
GaussiansLDL. See Low-density lipoproteinsLee, J.S., 154–157, 183Lee, R.T., 89Lee, S.J., 477Lemarie-Battle filter, 135Levy interdistribution distance, 420Liang, Q., 292, 296Liguori, C., 292–295Lima, J.A., 579, 589Linear combination of discrete Gaussians
(LCDG)3-class LCDG model, 417, 419initial LCDG model, 417, 418, 423–425slice-wise segmentation
Bayesian probability, 415cumulative Gaussian probability
function, 414K-modal, 414, 415probability distribution, 414–415Q-ary intensities, 414segmentation algorithm, 415–416
936 Index
Line segmentsfitting
combinability and validation, 229–230energy function, 228geometric features measurement, 228intersection energy, 230iterative line segment formation, 227–228linear discriminator, 227, 230sample image detection, 231–232
recognition and classification, 232–233Lin, W., 472Lipid-rich necrotic core (LRNC)
conventional MRI, 507, 508non-invasive imaging, carotid
atherosclerosis, 514–515Lisauskas, Jennifer, 127Li, Z.Y., 91, 389Lizzi, F.L., 129Lobregt, S., 297Local binary pattern (LBP binary), 140Local statistics and snakes
automatic detection, lumen points, 299–300fuzzy K-means classifier, 301snake formulation, 301user–independent segmentation,
carotid wall, 302Loizou, C.P., 287, 289–292, 299, 314Long, G.W., 547Loree, H.M., 388Lovett, J.K., 114, 382Low-density lipoproteins (LDL), 570–573LRNC. See Lipid-rich necrotic coreLucev, N., 288
MMAC. See Membrane attack complexMacKinnon, A., 503MAD. See Mean absolute distanceMagnetic resonance angiography (MRA)
carotid artery imaging, 369–370fibrous cap, 389
Magnetic resonance imaging (MRI), 23–25arterial plaque characterization techniques,
197–198biomarkers and surrogate endpoints,
579–580bright-blood technique, 61, 63conventional MRI
gadolinium-based contrast agents, 506, 507
hemorrhages and calcifications, 508LRNC, 507, 508multisequence non-CE MRI, 506, 508
pre and postcontrast T1-weighted TSE images, 506, 507
T1-weighted TFE images, 507, 5082D modeling, 91dynamic CE MRI, 509dynamic contrast-enhanced MRI and
neovascularisation, 472–473expansive remodeling, 467–468fibrous cap and lipid rich-necrotic core,
464–465fibrous cap disruption and platelet
aggregation, 465–466flow modeling, shear stress estimation,
469, 471FSI models, 92geometry reconstruction reproducibility, 92IPH, 466–467long image acquisition times, 463multi-contrast imaging, 93physiological loading condition, 96plaque criticity, 56plaque vulnerability, 92rupture, plaque morphology
information, 106SE-TSE technique, 60severity of stenosis, 466superficial calcified nodules, 468–470three dimensional (3D)
data acquisition, 463vs. ultrasound imaging, 56USPIO-enhanced MRI, 510–511USPIO-enhanced MRI and macrophage
content, 473–475virtual histology, 55
MALDI imaging technique, 862–865Malik, J., 162, 163Manca, G., 373Markl, M., 910Markus, H.S., 503Maroko, P.R., 657Masden, E, 270Mathias, K., 548Mathur, K.S., 38MATLAB, 212Matlab, 154, 157Matrix metalloproteinases (MMPs), 481, 851Maurice, R.L., 132, 772, 773Mauriello, A., 19Maximum-intensity projection (MIP),
358–360MDCT. See Multidetector-row computed
tomographyMean absolute distance (MAD),
309–310
937Index
Mean squared error (MSE), 156, 157, 168, 179, 299
Membrane attack complex (MAC), 646Metabonomics
analysis techniquescorrelation analysis, 705–706discriminant analysis, 707–708multi-way ANOVA, 706PCA, 706PLS, 707
and atherosclerosis, 700–701classification, 713, 715database and patients
hematochemical variables, 703, 704instrumental data, 702–703patient population, 701–702
database reduction, 708–710patient analysis
eigenvalues, 710, 711hyperplane, PCA subjects distribution,
710–712hyperplane, PLS subjects
distribution, 712original variables weight, 710, 711PCA components, 711PLS-DA classifier, 712–713
plaque typology, 713, 714Minimum-mean-square error (MMSE)
criterion, 156Mintz, G.S., 147MIP. See Maximum-intensity projectionMissel, E., 147Molecular and cellular specific
IVPA imagingatherosclerosis-related biomarkers, 795contrast agents, 796macrophages, atherosclerosis animal model,
779–801macrophages, Au NPs, 796–798
Molecular imagingcontrast agents, 734–736homing ligands
antibodies, 733competitive binding assays, 734interactions, 732–733SELEX process, 734short peptides, 733
image analysisfluorescence images, 740image intensity, 741lesion regression, 739optical imaging, 740PET imaging, 740–741ultrasound imaging, 740
imaging modalitiesCT and MR imaging, 739nuclear and optical imaging, 738resolution and sensitivity, 736, 737ultrasound imaging, 736, 738
molecular markersatheroma components, 725–726atherosclerosis animal models, 727characteristics, 724–725phage display technique, 726–727receptor identification, 727screening strategy, 726
potential molecular targetsadhesion molecules, 728–729apoptosis, 731expression pattern, 725, 727–728fibrin deposition and thrombus formation,
731–732neovessel formation, 729–730oxidized LDL and foam cells, 729proteolytic enzymes, 730–731
principles, 724Molinari, F., 265, 288, 289, 292, 301, 302,
305, 306, 314, 316Monney–Rivilin model (AnsysTM ), 97Montecucco, F., 282Moody, A.R., 391, 467, 507Moore, W.S., 458Moran, P.R., 907MPR. See Multi planar reconstructionMRA. See Magnetic resonance angiographyMRI. See Magnetic resonance imagingMSE. See Mean squared errorMultidetector-row computed tomography
(MDCT), 504–505Multi-detector row computed tomography
angiography (MDCTA)carotid artery imaging, 371–372fibrous cap, 390pathology and stroke risk, 375–376vs. US-ECD, 380–381
Multimodal molecular imagingatherosclerosis and angiogenesis imaging,
860–861color mapping techniques, 8543D echographic data segmentation,
866, 8673D imaging, nanoparticles, 861–863FDG-PET/CT and MRI, 854imaging principles, 854–856imaging techniques, 856–857MALDI imaging technique, 862–865microfluidics, 865–867nanoparticles, 858–860
938 Index
Multi planar reconstruction (MPR), 358, 360Multiscale filter, 412Multi-way ANOVA analysis, 705, 706, 709Munk, P., 896Myocardial infarction (MI), 41, 436
NNahrendorf, M., 738, 739Nair, A., 129, 136Nasu, K., 198Navone, Roberto, 76–78, 81, 82Near-infrared spectroscopy (NIRS), 536Neighbourhood gray tone difference matrix
(NGTDM)distance measures, 166–167, 171–173kNN classifier, 167, 173, 177–178texture features extraction, 166
Neurological evaluation and managementclinical examination, 59ethical issues, 58–59exclusion criteria, 58instrumental examinations, 59–60laboratory and hematochemical exams, 59objectives and end points
atheromasic plaque, 56IVUS, 56US vs. MRI, 56–57
patients inclusion criteria, 57–58results and impact
plaque composition, 65sensibility and specificity, plaque, 65
sample instrumental dataangio-MRI, 60–61bright-blood MRI characterization, 61, 63carotid endarterectomy, 62NASCET criterion, 61sonographic appearance, 62, 64
strokeischemic stroke, 53prevention and management, 54–56stenosis, 54
NGTDM. See Neighbourhood gray tone difference matrix
Noble, J.A., 413Non-invasive imaging
arterio-embolic strokes, 433B-mode ultrasound, 434–435carotid plaque, 434carotid plaques characterization, 498degree of stenosis, 437, 439echolucent plaque, 516high-resolution multicontrast MRI
classification, 443–444
computer-based three-dimensional analysis, 450
fibrous cap status and lipid core, 444–447hemorrhage, 447–449image resolution, 450limitations, 451MRI 3D surface rendering, 450, 451USPIO, 450
IMTcerebrovascular events, 436, 438computer-assisted automatic
measurement, 435, 437definition, 435leading edge method, 435, 436progression and regression, 436risk factor-modifying therapy, 436young populations, 437
ipsilateral TIA/stroke, 515LRNC, 514–515MDCT, 504–505MES positive symptomatic and
asymptomatic patients, 514MRI
conventional MRI, 506–508dynamic CE MRI, 509USPIO-enhanced MRI, 510–511
nuclear imaging techniquesannexin A5 scintigraphy, 513–51418F-FDG PET, 512–513
origin of stroke, 439–440plaque neovasularization, 500plaques morphology and texture
histology, 440, 441stable plaque, 442–443stages of atherosclerosis, 440–442
statin therapy, 516TCD, 502–504ultrasonography
CE US, 502conventional B-mode US, 499–501
vulnerable plaque, 498Non-invasive targeting,
vulnerable carotid plaquesB-mode ultrasonography
intima/media thickness, 460–461limitations, 460molecular contrast-enhanced
ultrasonography, 462–463plaque echogenicity, 461plaque irregularity, 462primary screening tool, 460
clinical trialsATHEROMA study, 480CEU techniques, 481
939Index
endovascular treatment, 477FDG-PET, 480high-risk plaques, 478, 479LRNC, 478, 480METEOR study, 480ORION study, 478, 480–481pravastatin, 478rosuvastatin, 478, 480simvastatin, 478statin treatment, 478VWA and VWT, 478
culprit plaques, 458diagnostic imaging methods, 459DSA, 458luminal stenosis, 458, 459MMPs, 481molecular imaging, 482–486molecular-targeted media, 482MRI
dynamic contrast-enhanced MRI and neovascularisation, 472–473
expansive remodeling, 467–468fibrous cap and lipid rich-necrotic core,
464–465fibrous cap disruption and platelet
aggregation, 465–466flow modeling, shear stress estimation,
469, 471IPH, 466–467long image acquisition times, 463severity of stenosis, 466superficial calcified nodules,
468–469three dimensional (3D)
data acquisition, 463USPIO-enhanced MRI and macrophage
content, 473–475nuclear imaging and ultrasonography,
481–483PET and SPECT, 475–477plaque characteristics,
459–460Nuclear imaging, 738
OOhara, T., 393Ohayon, J., 89, 112Oikawa, M., 26Okubo, M., 135Ombrellaro, M.P., 42Ophir, J., 767Optical imaging, 738, 740Overbeck, J.R., 889, 890
PPaigen, B., 636Papaharilaou, Y., 908, 909Partial differential equation (PDE), 162–163Partial least squares (PLS), 707, 712, 714Paterson, J.C., 391PCA. See Principal component analysisPDE. See Partial differential equationPDM. See Polyline distance metricPeak signal-to-noise ratio (PSNR), 168,
178–179Pearson’s R coefficient, 313Percent atheroma volume (PAV), 335, 337Performance evaluation and benchmarking
CA automated tracingCALEXia performances, 240–242CALEXia vs. ground truth tracings, 242PDM, 240
carotid wall segmentation and IMT measurement, 242–244
Perona, P., 162, 163PET imaging, 740–741Phage display technique, 726–727Phosphate buffered saline (PBS), 124Picker 1.5T Edge MRI scanner, 416Pignoli, P., 254, 291Plane wave excitation (PWE),
893–895Plaque
automated plaque analysis, 396–397calcification, 392–393carotid plaque enhancement, 394–395carotid plaque volume, 387–388eccentricity and remodelling, 393erosion, 15fibrous cap
arterial remodelling, 388automatic computer classifier
algorithm, 390contrast material gadolinium, 389fibrous connective tissue, 388juxtaluminal band, 389MDCTA, 390MRA, 389
hemorrhage and rupture, 379intraplaque haemorrhage, 391luminal narrowing, carotid
vulnerable plaqueacute myocardial infarction, 377atherosclerotic plaque, 378plaque classification, 378–379
smooth surface, 379surface irregularities, 379, 380thrombus, 391–392
940 Index
Plaque (cont.)types, analysis
ANOVA testing, 383carotid endarterectomy, 383cerebrovascular symptoms, 383, 386fatty, mixed and calcified plaques,
383–384hypercholesterolemia and
hyperfibrinogenemia, 386hypodense regions, 385–386lipid-lowering drug therapy, 386ROI, 384–385
ulcerationsatherosclerotic carotid plaque, 382CTA, 383definition, 379hypercholesterolemia, 380ischemic cerebral event, 380luminal stenosis, 381, 382MDCTA vs. US-ECD, 380–381
Plaque stress analysiscarotid plaque reconstructions
3D geometry reconstruction, 95–96MR imaging acquisition, 93plaque components segmentation,
93–953D structure-analysis vs. FSI, 91–922D vs. 3D structure, 90–91FEM, 90FSI simulation and boundary conditions
CCA, 97fluid flow parameters, 98
lipid core volume and fibrous cap thickness, 108–110
modeling procedure uncertainties analysisaxial stretch, 112geometry reconstruction reproducibility,
110–111material model definition, 111–112residual stress, 112
multiple patientsfluid domain results, 100plaque morphological impact,
103–106wall tensile stress, 100–102
rupture hypothesisde-bonding effect, 89local maximum stress, 88–89in vitro balloon angioplasty, 89
TIA patients, 106–108PLS. See Partial least squaresPolyline distance metric (PDM)
CA automated tracing, 240CALEXia performance, 244–245
carotid ultrasound images, intima-media thickness measurement, 311–312
performance metric design, 237–240Porsche, C., 383Positron emission tomography (PET),
475–477Prabhakaran, S., 462Principal component analysis (PCA), 706,
710–711, 714Protein microarray, 852Psaty, B.M., 609PSNR. See Peak signal-to-noise ratioPulsed wave (PW) Doppler, 883–886PWE. See Plane wave excitation
QQuantitative coronary angiography
(QCA), 577
RRadiograph, CEA, 5–6Raff, M.R., 365RAS. See Renin-angiotensin systemRayleigh and Rician probability density
function (PDF), 158Reactive oxygen species (ROS), 569Redgrave, J.N., 381, 388Regions of interest (ROIs)
local marking, 124–127systematic marking, 127
Renin-angiotensin system (RAS)antiatherosclerotic drugs
ACE2, 605angiotensinogen, 604–605AT1 and AT2, 605–606bradykinin, 606–607
hypertension and atherogenesis, 602–604
Ribbers, H., 775Rician distributions, 413RMSE. See Root mean squared errorRobert, R., 727Romero, J.M., 394Root mean squared error (RMSE), 168,
178–179Rossi, A.C., 223, 240, 288Ross, R., 26, 635Rothwell, P.M., 379, 392, 458, 534Roubin, S.G., 549Rudd, J.H., 372, 476, 477, 512Run-length method, 140Russell–Movat pentachrome, 126–127
941Index
SSaam, T., 197, 446, 447, 506Saba, L., 374, 376, 380, 382, 384, 389,
390, 394Sabetai, M.M., 461Salonen, J.T., 460Saloner, D., 910Sanz-Requena, R., 309SAPPHIRE, 44Savory, W.S., 373Scabia, M., 890Schroder, 3833-D Segmentation methods, 306–307Sethuraman, S.R., 790SFM. See Statistical feature matrixSGLDM. See Spatial gray level dependence
matricesShah, F., 502Shah, M., 181, 182, 184, 186, 296Shah, P.K., 113Sheikh, H.R., 169Shi, H., 778Signal-to-noise ratio (SNR), 168, 178–179Singh, N., 447, 508Single detector-row scanners, 355Single photon emission computed
tomography (SPECT), 475–477Sitzer, M., 502Snake-based segmentation strategy.
See Completely user-independent layers extraction algorithm based on signal analysis
SNAKES algorithm, 838, 839Solid domain parameter, 99Sorensen, H., 649Spagnoli, L.G., 19, 21, 465Spatial gray level dependence matrices
(SGLDM)distance measures, 171, 173feature extraction, 166univariate statistical analysis,
173, 176Speckle noise
CCA, 299Nakagami modeling, 302noise source, 289–290
Spectroscopic IVPA imagingcorrelation based approaches, 795first derivative, 792–793lesions composition, 793–794multi-wavelength, photoacoustic
response, 793optical absorption spectra, 792rabbit aorta samples, 794
Spence, J.D., 504SSIN. See Structural similarity indexStaessen, J.A., 610Stary, H.C., 440, 818Statistical feature matrix (SFM), 166,
177–178Statistical k-nearest-neighbour classifier
filtering method, 167filter performance investigation, 170texture analysis, 173, 177–178
Staub, D., 463Stein, J.H., 292, 294Stenosis severity, 21Sternocleidomastoid muscle (SCM), 539Stoica, R., 229Strain (shear) imaging, vulnerable plaques
detectionfatty streaks, 766intravascular strain imaging, 769–770non-invasive shear strain imaging
techniquesecho-tracking, 777–778radiofrequency-based ultrasound,
778–779relative lateral shift, 778
non-invasive strain imaging techniquescross-correlation-based methods,
773–774Doppler-based methods, 772registration-based method, 772–773ultrasound beam alignments, 771
schematic representation, plaques, 766transverse cross-sections
a-line based beam steering, 775image-based beam steering and
compounding, 775–777ultrasound strain imaging, 767–769
Structural similarity index (SSIN), 169, 178, 179
Suri, J.S., 221–248, 253–276, 281–316Sztajzel, R., 389
TTahara, N., 372, 477, 480Takaya, N., 18, 24, 25, 447, 459, 466, 508Tang, D., 89, 92Tawakol, A., 372, 476, 512TCD. See Transcranial DopplerTheron, J.G., 550Thin cap fibrous atheroma, 12–14Thitaikumar, A., 777Thorbjörnsdottir, P., 658TIA. See Transient ischemic attacks
942 Index
Time-of-flight magnetic resonance angiography (TOF-MRA), 411, 413
Tortoli, P., 285Total plaque area (TPA)
intensive statin treatment, 334–337intima media thickness (IMT),
326, 327scanning technique, 327
Total plaque volume (TPV)carotid atherosclerosis quantification,
331, 332IMT, 326, 327
Touboul, P.J., 292, 293Toussaint, J.F., 443Touze, E., 464Touzè, E., 40Trahey, G.E., 892, 893Transcranial Doppler (TCD), 502–504Transient ischemic attacks (TIA), 20, 39–41,
106–108, 530, 534Tree structure, 134, 135Triglyceride-rich lipoproteins
(TGRLP), 570Trivedi, R.A., 389, 464, 471, 474, 511Tunica adventitia, 73Tunica intima, 634, 635
UUdesen, J., 893U-King-Im, J.M., 471Ultrasonography (US)
CE US, 502conventional B-mode US
CCA, 499, 500echolucent plaques, 499GSM measurement, 499–500ICA, 499, 501pixel segmentation, 500standard B-mode US vs. compound US,
500, 501Ultrasound (US).
See also Ultrasonographyatheromasic disease, 60imaging, 736, 738, 740instrumental diagnosis, 65mechanical radiations, 55vs. MRI imaging, 56–57pharmacological therapies, effects, 65serial evaluations, 56
Ultrasound contrast agents, plaque characterization
advantage, 213
atherosclerotic process, 196CA ultrasound examination advantage,
198–199ceUS B-mode imaging
color-coded image, after analysis and tissue characterization, 205–207
CULEX segmentation, plaque, 204–205
CULEX vs. manual segmentation, 205–206
image after analysis and tissue characterization, 205–206
image enhancement, 204–205processing strategy, 203–204wall tissue enhancement, 203
ceUS plaque characterization and histology
plaque with calcium deposits, 207–208
soft unstable plaque, 209–211experimental protocol and
patients selectionGradenigo Hospital, 199–200testing protocol, 200–201
IMT risk indicator, advantage, 196–197
limitation, 212MATLAB implementation, 212techniques, 197–198ultrasound images segmentation strategy
CULEX segmentation, 202CULEX structure, 201
Ultrasound echo color Doppler (US-ECD)carotid artery imaging, 367–369vs. MDCTA, 380–381pathology and stroke risk, 377
Ultrasound strain imaging, 767–769Underhill, H.R., 468, 469, 507Universal quality index, 169, 178, 179Urbinati, S., 42US. See UltrasonographyUS-ECD. See Ultrasound echo color doppler
VVaccinia virus complement control protein
(VCP)complement inhibitor, 647diet-induced atherosclerosis model,
651–653myocardial damage, 658
van der Lugt, Aad, 387, 397van Der Meer, R.W., 910van der Wal, A.C., 9
943Index
Vascular disease and biologic NPsarterial calcification, 749biochemical characterization,
751–753FBS-derived NPs, 755Hill’s criteria, 756history, 750–751infection, 750Koch’s Postulates, 755–756microparticles, 757origin and life forms, 753–754
VCP. See Vaccinia virus complement control protein
Vector Dopplerblood velocity measurement, 889cross-beam Doppler, 888Doppler shift, 889–8902D vector Doppler, 891–892Overbeck’s system, 890vector velocity mapping, 890–891
Verdecchia, P., 611Very low-density lipoproteins (VLDL),
570–571Vessel wall segmentation
structure, 283–284ultrasound longitudinal B-Mode image,
284–285Vessel wall volume (VWV)
carotid atherosclerosis quantification, 331, 333, 334
3D and 2D carotid map generationatorvastatin and placebo
treatment, 339CCA and ICA, 340vessel wall and plaque thickness,
337, 338IMT, 327intensive statin treatment
ANOVA, 337atorvastatin and placebo treatment, 337carotid bifurcation, 335manual planimetry, 335PAV, 335, 337transverse and longitudinal 3D
ultrasound, 336, 337mapping spatial and
temporal changescarotid artery wall and lumen
segmentation, 340carotid stenosis, 341, 342flattened 2D thickness map, 341image segmentation, 341ISD, 340manual planimetry, 340
plaque and wall thickness changes, 343scan-rescan 2D thickness
difference maps, 343scanning technique, 327
Viator, J., 806Vibro-acoustography
arterial calcifications detectionarterial plaques detection, in vivo,
684–687contrast enhanced vibro-acoustography,
687–688excised human carotid arteries, 681–683normal arteries, in vivo imaging,
682–684clinical potential, 691detection sensitivity, 688–689exposure safety, 690image resolution, 681, 683, 689limitations, 690–691principle, 680–681quantitative measurements, 689–690ultrasound methods, 679
Vicenzini, E., 287Viergever, M.A., 297Virchow, R., 4, 25Virmani, R., 378VLDL. See Very low-density lipoproteinsVolume rendering (VR), 361–362Von Mises stress (VWTS), 99von Rokitansky, Carl, 4VR. See Volume renderingVulnerable plaque, 80VWTS. See Von Mises stress
WWagner, R.F., 154Waki, 389Wald, D.S., 46Walker, L.J., 381Wall shear stress (WSS), 879Wall tensile stress
fibrous cap, 100–101stress distributions, peak systole,
101, 104VWTS distribution, 100–101, 103
Wang, J.G., 613Warburton, E., 476Wardlaw, J.M., 377Ward, P.A., 648Warwick, R., 286Wasserman, B.A., 367, 395Watershed (WS) transform,
259–262
944 Index
Wendelhag, I., 295, 296Williams, D.J., 181–184, 186, 296Wilson, D.L., 413Wilson–Noble’s segmentation, 417, 420–423Wintermark, M., 390, 391, 397World Health Organization, 222, 282WSS. See Wall shear stress
YYang, J.-M., 806Yasuda, N., 605
Yonemura, A., 468, 589
Yuan, C., 446, 850
ZZahalka, A., 307Zhang, 290Zhao, S.Z., 92Zhao, X.Q., 19Zheng, J., 92