Artificial Intelligence Techniques in Nuclear Cardiology

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Jul 17, 2012 (4 years and 11 months ago)

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Cardiac Imaging
NUCLEAR CARDIOLOGY
a report by
Gui do Ger mano,PhD
Professor of Medicine,UCLA School of Medicine,and Director,Artificial Intelligence in Medicine Program,
Cedars-Sinai Medical Center
Ar t i f i ci al I nt el l i gence Techni ques i n Nucl ear Cardi ol ogy
B US I NE S S B RI E F I NG:US CA RDI OL OGY 2 0 0 6
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The term ‘artificial intelligence’ (AI) was coined in 1956
by Professor John McCarthy at the Massachussetts
Institute of Technology (MIT).While the ambitious goal
of ‘making computers think and behave like humans’ is
still largely unrealized (to date no computer has ever
passed the Turing test—defined as the capability to engage
undetected in a natural language conversation with a
human party),human behavior has been rather effectively
emulated by computers in game playing,as well as in
select scientific and medical applications.For the purposes
of this article,a software application will be considered to
be ‘intelligent’ if it follows different courses of action
based on the result of evaluation(s),e.g.expressed by
‘if...then’ statements in the code.By this criterion,an
algorithm that filters an image using a filter of predefined
characteristics would not qualify as intelligent,whereas an
algorithm that tailors the amount and type of filtration to
image quality and count statistics would.
A S c hemat i c Vi ew of Nuc l ear Car di ol ogy
I magi ng
Figure 1 presents a schematic view of the steps involved in
the performance,analysis,and interpretation of a nuclear
cardiology study—in this case a gated perfusion single
photon emission computed tomography (SPECT) study.
A standardized acquisition protocol produces a number of
sets of projection images that are then filtered and
reformatted into tomographic images by choosing
appropriate reconstruction limits and by identifying the
location of the left ventricle (LV) long axis in the three-
dimensional (3-D) space.Perfusion quantification requires
sampling of the LV myocardium’s uptake at a number of
locations,comparing it with the expected or ‘normal’
uptake pattern and producing numerical and graphical
information as to the portion of the myocardium that is
hypoperfused.Function quantification requires tracking
(or modeling) the epicardial and endocardial surfaces of
the LV as they move during the cardiac cycle,so as to
obtain dynamic measurements of the LV from which
both global (ejection fraction and diastolic parameters)
and regional (myocardial wall motion and thickening)
parameters of LV function can be derived.Parameters
other than perfusion and function can be similarly
quantified by determining the LV shape,its relative uptake
compared with other organs (lung/heart ratio (LHR)) or
its size under different study conditions (transient
ischemic dilation ratio (TID)).Study assessment is a
complex process that starts with quality control and the
identification of imaging artifacts (often related to photon
attenuation,patient/organ motion and gating errors) and
involves ascribing perfusion and function abnormalities to
specific coronary vascular territories,correcting the
automated software where/if necessary,combining
perfusion and functional information,and interpreting it
in the appropriate clinical context.The final output is a
report that contains information on the individual
patient’s demographics and clinical history,the type of
nuclear procedure performed,the quantitative,semi-
quantitative and visual data that resulted from it,and an
answer to the clinical question that caused the patient to
be referred,with a prognostic statement included (if
possible and appropriate).
It is apparent that each of the blocks and sub-blocks in
Figure 1 accomplishes tasks that require some degree of
‘thinking’,whether it is distinguishing the LV from other
body structures,deciding whether a certain feature or
behavior is normal or abnormal,or making sure that the
reported data is internally consistent.The two main classes
of AI techniques that have traditionally been used to help
scientists automate many of those tasks are now discussed.
Exper t S ys t ems and Neur al Net wor ks
An expert system (more appropriately referred to as a
rule-based system) is a software application that uses rules
to store knowledge,and ‘rule chaining’ to apply that
knowledge to solving problems.A typical example is the
fact that most adult human hearts comprise an
ellipsoidally shaped LV with a cavity volume of
20–500ml,even in severely pathological cases.Software
that aims to identify the LV can therefore safely discard
candidate structures that are too small,too large,or
inappropriately shaped.
1
A rule is generally expressed by
‘if...then’ clauses—if a specific condition is verified (or not
Guido Germano,PhD,is the
Director of the Artificial Intelligence
in Medicine (AIM) Program at
Cedars-Sinai Medical Center,and a
Professor of Medicine at the
University of California in Los
Angeles (UCLA) School of Medicine.
From 1992 to 2001,he also held
the positions of Adjunct Professor,
Associate Professor,and Assistant
Professor of Radiological Sciences at
UCLA.As a past Associate Editor of
the Journal of Nuclear Medicine
(JNM),Dr Germano serves on the
editorial boards of the JNM,Journal
of Nuclear Cardiology (JNC) and
several international journals,
including the International Journal
of Cardiac Imaging.He also serves
on the Board of Directors of the
Cardiovascular Council of the
Society of Nuclear Medicine (SNM),
is a founding member of the
Nuclear Cardiology Foundation of
the American Society of Nuclear
Cardiology (ASNC),and is a Fellow
of the American College of
Cardiology (ACC),as well as the
co-chair of ACC’s longest
continuously held intramural course
for nuclear cardiology practitioners.
He is currently a reviewer for 11
journals in the fields of cardiology,
nuclear medicine,and imaging,he
has published over 200 original
papers,books and book chapters,
and has given invited lectures in
four continents.Dr Germano earned
a BS in electrical engineering from
the University of Naples,Italy,in
1984,an MBA from the Italian
government in 1985,and in 1985
was awarded a Fulbright fellowship
for post-graduate study at UCLA,
where he received a MS in 1987
and a PhD in 1991,both in
biomedical bhysics.
1.Germano G,Kavanagh P B,Su H T et al.,“Automatic reorientation of three-dimensional,transaxial myocardial perfusion
SPECT images” [see comments],J.Nucl.Med.(1995);36:pp.1,107–1,114.
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Cardiac Imaging
NUCLEAR CARDIOLOGY
verified),then a specific action will be taken.Key to
building an effective expert system is using an appropriate
representation to describe the task to be accomplished—
in other words,if the rules are correctly chosen and
combined,the expert system is usually able to perform its
job well.The main advantage of the expert system
approach is that if a scientist or clinician fully understands
the problem at hand,and is capable of distilling his/her
reasoning in solving it into a coherent set of rules,then
the expert system can apply those rules in a consistently
faster and more reproducible manner.The main
disadvantage is that the problem must be simple enough
to be defined by a reasonably small number of rules.The
more rules that are introduced,the higher the likelihood
that conflicts may result—it is of course possible to assign
a priority to each rule,but as their numbers grow,the
sheer number of interactive combinations may make the
problem intractable,even when the entire decisional
structure is visualized using a ‘decision tree’.
A neural network is a ‘black box’ approach that aims to
replicate the human performance of a task by analyzing a
large number (‘training set’) of examples of how an
expert practically accomplishes that task.In engineering
terms,enough input-output sample pairs are provided to
permit the derivation of the ‘transfer function’ of the
black box,so that the right output can be generated upon
presentation of a new input.An example of this is software
that automatically determines thresholds for semi-
quantitative LV segmental perfusion or function scoring
by maximizing its own agreement (kappa statistics) with
expert scores in a sample patient population,which
subsequently applies those thresholds to new patients.
2
The main advantage of neural networks is that,
conceptually,they can be used to solve complex
problems that are either not completely understood or
not suitable for being described by a clear set of rules.
The main disadvantage is that the approach ‘I cannot
quite explain how I do it,but watch me do it and you’ll
figure it out’ is intrinsically limited.The expert trainer
may not show the trainee a wide enough sample of cases,
or may provide wrong interpretations.(Admittedly,this
limitation is not unique to neural networks,as the wrong
rules could be chosen in the expert system approach;
however,at least the rules would be clearly spelled out,
and could be more readily identified and corrected.)
More importantly,computational speeds limit neural
networks to a small number of electronic nodes grouped
in few layers,and their design is still widely regarded as a
form of art.Replicating the complexity of the human
brain using a neural network is impossible with current
technology,because the human brain consists of more
than 1,011 neurons,each of them with 105 connections,
and its super-massive parallelization would not be
achievable by any existing computer.
Case-based reasoning programs can be considered to be
a sub-category of neural networks,because they also
base their decisions on a sample set of inputs and
corresponding outputs.However,no attempt to identify
the transfer function of the decisional process is made,
and the output corresponding to the sample input that
most closely resembles the one at hand is chosen.It is
obvious that the number and variety of input/output
sets from which to choose is extremely critical to the
success of this approach.
AI i n Nuc l ear Car di ol ogy—
A Br i ef Revi ew
The great majority of AI techniques employed in
nuclear cardiology are of the expert system type and,as
Figure 1 suggests,can be applied to solving a wide
variety of problems.
Figure 1:Block Diagram Summarizing the
Acquisition/Processing/Analysis/Reporting Chain
Connected with the Performance of a Nuclear
Cardiology SPECT Study
• Sampling
• Normal limits
• Polar maps
Perfusion
• Volumes
• Ejection fraction
• Motion/thickening
Function
• TLD
• LHR
• LV shape
• Fusion
Other
• Assessment
• Report generation
• Filtering
• Reconstruction
• Reorientation
Acquisition
Processing
Analysis and
Quantification
Interpretation
and Reporting
2.Germano G,Erel J,Lewin H,Kavanagh P B,Berman D S,“Automatic quantitation of regional myocardial wall motion and
thickening from gated technetium-99m sestamibi myocardial perfusion single-photon emission computed tomography”,J.Am.Coll.
Cardiol.(1997);30:pp.1,360–1,367.
TLDthermoluminescent dosimeter.
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Immediately following cardiac SPECT acquisition,
various quality control tasks can be accomplished entirely
through rule-based software.For example,patient and/or
heart motion has been detected by algorithms that track
the LV center of mass or its epicardial boundary in the
projection datasets,much like a human operator could by
visual review of the rotating projection images.
3
Gating
errors and arrhythmia-induced anomalies have also been
identified from automated analysis of the relative
projection image counts and count patterns in the
various intervals of a gated SPECT study.
4
Both the reconstruction/reorientation process and the
quantification performed on the resulting tomographic
images are based on isolating or ‘segmenting’ the heart
from neighboring structures.The simplest form of image
segmentation is thresholding,i.e.setting to zero all the
pixels below a certain fraction of the image’s maximal
pixel count so as to reduce or eliminate extra-cardiac
activity.Since the heart is not often the ‘hottest’ structure
in the image,rule-based approaches to segmentation have
been devised using adaptive thresholding or knowledge of
the expected location,size,and shape of the heart.For
example,heuristic criteria could require that the LV be in
the upper right area of a projection image,have size
within the physiological range,or present a doughnut-like
shape with a reasonable aspect ratio in a short axis
image.
1,5
In gated studies,isolation of the LV cavity or
myocardium can also be effected by identifying and
clustering pixels whose count value changes the most
during the cardiac cycle,based on the assumption that
count variations are a consequence of motion of activity-
containing structures.Figure 2 shows an example of
segmentation of the LV within a projection image.
Expert systems based on edge detection or pattern
recognition algorithms are commonly employed to
precisely outline the boundaries of the LV cavity or the
myocardium.Edge detection may involve the Gaussian
fitting of count profiles across the myocardium,
2,5
moment analysis,
6
gradient analysis of the count
distribution in the myocardium
7
or in the LV cavity,
8
and
the partial volume effect,
9
sometimes with the
assumption of fixed myocardial thickness.
10
Rules may
also include the constraint that the LV myocardial mass
should be constant throughout the cardiac cycle,
5
or
strategies to ensure that edge detection succeeds even in
the presence of severe perfusion defects—specifically,
expert systems will often ‘fill’ discontinuities based on
the geometry and smoothness of the immediately
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Cardiac Imaging
NUCLEAR CARDIOLOGY
Figure 2:Automatic Segmentation of the LV and Determination of
Reconstruction Limits
An anterior projection image (A) is masked (B),then iteratively convolved with a 2-D Gaussian function (C).A second convolution
process is followed by local maxima extraction (green dots in D).A ring encompassing the ‘best’ star in C is sought in D.This ring
bounds the LV myocardium (red patch in E),and reconstruction limits straddle it (yellow dashed lines in E).Reproduced with
permission from Germano et al.,“Operator-less processing of myocardial perfusion SPECT studies”,J.Nucl.Med.(1995);36(11):
pp.2,127–2,132.
Figure 3:A Sample Dialogue Box Generated by Cedars-Sinai’s Automatic Report
Generator Application in the Presence of Conflicting Inputs
All errors must be corrected and warnings acknowledged,prior to finalizing and saving a report.
3.Matsumoto N,Berman D S,Kavanagh P B et al.,“Quantitative assessment of motion artifacts and validation of a new motion-
correction program for myocardial perfusion SPECT”,J.Nucl.Med.(2001);42:pp.687–694.
4.Nichols K,Dorbala S,DePuey E G,Yao S S,Sharma A,Rozanski A,“Influence of arrhythmias on gated SPECT myocardial
perfusion and function quantification”,J.Nucl.Med.(1999);40:pp.924–934.
5.Germano G,Kiat H,Kavanagh P B et al.“Automatic quantification of ejection fraction from gated myocardial perfusion
SPECT”,J.Nucl.Med.(1995);36:pp.2,138–2,147.
6.Goris M L,Thompson C,Malone L J,Franken P R,“Modelling the integration of myocardial regional perfusion and function”,
Nucl.Med.Commun.(1994);15:pp.9–20.
7.Faber T L,Stokely E M,Peshock R M,Corbett J R,“A model-based four-dimensional left ventricular surface detector”,IEEE
Trans.Med.Imaging (1991);10:pp.321–329.
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adjacent areas with normal (or less abnormal) perfusion.
5
The partial volume effect is particularly helpful in the
assessment of systolic myocardial thickening from gated
SPECT imaging,since it can be assumed that
myocardial ‘brightness’ (in the image),and actual
myocardial thickness are linearly proportional,and that
the apparent myocardial ‘brightening’ between diastole
and systole is a good proxy for myocardial thickening.
Recently proposed rule-based algorithms in the area of
myocardial perfusion SPECT are capable of
‘intelligently’ combining quantitative information
derived from prone and supine studies,
11
or registering
image volumes so that a common set of contours can be
applied to rest and stress (or to serial stress) studies.
12
The automatic interpretation of nuclear cardiology
studies is a complex and difficult task,and it is perhaps
for this reason that a variety of expert systems,neural
networks,and case-based reasoning approaches have
been attempted in this area.
13
The problem most
frequently studied has been the identification of the
presence of perfusion defects and the location from
perfusion SPECT images,or rather from a parametric
representation of those images (polar maps,Fourier
decomposition,or segmental uptake),which makes the
input data more computationally manageable,
particularly for neural network purposes.
14
An
interesting expert system approach has been described,
using 253 heuristic (‘if...then’) rules created by experts
in order to best match SPECT perfusion assessment to
angiographic data—of note,some of the rules contain
information on patient body size and probable
attenuation artifacts,suggesting that patient history and
other (non-perfusion) nuclear information may be
incorporated into the diagnostic software in the future.
15
Currently,it is fair to say that AI techniques should only
be regarded as a ‘second expert’,and can provide
computer-assisted,rather than completely automatic,
interpretation of nuclear cardiology studies.The key
function of a report generator is that of:
• accepting input data such as patient demographics,
medical history,qualitative and quantitative results
of the nuclear and possibly stress echocardiographic
(ECG) study;
• appropriately combining that input data;and
• generating as output diagnostic and prognostic
information,expressed in a format and a language
that can be readily understood by the referring
physician.
In addition to providing the input-output transfer
function,an ‘intelligent’ function of an expert system
for report generation is ensuring the consistency of
the input data.When a reviewing clinician modifies
the results of the quantitative algorithm (for example,
segmental perfusion scores could be modified if
suspected to be secondary to attenuation artifacts) or
contributes his/her own qualitative assessments,it is
the report generator’s task to determine if human
intervention has rendered the data internally
inconsistent.If that is the case,conflicting items of
information can be flagged,appropriate errors and
warning messages generated,and suggestions as to
how to reconcile the data presented,ultimately
improving report accuracy and turnaround time.
While it is desirable that an expert system or neural
network should be able to operate in a totally
automated fashion,some may require a minor level of
operator interaction to complete their task.The
higher the degree of interaction required,the lower
the reproducibility of the final results.The continuing
research in the field of AI,coupled with the increase
in computer processing speed,makes it likely that
new and improved software applications will be
developed in the future—this is expected to further
enhance the accuracy,reproducibility,and overall
efficiency of nuclear cardiology,a modality that
already has seen remarkable growth over the past
three decades.■
Ar t i f i ci al I nt el l i gence Techni ques i n Nucl ear Cardi ol ogy
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8.Faber T L,Stokely E M,Templeton G H,Akers M S,Parkey R W,Corbett J R.“Quantification of three-dimensional left ventricular
segmental wall motion and volumes from gated tomographic radionuclide ventriculograms”,J.Nucl.Med.(1989);30:pp.638–649.
9.Liu Y,Sinusas A,Khaimov D,Gebuza B,Wackers F,“New hybrid count- and geometry-based method for quantification of left
ventricular volumes and ejection fraction from ECG-gated SPECT:methodology and validation”,J.Nucl.Cardiol.(2005);12:
pp.55–65.
10.Faber T L,Cooke C D,Folks R D et al.“Left ventricular function and perfusion from gated SPECT perfusion images:an
integrated method”,J.Nucl.Med.(1999);40:pp.650–659.
11.Nishina H,Slomka P,Abidov A et al.,“Combined supine and prone quantitative myocardial perfusion SPECT:method
development and clinical validation in patients with no known coronary artery disease”,J.Nucl.Med.(2006) in press.
12.Slomka P J,Nishina H,Berman D S et al.,“Automatic quantification of myocardial perfusion stress-rest change:A new measure
of ischemia”,J.Nucl.Med.(2004);45:pp.183–191.
13.Wallis J W,“Use of artificial intelligence in cardiac imaging – Invited commentary”,J.Nucl.Med.(2001);42:pp.1,192–1,194.
14.Lindahl D,Palmer J,Ohlsson M,Peterson C,Lundin A,Edenbrandt L,“Automated interpretation of myocardial SPECT
perfusion images using artificial neural networks”,J.Nucl.Med.(1997);38:pp.1,870–1,875.
15.Garcia E V,Cooke C D,Folks R D et al.,“Diagnostic performance of an expert system for the interpretation of myocardial
perfusion SPECT studies”,J.Nucl.Med.(2001);42:pp.1,185–1,191.
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