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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5835_Библиотеки_им_академика_М_И_Перельмана.pdf
H
Heart
blood flow through, 283
conduction system, 285
cross-sectional view, 284
diagnosis, 283–309
Heart failure (HF), 247
Heart sounds
abnormal, 288–89
abnormal S1, 288–89
abnormal S2, 289
auscultatory areas, 285–86
clicks, 292–93
first (S1), 287
fourth (S4), 288
gallop, 291–92
heartbeat, 284
maneuvers, 294, 295
murmur classifications, 289–91
normal, 287–88
opening snaps, 293–94
origin of, 284–94
phonocardiograph and, 294–309
rate and rhythm, 286–87
second (S2), 287–88
software-aided analysis, 283
third (S3), 288
Heaviside function, 336
Hidden Markov model (HMM), 307
HIFU
ablation, interfaces effect, 390–95
acoustical field measurement, 370–71
acoustical monitoring, 369–70
applications, 351
attenuation measurement, 368, 374–76
beam, 352
brain tumor ablation, 351
cancer tumor ablation, 351
challenges, 362
clinical use, 362
defined, 349, 367
delivery and control planning, 359
delivery parameters, 355
delivery via craniotomy, 354
dosimetry, 353–54
dynamically focused treatment, 353
effect examples, 350
exposure duration, 358
generalized strategy, 355
general purpose therapy-planning software,
361
high-precision dosimetry, 350
homeostasis of blood vessels, 351
instrumentation, 351, 352
integration and visualization, 358–59
introduction to, 367–68
kidney ablation, 376–85
for kidney and liver, 367–95
lesion mechanism, 353
lesions, 381
liver ablation, 386–90
main goal, 367
MRI-guided, 353
parameters, 381, 386
pretherapy imaging, 350
pretreatment imaging, 355–56
recent developments, 351–54
simulation, 354, 357–58
simulation model, 372–74
software, 370
software design and tool integration,
359–62
target definition, 356–57
temperature profiles, 355
therapy guidance, 351–52
therapy planning, 349–63
therapy planning strategies, 354–62
tissue ablation, 350
tissue damage, 352
transducers, 352
validation, 362
in vitro experiments, 371–72
in vivo experiments, 372
Higher order statistics (HOS), 299
High-intensity focused ultrasound. See HIFU
Ho-Kashyap learning algorithm (HKL), 309
Holosystolic murmur, 290
Homeostasis of blood vessels, 351
Homo filter, 44
Homogeneity
lesion, 214
speckle reduction, 38, 39
Homogeneous mask area filtering, 42
Homog filter, 42–43
Homomorphic filtering, 44
Homomorphic Wiener filtering, 229
Hyperthermia, 402–3
I
Image database, 127–28
Image enhancement via sigmoid mapping,
148–49, 170
Image normalization, 96–97
Image quality evaluation, 93–108
424 Index
error summation (Minkowski metric),
98–99
GAE, 99
importance, 93
material, 95–96
methodology, 94–98
metrics, 50, 54–56, 98–100
MSE, 98
normalization, 96–97
PSNR, 99–100
results, 101–5
RMSE, 98
SNR, 99
speckle reduction, 96
SSIN, 100
statistical analysis, 97–98
texture analysis, 97–98
universal quality index, 100
visual perception, 100–101
Implicit snake, 206
Incorrect segmentation rate (ISR), 339
Initial value formulation, 316–17
Inspiration, 295
Integrated System for the Support of the
Diagnosis for the Risk of Stroke
(IASIS) project, 108
Intensity prior extraction, 333–34
Interfaces in HIFU ablation, 390–95
MRI analysis, 392
results, 392–95
See also HIFU
Interslice distance (ISD), 77
Interstitial laser photocoagulation (ILP), 14,
406
Interstitial laser thermotherapy (ILT), 406
Intima-media thickness (IMT), 111
average error, 131
measurements, 130–33
Intrastandard error of measurement (SEM), 79
Isovolumetric indices, 251–52
Iterative closest point (ICP) algorithm, 85
K
K-curvature BAS statistics, 151
Kernel function, 305
Kidney ablation, 376–85
axial MR images, 383
HIFU parameters, 381
MR images, 385
MRI parameters, 381–82
results, 376–79, 382–85
under MRI guidance, 380–85
K-means (KM), 190
KNN classifier, 40, 50, 54
Kuan filter, 38
L
Lagragian techniques, 316
Laplace filtering, 313
Laser-induced interstitial thermotherapy
(LITT), 406
Laser thermal ablation (LTA), 406–7
Late diastolic murmur, 290
Late systolic murmur, 290
Laws texture energy measures (TEM), 49
Lee filter, 38, 40
Left semilateral position, 295
Left ventricle. See LV
Left ventricle systolic dysfunction. See LVSD
Lesions, 408
acoustic shadow, 214
area (AR), 213, 219
automatic classification, 189–220
circularity (CT), 214, 219
contour difference, 177–78
HIFU, 353, 381
homogeneity, 214
kidney, 392
in pig kidney in vitro, 377, 378, 379
postcompressed, 175
precompressed, 175
protuberance (PT), 214, 219
shape difference, 175–76
size distribution of lobes, 214
thermal, 377
true and fitted, 155–56
volume difference, 175
See also Automatic lesion classification;
Breast lesion classification
Level set formulation, 206–9
for smoothing ROI, 206
speed function, 207, 208
Level set method, 315–18
applications, 317–18
boundary value formulation, 316
front evolution, 315–16
initial value formulation, 316–17
See also Prostate analysis
Level set segmentation, 335
basic theory, 145–46, 170–73
discussion, 183
image-based features, 172–73
method, 145–47
methodology and curve evolution, 147, 172
Index 425
Level set segmentation (continued)
summary, 146
theory, 145–46
Linear mechanical 3-D scanners, 6
Liver ablation, 386–90
HIFU parameters, 386
MRI analysis, 386–87
results, 387–90
Lobulation index (LI), 196
Local statistics filtering, 41–42
first-order, 41–42
homogeneous mask area, 42
Low-dose-rate (LDR) brachytherapy, 15
Lsminsc filter, 42
Lsmv filter, 42
Lumen-intima, 130–34
LV
contour shape, 267
delineation of, 263–65
ejection phase indices, 250–51
end-systolic wall stress, 260
function assessment, 249–57
geometry, 266
isovolumetric indices, 251–52
long axis, 267
lumen, 248
motion, 267
pressure-volume analysis, 252–56
pressure-volume relationship, 249
real-time volume, 262–63
S3 gallop (LV S3G), 292
S4 gallop (LV S4G), 292
systolic performance, 249
volume estimation, 231
wall, 266
wide-angled scan, 274
LVSD, 248
anatomic M-mode, 270–71
centerline method, 268–69
color kinesis method, 269–70
defined, 247
delineation of left ventricle, 263–65
detection and treatment, 247
echocardiographic examinations, 257–63
identification, 263–72
illustrated, 248
quantification of systolic dysfunction,
265–66
radial line mode, 271
regional area method, 272
region of dysfunction identification, 266–72
LV volume
calculation, 276
echocardiographic measurement, 258–59
forward stroke, 261
mitral regurgitant, 261
pyramidal, 275
quantification, 275
M
Magnetically tracked free-hand 3-D carotid
ultrasound imaging, 71–72
Magnetic field sensors, 10, 72
Mamdani method, 263
Maneuvers, 294, 295
Manual planimetry method, 79
Mathematical morphology definitions, 211–12
MATLAB, 370
Maximum homogeneity over a pixel
neighborhood filtering, 42–43
Mean-square error (MSE), 39, 98, 233
Mechanical linear 3-D carotid US imaging,
69–70
Mechanical linear scanners, 70
Mechanical scanning, 5–9
advantages, 5, 70
motorized mechanism, 5
Media-adventitia, 130–34
Median filtering, 42
Microwave ablation, 405–6
Mid diastolic murmur, 290
Mid systolic murmur, 290
Minimally invasive surgery (MIS), 401
Min/Max flow, 327, 328
3-D surface after, 329
motion curve under, 327
to smooth 3-D surface, 328
Mitral annulus motion (MAM), 262
Mitral area, 286
Mitral valve opening snap (MVOS), 293
best heard, 293
differential diagnosis, 294
See also Opening snaps
M-mode imaging, 270
Morphological chains, 211–12
MRI
3-D, 3
brain tumor images, 356
kidney ablation, 380–85
monitoring, 390
parameters, 381–82, 386–87
pulse sequences, 381, 382, 387
rapid continuous temperature mapping, 351
in thermal therapy guidance, 380
426 Index
thermometry, 352
Multilayer feed-forward neural network
(MFNN), 196, 197
Multilayer perceptrons (MLPs), 336
Multiplanar reformatting, 11–12
Multiple autocorrelation matrixes
(MULTI-US), 194
Multisegmentation algorithm, 202
Mumford-Shah (MS) function, 322–25, 326
defined, 322
level set approach to solving, 322–25
Murmurs
classifications, 289–90
list of, 290
mechanisms, 291
Myocardial field estimation, 242
Myocardial performance index, 251
N
Needle
angulation accuracy, 26
placement accuracy, 25
segmentation, 22, 28
targeting accuracy, 26, 27
tracking accuracy, 26–28
Negative predictive value (NPV), 158, 181,
217
Neighborhood gray tone difference matrix
(NGTDM), 48–49
Neural networks, 189, 191, 307–8
artificial (ANNs), 194
multilayer feed-forward (MFNN), 196, 197
solid breast nodules with, 195–98
threshold (TNN), 197–98
Nldif filter, 46
Noise
CNR, 289, 386, 388
SNR, 99
speckle as source, 37
Nonejection clicks, 293
Nonextensive characteristic, 199
Nonextensive segmentation, 200–211
algorithm for image binarization, 201–2
level set formulation, 206–9
NESRA, 191, 203–5
SVM theory, 209–11
Nonextensive Segmentation Recursive
Algorithm (NESRA), 191, 203–5
advantages, 219
application, 212
as image binarization algorithm, 205
iteration, 205
recursions, 212
results, 213
Normalized speckle reduction (NDS) images,
98, 103
Number of substantial protuberances and
depressions (NSPD), 196, 197
O
Opening snaps, 293–94
mitral valve (MVOS), 293, 294
tricuspid valve (TVOS), 293
See also Heart sounds
Optical flow
algorithm, 238
computing, 237
correlation-based, 231
dynamic cardiac information from, 237–42
estimation, 227
real-time 3-D US with, 230–37
surface tracking with, 233
tracking, 227, 230
Optimal separating hyperplane (OSH), 210
Orthogonal view plane, 73
P
Partial differential equation (PDE), 44, 149
Pattern recognition, 314
Peak SNR (PSNR), 99–100
Percent Statistics algorithm, 125–26, 133–34
Performance
automatic lesion classification, 217–18
CAD system, 158–59
CULEX, 132, 137, 138
prostate analysis, 338–39
Phonocardiograms (PCGs), 298
CWD distribution of, 306
decomposition of, 300–301
instantaneous frequency, 304
recordings, 300
STFT of, 302
Wigner distribution, 304
Phonocardiography, 294–309
amplifier, 297
analysis, 297–98
autoregressive modeling, 305–6
classification, 307–9
defined, 294
feature extraction, 306–7
filter, 297
instrument description, 296–97
segmentation of S1 and S2, 298–300
transducer, 296–97
Photodynamic therapy (PDT), 14, 406
Index 427
Photorefractive keratotomy (PRK), 407
Plaques
border irregularities measurement, 115
change, 85
contours, 89
dimension measurements, 115
distribution change, 86
echolucent, 137, 139
gray-scale classification, 115
imaging, 134–38
local distribution/change, 83–86
lumen boundary, 82
morphological characterization, 67
progression analysis, 76
progression/regression monitoring, 75
roughness, 82
stable, 136–37
surface morphology, 82–83
types, 95–96, 114–15
ultrasound analysis of, 114–15
unstable, 137–38
Plaque segmentation, 73–74, 115
analysis results, 81
automated, 79–81
user-independent technique, 134
Plaque volume
measurement, 73–87
measurement accuracy, 77–78
measurement steps, 75
measurement variability, 78–79
measurement variability reduction, 88
relative mean, 77
simulated measurement error, 78
studies of, 74–77
vessel wall plus, 76
Plateau, 290
Positive predictive value (PPV), 158, 181, 217
Postcompressed lesions, 175
Posterior probability, 335
Precompressed lesions, 175
Preload recruitable stroke work (PRSW), 255,
256
Preprocessing subsystem architecture
anisotropic diffusion filtering, 148
image enhancement via sigmoid mapping,
148–49
See also Computer-aided diagnosis (CAD)
Pressure-volume analysis, 252–56
Pretherapy imaging, 350
Pretreatment imaging, 355–56
Probabilistic intensity models, 333
Probability density function (PDF), 40
Probability of cavitation (POC), 386, 387, 393
Prompt standing, 295
Prostate
early-stage cancer management, 14
phantom, 27
US-guided brachytherapy, 15
Prostate analysis, 313–42
active contours, 318–25
conclusions and future work, 340–42
flow chart, 326
introduction, 313–15
level set method, 315–18
results, 339–40
system performance evaluation, 338–39
TRUS images, 325–38
Prostate segmentation, 17–19
3-D, 325–38
initialization, 18
propagation, 19
refinement, 19
views, 339
Protuberance, lesion, 214, 219
Proximal flow acceleration, 261
Pulmonic area, 286
Pulmonic ejection clicks, 292
Q
Q-entropy, 200
QLAB segmentation, 235
smooth segmentation, 235
surfaces, 233
QLAB software, 232, 233, 234
Quadratic TFRs, 300
Quality evaluation, 93–108
error summation (Minkowski metric),
98–99
GAE, 99
image normalization, 96–97
importance, 93
material, 95–96
methodology, 94–98
metrics, 50, 54–56
MSE, 98
PSNR, 99–100
RMSE, 98–99
scanners, 94–95
SNR, 99
speckle reduction, 96
statistical analysis, 97–98
structural similarity index (SSIN), 100
texture analysis, 97–98
universal quality index, 100
428 Index
See also Carotid artery US
R
Radial displacement, 240
Radial line method, 271
Radio-frequency ablation (RFA), 404–5
Radionuclide angiography (RNA), 262
Real-time 3-D echocardiography, 228, 272–76
full matrix array, 273
sparse array transducer, 273
volume-rendered imaging, 273–76
Real-time 3-D ultrasound
clinical data, 234–37
correlation-based optical flow, 231
datasets, 232
dog data, 234
evaluation, 233–34
with optical flow, 230–37
quantitative information, 230
results, 234–37
surface tracking, 232–33
surface tracking with optical flow, 233
Real-time LV volume, 262–63
Real-time volume rendered 3-D imaging,
273–76
Reconstruction of 3-D US images, 72–73
Recumbent position, 295
Reduced interference distributions (RIDs), 300
Regional area method, 272
Region-based active contour models, 321–25
MS function solution, 322–25
Mumford-Shah (MS) function, 322
See also Active contours
Regularizers, 319–21
Bayesian-based, 320
clustering-based, 320
coupling-surfaces, 321
shape-based, 320–21
Resilient backpropagation (Rprop), 337
RF ablation, 404–5
Right ventricle. See RV
ROC curves
analysis, 182, 183, 184
for CAD system, 215, 219
classification strategy using, 180–81
ROI
after morphological chain application, 213
detection, 128–29
feature extraction, 213
identification, 120
total area, 212–13
ROI extraction, 116–17, 118–22
example, 122
major threat to, 128
steps, 118–19
Root mean-square-error (RMSE), 98, 233
between OF tracking and manual tracing,
236
radial difference for initialized OF, 236
results, 234
value comparison, 235
Rotational scanning, 7–9
RV
S3 gallop (RV S3G), 292
S4 gallop (RV S4G), 292
S
Scanning
2-D array, 11
3-D ultrasound techniques, 4–5
free-hand, 9–11
mechanical, 5–9
parameters, 70
rotational, 7–9
Seed implantation, accuracy, 28
Segmentation
3-D strain imaging and, 173–74
approaches, 117–18
in Bayesian interface, 334–38
carotid artery US image, 116–18
correct rate (CSR), 339
CULEX, 122–24
image, with high blood backscattering, 129
Incorrect rate (ISR), 339
in level set framework, 335
lumen-intima interface, 130–34
mean, 134
of media-adventitia, 130–34
needle, 22
nonextensive, 200–211
plaque, 73–74, 79–81, 115
prostate, 17–19, 325–38
QLAB, 233, 235
of S1 and S2, 298–300
subsystem based on level set framework,
149–50
user-independent technique, 134
voxel number, 339
Segmentation errors, 126–27
absolute, 130–33
CULEX technique, 131
distribution, 131
IMT measurements and, 130–33
Self-organized maps (SOMs), 190
Index 429
Shannon entropy, 218
Shape-based regularizers, 320–21
Shape difference, normalized, 175–76
Shift distance, 167, 178–79
average, 179
motion estimation concept, 178
Short-term Fourier transform (STFT), 300,
301–2
of normal PCG, 302
windows, 301
Sigmoid mapping, 148–49, 170
image enhancement via, 148–49, 170
results, 171
Signal-to-noise ratio (SNR), 99
Similarity measurement
analysis, 156–57
benign cases, 157
discussion, 160
malignant cases, 157
means, 158
true lesion and fitted lesion, 155–56
Simpson’s rule, 257
Simulation model, 372–74
attenuation effect, 373–74
estimation of lesion size, 373
estimation of temperature, 372–73
estimation of thermal dose, 373
See also HIFU
Sitting up, 295
Sitting up, leaning forward, 295
Software-aided heart sound analysis, 283
Solid breast nodules, with neural networks,
195–98
Solidity, 167
Sonablate 200TM, 409–10
Sparse array transducer, 273
Spatial gray-level dependence matrices
(SGLDMs), 48, 97
Speckle, 37
decorrelation, 10
defined, 37
index, 39
Speed function
defined, 207
sign of, 208
Spline interpolation, 314
Stable plaques, 136–37
CULEX performance, 139
CULEX segmentation, 136
Standard deviation (STD), 25
Statistical enhancement, 229
Statistical feature matrix (SFM), 49
Statistical features (SF), 48
Strain features, 174–79
discussion, 183
experimental results, 184
normalized contour difference, 177–78
normalized shape difference, 175–76
normalized volume difference, 175
shift difference, 178–79
with SVM classifier, 183
See also 3-D strain imaging system
Strain imaging
2-D continuous, 167–68
3-D breast US, 165–84
Stroke force, 255
Structural similarity index (SSIN), 100
Subextensive entropy, 199
Superextensive entropy, 199
Support vector machine (SVM), 166, 189, 191,
309
applications, 209
with B-spline kernel, 220
classification strategy using, 180–81
classifier, 166, 309
in combining features, 179
diagnostic system, 219
as discriminant tool, 214
framework, 191, 214
nonlinear, 211
output with B-spline kernel, 216
output with polynomial kernel, 215
performance, 179
testing, 217
theory, 209–11
Surface tracking, 232–33
Surgical resection, 401
Symmetric correspondence algorithm, 83
Systolic dysfunction
identification, region of, 266–72
quantification of, 265–66
T
Target registration error (TRE), 23, 24
Texture analysis, 48–49
FDTA, 49
FPS, 49
GLDS, 48
laws TEM, 49
NGTDM, 48–49
results, 51–54
SFM, 49
SGLDMs, 48
statistical features, 48
430 Index
Thermal ablation, 401–12
chemical ablation, 403–4
concluding remarks, 411–12
cryoablation, 403
focused ultrasound-based ablation, 407–10
FUSBOTs, 410–11
hyperthermia and, 402–3
introduction, 401–2
laser, 406–7
mechanisms, 402–4
microwave ablation, 405–6
RF ablation, 404–5
therapeutic modalities, 404–10
Thickening
results, 241
segmental averaged, 242
values, 242
Threshold neural network (TNN), 197–98
Threshold selection, 160–61
Tilt 3-D mechanical scanners, 7
Tracked 3-D US with articulated arm sensing,
9
Tracking
acoustic, 9
boundary, 242
direct, 242
needle, 26–28
by speckle decorrelation, 10
surface, 232–33
surface, with optical flow, 233
Transrectal ultrasound (TRUS) images, 315
3-D prostate segmentation from, 325–38
3-D shape prior extraction, 327–33
evaluation, 338
intensity prior extraction, 333–34
segmentation in Bayesian interface, 334–38
Transthoracic 3-D echocardiography, 274
Tricuspid area, 286
Tricuspid valve opening snap (TVOS), 293
Tsallis entropy, 198–200
calculation, 201
defined, 199
T-Surfaces, 207
Tunica adventitia, 112
Tuning geometric active contours, 319–21
Bayesian-based regularizers, 320
clustering-based regularizers, 320
coupling-surfaces regularizers, 321
shape-based regularizers, 320–21
See also Active contours
Twist, 240
clockwise patterns, 241
results, 241
U
Ultrasonic system, HIFU, 369
Univariate statistical analysis, 50, 54
Universal quality index, 100
Unstable plaques, 137–38
V
Ventricular septal defect (VSD), 293
Vessel wall plus plaque thickness, 84–86
change, 85–86
map, 84–85
See also Carotid plaque; Carotid wall
Visual evaluation by experts, 50–51, 56–57
Visualization
3-D ultrasound, 11–13
advanced techniques, 360
with simultaneous rendering of multiple
features, 360
tools, need for improvement, 88
Visual perception evaluation, 100–101
VivaWave microwave ablation system, 406
Volume difference, 175, 233
Volume estimation, LV, 231
Volume of interest (VOI), 192
extraction method, 193, 194
ROIs as combined form, 193
Volume rendering techniques, 12–13, 73
W
Wall motion index (WMI), 262
Wavelet filtering, 38, 39, 46–47
Wavelet method, 308–9
Wavelet shrinkage (WS), 229
Waveltc filter, 46–47
Wide-angled acquisition mode, 273, 274
Wiener filter, 39, 42, 229
Wigner-Ville distribution (WVD), 300, 303
defined, 303
of PCG, 304
Index 431