Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_3592_Библиотеки_им_академика_М_И_Перельмана
.pdf
Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
https://t.me/medicina_free
the boundaries from the two techniques, the bias was further reduced to 0.074 ±
0.068 mm, which demonstrated that the two techniques could be considered as
complementary.
The fourth technique, i.e. CAUDLES-EF [64], can extract the far double line
(i.e.LI and MA) in the carotid artery using an edge flow technique based on
directional probability maps using the attributes of intensity and texture.
CAUDLES-EF was validated against manual tracings on a large dataset of 300
longitudinal B-mode carotid images with normal and pathologic arteries. In
comparison to previous techniques such as CULEXsa and CALEXia,
CAUDLES-EF performed the best with a high figure-of-merit (FoM) of 94.8%.
The fifth technique, i.e. CAILRS [38], can automatically segment the intima layer
of the far wall based on mean shift classification. CAILRS was validated against
manual tracings on a large dataset of 300 longitudinal B-mode carotid images.
CAILRS was benchmarked against a semi-automatic technique based on an FOAM
edge operator. The proposed technique showed an IMT bias of −0.035 ± 0.186 mm.
In comparison to all the previous techniques, such as CULEXsa, CALEXia,
CALSFOAM and CAUDLES-EF, CAILRS showed the highest FoM of 95.6%
compared to the other proposed techniques.
In 2012, Molinari et al proposed two more automated IMT measurement
techniques: Completely Automated Multi-resolution Edge Snapper (CAMES) [53]
and Carotid Measurement Using Dual Snakes (CMUDS) [65]. The first technique,
CAMES [53], consists of two stages. Stage one consists of automated carotid artery
recognition based on a combination of scale-space and statistical classification in the
multi-resolution framework, and stage two targets IMT measurement by performing
an automated segmentation of LI and MA interfaces for the far (distal) wall. By
carrying out a study on 365 B-mode longitudinal carotid images, the study found an
8.4% increase in the FoM compared to their previous technique CALEXia. The
study concluded that CAMES was a suitable and a validated clinical tool for
automating and improving cIMT measurement in multicenter large clinical trials.
The second technique, CMUDS [65], used a snake-based approach for estimating
the LI and MA borders. The technique used a novel first-order absolute momentbased external energy, which provides stable deformation. The dual snakes evolve
simultaneously and are forced to maintain a regularized distance to prevent
collapsing or bleeding. By carrying out a study on 665 B-mode longitudinal carotid
images, the study produced a very high FoM of 98.4%. Wilcoxon and Fisher’s test
further proved its accuracy, making this system adaptable for large multi-center
studies.
In 2013, Saba et al proposed the automated IMT measurement technique
CARES 3.0 [66]. CARES 3.0 is an upgrade of CARES, proposed previously by
Molinari et al [63] in 2011. After automated localization of the carotid artery, the
LI/MA segmentation followed four stages: stage 1 consisted of the creation of
the guided zone (i.e. ROI); in stage 2, edge enhancement was performed using the
FOAM operator; stage 3 performed a heuristic search for locating the LI/MA peaks;
finally, in stage 4, LI/MA regularization was performed. In a study on 250 patients,
the results showed 80% of the images as having an IMT measurement bias ranging
12-28
Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
https://t.me/medicina_free
between −50% and +50%. The results outperformed previous CARES releases and
showed high accuracy and reproducibility for IMT measurement.
A few months later, the same group [43] performed intra- and inter-observer
variability analysis and computed the measurement error of their recently proposed
completely automated IMT measurement software AtheroEdge
TM
. The study was
carried out on 200 carotid ultrasound images acquired from 50 asymptotic women
patients. The intra- and inter-observer variability was tested using three readings.
The measurement errors of AtheroEdge™ for the automated and semi-automated
methods were −0.0004 ± 0.158 mm and 0.008 ± 0.157 mm, respectively, compared
to the mean value of three expert readers. The FoMs were 99.9% and 98.9%
compared to the mean value of the three expert readers and 99.8% and 99.9%
compared to a commercial ultrasound scanner (using the automated and semiautomated method, respectively). The intra-class CC of the three independent users
was 0.98. The proposed AtheroEdge™ software showed a diagnostic accuracy of
90%, proving its application in processing large datasets for CCA and avoiding
subjectivity in cIMT measurements.
These systems were mainly research-based and did not allow for cross-institutional clinical trials [38, 43, 48, 53, 58, 59, 62–65]. To allow for the use of cIMT
measurements to occur across institutions in a reproducible fashion, improving the
uniformity and reliability of cIMT is critical. Therefore, we propose the use of cloudbased technology in order to allow for cross-institutional participation. The use of
information technology to support clinical services is a major driver for the push to
include cloud-based solutions in healthcare. Mobile or hand-held machines, such as
the iPhone and iPad, have been rapidly accepted in many countries as point-of-care
solutions, providing access to healthcare data and statistics in ways that were not
previously conceivable [60]. Smartphones with faster processors, improved memory
and smaller batteries, in concert with highly efficient operating systems, are affecting
our personal and work environments [67]. The use of these pervasive technologies in
healthcare provides an anytime–anywhere solution so patients and healthcare
providers can both benefit from better atherosclerosis disease management.
12.6.4 A note on PoM, cross-correlation and ROC analysis
In this study, we computed the PoM for AtheroCloud™ and sonographer cIMT
readings. The PoM computes the closeness of AtheroCloud™ and sonographer
cIMT readings against the manual cIMT. Tables B3 and B4 clearly revealed the
percentage improvement of the PoM and CC of AtheroCloud™ over sonographer
readings, as shown in the appendix B. This proved our hypothesis that the error
between the automated AtheroCloud™ software system and manual tracings (gold
standard) was lower compared to the error between the sonographer readings and
manual tracings. In this study, we also analyzed the correlation between age and left
and right cIMT readings computed using AtheroCloud™, manually and by a
sonographer. The CC is moderate and mild which showed an increase in the risk of a
CAD with advancement in age. The combined cumulative distribution error curves
of cIMT for (i) AtheroCloud™ versus manual, (ii) sonographer versus manual and
12-29
Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
https://t.me/medicina_free
(iii) AtheroCloud™ versus sonographer are shown in figure 12.11. We observed that
about 23% of the patients’ cIMT was calculated more accurately in AtheroCloud™
compared to sonographer readings. The AUCs for the AtheroCloud™ and
sonographer were 0.99 and 0.81, respectively, which further confirms the accuracy
and reliability of our proposed system. Even though the automated system performs
better compared to the manual readings and sonographer readings, there are subtle
factors which we did not consider while designing this study. These include studying
the effect of lighting conditions during the manual tracings [68], the attention of the
sonographer while taking his/her readings, such as his/her mood, experience
recording sonographer readings and type of image format (DICOM versus
JPEG), as this can affect performance [69]. This is beyond the scope of this pilot
study. We have, however, adapted a standardized software (ImgTracer™,
AtheroPoint, Roseville, CA, USA), which has been applied to several studies in
medical imaging [70–75].
12.6.5 Risk stratification
In the current analysis, we showed the risk stratification using the FRS. A risk score
was derived for each patient using the gender-specific prediction formulae proposed
by Wilson [57] based on the following standard conventional cardiovascular risk
factors. Using the above technique, we stratified the population in low-, mediumand high-risk bins.
Table B5 clearly shows the percentage improvement of cIMT for left and right
cIMTs for the cloud-based (AtheroCloud™) over the desktop-based (AtheroEdge™)
system, as shown in appendix B. The percentage differences of AtheroCloud™ to
AtheroEdge™ were 2.27% and 3.75% for left and right cIMTs, respectively.
12.6.6 Strengths, weaknesses and extensions
The proposed automated cloud-based system can be a very valuable tool for multicenter clinical trials and useful in the Routine mode to interface with OEM machines
for cIMT computation. The proposed system is reliable, accurate, reproducible,
cost-effective and fast (less than five seconds per image). The system is fully
automated and can fall back on a user-interactive solution. Our pilot design has a
multi-tenancy paradigm; hence multiple centers can use it at the same time. Further,
the cloud servers can be changed to elastic, and therefore it is scalable depending
upon usage. It can not only accept images from various ultrasound scanners, but
also in various image formats. The system automatically computes the carotid SSI
and the design has an inbuilt database of patient record systems that can be shared
electronically or printed. This pilot study was performed in a private cloud, but can
run in available public clouds such as IBM, Amazon or Hewlett Packard (HP).
Batch-processing further improves the processing speed and provides a reproducible
feature needed for pharmaceutical trials and can be used as a teaching tool. Thus,
the proposed system is a fully automated 24/7 system (anytime–anywhere solution)
with multiple features that bring more user-friendliness compared to a sonographer’s
12-30
Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
https://t.me/medicina_free
manual methods. Even though the proposed system is fully automated and very fast,
it offers certain challenges and limitations:
(a) The processing speed of AtheroCloud™ depends on the speed of the WIFI
or land-line connection. If the internet speed is slow, it can be challenging
for the physician to load imaging and process images.
(b) The cloud’s cost can also increase over time, but as cloud-based technol-
ogies evolve, we expect the systems to become more economical [76] for
hospital and clinic-based settings.
Finally, since our local server is Windows-based, all the encryption is performed
using Microsoft bit blocker device encryption and these details are outside the scope
of this pilot study. All the transfer and storage is performed by the Microsoft server.
This is a pilot research study, so no cost analysis was attempted. This is being
adopted as a service model and it can be used in a service setting. Current reports are
produced in PDF, Word or Excel-sheet formats. It can be converted to HTML
format (H7 format) for universal usage. Under the information technology framework, more work can be accomplished as part of future research studies, but the
current results are truly encouraging.
12.7 Conclusion
We presented AtheroCloud™—a completely automated, cloud-based, point-of-care
system for ultrasound carotid intima–media thickness measurement. AtheroCloud™
cIMT readings were compared against sonographer readings and showed superior
performance. AtheroCloud™ showed its usability in the Routine and
Pharmaceutical modes when benchmarking against the desktop-based
AtheroEdge™ (AtheroPoint™, Roseville, CA) system, which was previously
benchmarked against a Siemens system. Comprehensive statistical analyses were
performed demonstrating its reliability. Although more tests need to be performed
for this pilot study, current results show that the system can be adopted in the
clinical setting for the clinical Routine mode or multicenter Pharmaceutical Trial
mode.
Acknowledgments
Reprinted from Saba L, Banchhor S K, Suri H S, Londhe N D, Araki T, Ikeda N,
Viskovic K, Shafique S, Laird J R, Gupta A, Nicolaides A and Suri J S 2016
Accurate cloud-based smart IMT measurement, its validation and stroke risk
stratification in carotid ultrasound: a web-based point-of care tool for multicenter
clinical trial Comput. Biol. Med. 75 217–34, with permission from Elsevier.
Funding
This research did not receive any specific grant from funding agencies in the public,
commercial, or not-for-profit sectors.
12-31
Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
https://t.me/medicina_free
Conflicts of interest
The authors declare no conflict of interest.
Appendix A Polyline distance metric and precision-of-merit for
AtheroCloud™ cIMT measurements
This section presents a brief derivation for the computation of the PoM for
AtheroCloud™ cIMT measurements. Section A.1 presents the mathematical deriva-
tion for the polyline distance method (PDM) used for the measurement of
AtheroCloud™ cIMT [39, 40] and section A.2 presents the derivation of the PoM [63].
A.1. Polyline distance metric
The PDM is used to measure the cIMT. It measures the changes of the contours of
the far wall LI interface and MA interface. Let the first contour LI be denoted by C
and a reference point on this contour be (p0,q0). Let the second contour MA be
denoted by C
a line segment s. Next
reference point
Let the distances between the reference point (p
be d
and d2. Let λ be the distance of the reference point
1
The perpendicular distance between the line segment s and the reference point
given by
and the two consecutive points on C2be (p1,q1) and (p2,q2), forming
2
is obtained, which is the distance between the
v
s(, )
pq(, )
on C1and the line segment formed by the two points on C2.
00
, q0) and the two consecutive points
0
towards the line segment s.
⊥
. Using the calculus, one can derive the value of λ and⊥as follows:
1
is
−−+− −
qqqq pppp
()()( )( )
2101 2 10 1
The distance
λ =
−+−
pp qq
()()
21221
−−+−−
qqpp ppqq
()( )( )()
211 0 2 101
⊥
=
d
between the vertex
v
s(, )
−+−
pp qq
()()
21221
2
2
and the line segment s can be
.
(A.1)
(A.2)
mathematically given as
The process to obtain
and is given by
C
1
v
(, )
=
ds
dC C d C( , ) ( , ), (A.4)
⎧
⎨
⎩
v
dd
min{ , } 0, 1
12
⊥
d
is repeated for the rest of the points of the contour
s(, )
=
λλ
<>
⩽⩽
01
v
∑
i12 2
=
iN1
λ
. (A.3)
where n is the number of points in the contour C1, andC2is the segment on contour
C
. Second, the algorithm above is repeated where C2now becomes the reference
2
12-32
Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
https://t.me/medicina_free
contour and C1becomes the segment contourC1. The reverse can be represented by
d(C
). Lastly, combining both d(C1,C2) and d(C2,C1) will yield the equation
2,C1
below which is the PDM:
(: )
DC C
S 12
=
dC C dC C
( vertices vertices )
#+#
+
12 21
CC
ϵϵ
12
.
(A.5)
(, ) (, )
In this study, we have used the term PDM which is a more convenient expression
for equation (A.5). We will use equation (A.5) for computation of AtheroCloud™
cIMT and PoM in section A.2.
A.2 Precision-of-merit for AtheroCloud™ cIMT measurements
Given that LI
AtheroCloud
and MA
AtheroCloud
are the interfaces computed using the
AtheroCloud™ automated method, we compute the AtheroCloud™ cIMT using the
PDM equation (A.5) and it is given as
AtheroCloud AtheroCloud AtheroCloud
=cIMT PDM (LI , MA ). (A.6)
Similarly, using the definition of PDM, we can compute the cIMT measurements
using manual tracings using equation (A.5), given as
=cIMT PDM (LI , MA ). (A.7)
Manual Manual Manual
Let AtheroCloud™ cIMTibe the cIMT value automatically computed by the
proposed system, AtheroCloud™, on the ith image of the database of N patients. If
a database of N images is considered, then the overall mean AtheroCloud™ cIMT
estimate can be defined as
cIMT
AtheroCloud AtheroCloud
1
=
cIMT . (A.8)
N
∑
=
iN1
i
Correspondingly, if manual cIMTiis the cIMT value computed from the
radiologist’s traced manual measurements, the mean manual cIMT for the manual
is given as
cIMT
Manual Manual
1
=
cIMT . (A.9)
N
∑
=
iN1
i
The overall system’s performance can be computed using PoM in percentage as
AtheroCloud
⎡
⎛
=−
⎢
⎜
⎝
⎣
cIMT
−
Manual
cIMT cIMT
AtheroCloud Manual
⎞
⎟
⎠
⎤
⎥
100 . (A.10)
*PoM (%) 100
⎦
12-33
Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
https://t.me/medicina_free
Appendix B Tables
Table B1. PoM for left, right and combined cIMTs.
Parameters Left cIMT Right cIMT Combined cIMT
AtheroCloud™ when compared to sonographer
AtheroCloud™ PoM 89.47% 86.29% 87.94%
Table B2. CC between the three methods for left, right and combined cIMTs.
Parameters Left cIMT Right cIMT Combined cIMT
Method 3: AtheroCloud™ and sonographer
CC 0.70 0.62 0.66
Table B3. Percentage improvement of PoM for left, right and combined cIMTs.
% Improvement of AtheroCloud™ over sonographer
Parameter Left cIMT Right cIMT Combined cIMT
PoM 2.98% 9.93% 6.23%
Table B4. Percentage improvement in CC for left, right and combined cIMTs.
% Improvement of AtheroCloud™ over sonographer
Parameter Left cIMT Right cIMT Combined cIMT
CC 29.73% 49.23% 40.58%
Table B5. Percentage difference between AtheroCloud™ and AtheroEdge™ systems for left cIMT and right
cIMT.
% Difference between AtheroCloud (cloud-based) and AtheroEdge™ (desktop-based)
Parameter Left cIMT Right cIMT
cIMT 2.27% 3.75%
12-34
Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
https://t.me/medicina_free
References
[1] WHO CVD website, http://who.int/mediacentre/factsheets/fs317/en/ (Accessed: 20 Sep 2018)
[2] Leeder S, Raymond H and Greenberg H 2004 A Race Against Time: The Challenge of
Cardiovascular Disease in Developing Countries (New York: Columbia University)
[3] Araki T et al 2014 Link between automated coronary calcium volumes from intravascular
ultrasound to automated carotid IMT from B-mode ultrasound in coronary artery disease
population Int. Angiol. 33 392–403
[4] Ross R 1995 Cell biology of atherosclerosis Annu. Rev. Physiol.
[5] Libby P, Ridker P M and Hansson G K 2011 Progress and challenges in translating the
biology of atherosclerosis Nature
[6] Sobieszczyk P and Beckman J 2006 Carotid artery disease Circulation 114 244–7
[7] Arroyo L H and Lee R T 1999 Mechanisms of plaque rupture: mechanical and biologic
interactions Cardiovasc. Res.
[8] Araki T et al 2015 A comparative approach of four different image registration techniques
for quantitative assessment of coronary artery calcium lesions using intravascular ultrasound
Comput. Methods Programs Biomed.
[9] Suri J S, Kathuria C and Molinari F 2010 Atherosclerosis Disease Management (Berlin:
Springer)
[10] Saba S, Pedro and Suri J S 2012 Advanced Atherosclerosis in Diagnosis and Therapy using
MR, CT, Ultrasound (Berlin: Springer)
[11] Saba L et al 2013 Association of automated carotid IMT measurement and HbA1c in
Japanese patients with coronary artery disease Diabetes Res. Clin. Pract.
[12] Sanches M J, Laine A and Suri J S 2011 Advanced Ultrasound Imaging (Berlin: Springer)
[13] Molinari F, Zeng G and Suri J S 2010 Intima–media thickness: setting a standard for a
completely automated method of ultrasound measurement IEEE Trans. Ultrason.
Ferroelectr. Freq. Control
[14] Molinari F, Zeng G and Suri J S 2010 A state of the art review on intima–media thickness
(IMT) measurement and wall segmentation techniques for carotid ultrasound Comput.
Methods Programs Biomed.
[15] Suri J S and Laxminarayan S 2003 Angiography and Plaque Imaging: Advanced
Segmentation Techniques (Boca Raton, FL: CRC Press)
[16] Amato M et al 2007 Carotid intima–media thickness by B-mode ultrasound as surrogate of
coronary atherosclerosis: correlation with quantitative coronary angiography and coronary
intravascular ultrasound findings Eur. Heart J.
[17] Bots M L et al 2007 Carotid intima–media thickness and coronary atherosclerosis: weak or
strong relations? Eur. Heart J.
[18] Polak J F et al 2010 Associations of carotid artery intima–media thickness (IMT) with risk
factors and prevalent cardiovascular disease comparison of mean common carotid artery
IMT with maximum internal carotid artery IMT J. Ultrasound Med.
[19] Polak J F et al 2011 Associations of edge-detected and manual-traced common carotid
intima–media thickness measurements with Framingham risk factors the multi-ethnic study
of atherosclerosis Stroke
[20] Ikeda N et al 2014 Ankle–brachial index and its link to automated carotid ultrasound
measurement of intima–media thickness variability in 500 Japanese coronary artery disease
patients Curr. Atheroscler. Rep.
42 1912–6
473 317–25
41 369–75
118 158–72
57 1112–24
100 201–21
28 2094–101
28 398–406
16 1–8
57 791–804
100 348–53
29 1759–68
12-35
Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
https://t.me/medicina_free
[21] Liao H, Hong H and Wang H 2014 Relation between carotid stenosis severity, plaque
echogenicity characteristics and IMT assessed by ultrasound in the community population of
Southern China Open Access Library J.
[22] Nezu T et al 2015 Carotid intima–media thickness for atherosclerosis J. Atheroscler. Thromb.
23 18–31
[23] Lacroix P A, Aboyans V, Espaliat E and Cornu E 2003 Carotid intima–media thickness as
predictor of secondary events after coronary angioplasty Int. Angiol. 22 279
[24] Geroulakos G A, O’gorman D J, Kalodiki E, Sheridan D J and Nicolaides A N 1994 The
carotid intima –media thickness as a marker of the presence of severe symptomatic coronary
artery disease Eur. Heart J.
[25] Ikeda N et al 2013 Impact of carotid artery ultrasound and ankle–brachial index on
prediction of severity of SYNTAX score Circulation
[26] Araki T et al 2015 Shape-based approach for coronary calcium lesion volume measurement
on intravascular ultrasound imaging and its association with carotid intima–media thickness
J. Ultrasound Med.
[27] Amato M, Montorsi P, Ravani A, Oldani E, Galli S, Ravagnani P M and Baldassarre D
2007 Carotid intima–media thickness by B-mode ultrasound as surrogate of coronary
atherosclerosis: correlation with quantitative coronary angiography and coronary intra-
vascular ultrasound findings Eur. Heart J.
[28] Elias-Smale S E et al 2012 Carotid intima–media thickness in cardiovascular risk strat-
ification of older people: the Rotterdam Study Eur. J. Prev. Cardiol.
[29] Ikeda N et al 2015 Improve the correlation between the carotid and coronary atherosclerosis
SYNTAX score using automated ultrasound carotid bulb plaque IMT measurement
Ultrasound Med. Biol.
[30] Molinari F et al 2012 Ultrasound IMT measurement on a multi-ethnic and multi-institu-
tional database: our review and experience using four fully automated and one semi-
automated methods Comput. Methods Programs Biomed.
[31] Sharma A M et al 2015 A review on carotid ultrasound atherosclerotic tissue character-
ization and stroke risk stratification in machine learning framework Curr. Atheroscler. Rep.
17 1–13
[32] Saba L et al 2016 Carotid inter‐adventitial diameter is more strongly related to plaque score
than lumen diameter: an automated and first ultrasound study in Japanese diabetic cohort J.
Clin. Ultrasound
[33] Molinari F, Zeng G and Suri J S 2010 Intima–media thickness: setting a standard for
completely automated method for ultrasound IEEE Trans. Ultrason. Ferroelectr. Freq.
Control
[34] McCloskey K et al 2014 Reproducibility of aortic intima–media thickness in infants using
edge-detection software and manual caliper measurements Cardiovasc. Ultrasound
[35] Sicari R et al 2011 The use of pocket-size imaging devices: a position statement of the
European Association of Echocardiography Eur. J. Echocardiogr.
[36] Switzer J A, Levine S R and Hess D C 2009 Telestroke 10 years later—‘telestroke 2.0’
Cerebrovasc. Dis.
[37] Acharya R U et al 2011 Distributed Diagnosis and Home Healthcare (D2H2) (Valencia, CA:
American Scientific)
[38] Meiburger K M et al 2011 Automated carotid artery intima layer regional segmentation
Phys. Med. Biol.
57 1112–24
34 469–82
44 210–20
28 323–30
56 4073–90
15 781–5
41 1247–62
2 e2014
77 712–6
28 2094–101
19 698–705
108 946–60
12 18
12 85–7
12-36
Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
https://t.me/medicina_free
[39] Suri J S, Haralick R M and Sheehan F H 2000 Greedy algorithm for error correction in
automatically produced boundaries from low contrast ventriculograms Pattern Anal. Appl.
39–60
[40] Saba L et al 2012 What is the correct distance measurement metric when measuring carotid
ultrasound intima–media thickness automatically? Int. Angiol. 31 483–9
[41] Molinari F et al 2012 Carotid artery recognition system: a comparison of three automated
paradigms for ultrasound images Med. Phys.
[42] Saba L et al 2012 Comparison between manual and automated analysis for the quantifica-
tion of carotid wall by using sonography. a validation study with CT Eur. J. Radiol.
[43] Saba L et al 2013 Inter-and intra-observer variability analysis of completely automated
cIMT measurement software (AtheroEdge™) and its benchmarking against commercial
ultrasound scanner and expert readers Comput. Biol. Med.
[44] Molinari F et al 2012 Automated carotid IMT measurement and its validation in low
contrast ultrasound database of 885 patient Indian population epidemiological study: results
of AtheroEdge™ software Int. Angiol. 31 42–53
[45] AtheroEdge 510(K) clearance for desktop application:
pdf12/K122022.pdf (Accessed: 20 Sep 2018)
[46] Hertzberg B S et al 2000 Physician training requirements in sonography: how many cases are
needed for competence? Am. J. Roentgenol.
[47] BioMed Central: http://nvijournal.biomedcentral.com/ (Accessed: 1 June 2016)
[48] Molinari F et al 2011 CALSFOAM-completed automated local statistics based first order
absolute moment’ for carotid wall recognition, segmentation and IMT measurement:
validation and benchmarking on a 300 patient database Int. Angiol. 30 227–41
[49] Saba L et al 2014 Multi-Modality Atherosclerosis Imaging and Diagnosis (New York:
Springer)
[50] Mari G et al 2005 Middle cerebral artery peak systolic velocity technique and variability
J. Ultrasound Med.
[51] Molinari F et al 2009 Automated computer-based tracings (ACT) in logitudinal 2-D
ultrasound imaging using different scanners J. Mech. Med. Biol.
[52] Molinari F et al 2012 Hypothesis validation of far-wall brightness in carotid-artery ultra-
sound for feature-based IMT measurement using a combination of level-set segmentation
and registration IEEE Trans. Instrum. Meas.
[53] Molinari F et al 2012 Completely automated multiresolution edge snapper—a new technique
for an accurate carotid ultrasound IMT measurement: clinical validation and benchmarking
on a multi-institutional database IEEE Trans. Image Process.
[54] Chhabra B, Kiran S and Thakur A 2002 Risk stratification in anaesthesia practice Indian J.
Anaesth. 46 347
[55] D’Agostino R B et al 2008 General cardiovascular risk profile for use in primary care the
Framingham Heart Study Circulation
[56] Tzoulaki I, Liberopoulos G and Ioannidis J P 2009 Assessment of claims of improved
prediction beyond the Framingham risk score J. Am. Med. Assoc.
[57] Wilson P W et al 1998 Prediction of coronary heart disease using risk factor categories
Circulation
[58] Saba L et al 2013 Automated analysis of intima–media thickness analysis and performance
of CARES 3.0 J. Ultrasound Med.
97 1837–47
24 425–30
32 1127–35
39 378–91
81 911–8
43 1261–72
http://accessdata.fda.gov/cdrh_docs/
174 1221–7
9 481–505
61 1054–63
21 1211–22
117 743–53
302 2345–52
3
12-37
Соседние файлы в папке Библиотека им академика М.И. Перельмана