Signal processing for magnetic resonance imaging and spectroscopy

645 Pages·2002·8.61 MB·English

by  Hong Yan

Signal Processing for
Magnetic Resonance
Imaging and Spectroscopy
edited by
Hong Yan
University of Sydney
Sydney,Australia
Marcel Dekker, Inc. New York Basel
•
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Copyright ©2002 by Marcel Dekker,Inc. All Rights Reserved.
Copyright 2002 by Marcel Dekker, Inc. All Rights Reserved.
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Copyright 2002 by Marcel Dekker, Inc. All Rights Reserved.
Series Introduction
Over the past 50 years, digital signal processing has evolved as a
major engineering discipline. The fields of signal processing have
grown from the origin of fast Fourier transform and digital filter
design to statistical spectral analysis and array processing, image,
audio, and multimedia processing, and shaped developments in high-
performance VLSI signal processor design. Indeed, there are few
fields that enjoy so many applications—signal processing is
everywhereinourlives.
When one uses a cellular phone, the voice is compressed, coded,
andmodulatedusingsignalprocessingtechniques.Asacruisemissile
winds along hillsides searching for the target, the signal processor is
busy processing the images taken along the way. When we are
watching a movie in HDTV, millions of audio and video data are
being sent to our homes and received with unbelievable fidelity.
When scientists compare DNA samples, fast pattern recognition
techniques are being used. On and on, one can see the impact of
signal processing in almost every engineering and scientific
discipline.
Because of the immense importance of signal processing and the
fast-growing demands of business and industry, this series on signal
processing serves to report up-to-date developments and advances in
the field. The topics of interest include but are not limited to the
following:
·
Signaltheoryandanalysis
·
Statisticalsignalprocessing
·
Speechandaudioprocessing
·
Imageandvideoprocessing
·
Multimediasignalprocessingandtechnology
·
Signalprocessingforcommunications
·
SignalprocessingarchitecturesandVLSIdesign
v
vi SERIESINTRODUCTION
We hope this series will provide the interested audience with
high-quality, state-of-the-art signal processing literature through
research monographs, edited books, and rigorously written textbooks
byexpertsintheirfields.
Signal Processing and Communications
EditorialBoard
MauriceG.Ballanger,ConservatoireNational
desArtsetMétiers(CNAM),Paris
EzioBiglieri,PolitecnicodiTorino,Italy
SadaokiFurui,TokyoInstituteofTechnology
Yih-FangHuang,UniversityofNotreDame
NikhilJayant,GeorgiaTechUniversity
AggelosK.Katsaggelos,NorthwesternUniversity
MosKaveh,UniversityofMinnesota
P.K.RajaRajasekaran,TexasInstruments
JohnAastedSorenson,ITUniversityofCopenhagen
1. Digital Signal Processing for Multimedia Systems, edited by Keshab
K.ParhiandTakaoNishitani
2. Multimedia Systems, Standards, and Networks, edited by Atul Puri
andTsuhanChen
3. Embedded Multiprocessors: Scheduling and Synchronization, Sun-
dararajanSriramandShuvraS.Bhattacharyya
4. SignalProcessingforIntelligentSensorSystems,DavidC.Swanson
5. CompressedVideooverNetworks,editedbyMing-TingSunandAmy
R.Reibman
6. ModulatedCodingforIntersymbolInterferenceChannels,Xiang-Gen
Xia
7. Digital Speech Processing, Synthesis, and Recognition: Second Edi-
tion,RevisedandExpanded,SadaokiFurui
8. ModernDigitalHalftoning,DanielL.LauandGonzaloR.Arce
9. BlindEqualizationandIdentification,ZhiDingandYe(Geoffrey)Li
10. Video Coding for Wireless Communication Systems, King N. Ngan,
ChiW.Yap,andKengT.Tan
11. Adaptive Digital Filters: Second Edition, Revised and Expanded,
MauriceG.Bellanger
12. Design of Digital Video Coding Systems, Jie Chen, Ut-Va Koc, and
K.J.RayLiu
13. Programmable Digital Signal Processors: Architecture, Program-
ming,andApplications,editedbyYuHenHu
14. Pattern Recognition and Image Preprocessing: Second Edition, Re-
visedandExpanded,Sing-TzeBow
15. Signal Processing for Magnetic Resonance Imaging and Spectros-
copy,editedbyHongYan
16. SatelliteCommunicationEngineering,MichaelO.Kolawole
AdditionalVolumesinPreparation
Preface
Magnetic resonance imaging (MRI) and magnetic resonance spectroscopy
(MRS) have gained widespread use for medical diagnosis in recent years.
The potential also exists for magnetic resonance to be useful in building
quantum computers, which may have the capacity of being billions oftimes
faster than classical computers. An important step in the use of MRI and
MRS is the processing of detected signals. Typical tasks include image re-
construction from complete orincompletek-spacedata,removalofartifacts,
segmentation of an image into homogeneous regions, analysis of the shape
and motion of the heart, processing and visualization of functional MR im-
ages, characterization of brain tissues, and estimation of the parameters of
a spectroscopic signal. This book provides a review of prevalent signal pro-
cessing algorithms for solving these problems and reports the latest devel-
opments in the field.
The book is divided into three parts. Part I discusses algorithms for
image reconstruction from MR signals, removal of image distortions and
artifacts, and image visualization. Chapter 1 reviews data sampling require-
mentsinthek-spaceandprovidesbasicmathematicalformulationsofimage
reconstruction problems. Chapter 2 presents algorithms for the reconstruc-
tion of wavelet transform coefficients directly from the Randon transform
domain. The algorithms can be useful for image enhancement, feature ex-
traction, and the study of local tomography. Chapter 3 describes the con-
volution regridding method for image reconstruction from data obtained on
v
Copyright 2002 by Marcel Dekker, Inc. All Rights Reserved.
vi Preface
a nonuniform grid in the k-space. Chapter 4 develops a technique based on
a dynamic finite element mesh (DMESH) model to analyze object motion
anddistortionsinMRI.Chapter5presentsamethodbasedontheprojection
onto convex sets (POCS) algorithm to reduce the artifacts caused by un-
wanted motion of an object during data acquisition. Chapter 6 provides an
overview of several tagged MRI methods for fast imaging of the heart mo-
tion. Chapter 7 presents a cortical flattening method for the visualization of
functional MRI (fMRI) data and a k-space correlation method for the anal-
ysis of the data based on both magnitude and phase information.
Part II covers techniques for the extraction of meaningful regions in
MR images, as well as the analysis of shapes, geometric features, and dy-
namic information. Chapter 8 presents an image segmentation algorithm
based on a multiscale linking model, which can provide the gross shape
informationatahigherscaleandsubvoxelaccuracyatalowerscale.Chapter
9 proposes a method for cortical surface segmentation by deforming a cel-
lular object initialized on the volume of the brain toward the interior of the
deep corticalconvolutions. Chapter10reviewsfeature-basedimageanalysis
methods including registration, segmentation, and supervised and unsuper-
vised classification. Chapter 11 presents an fMRI segmentation method that
takes voxel connectivity information into account and a method for activity
detection based on anisotropic diffusion. Chapter 12 describes image seg-
mentation techniques based on neural network models including learning
vector quantization (LVQ), the multilayer perceptron, and self-organizing
feature maps (SOFM). Chapter 13 introduces an approach to image seg-
mentationbasedonstochasticpixelandcontextmodels.Chapter14presents
a frequency analysis-basedmethodandalocalprincipalcomponentanalysis
(PCA) based method for brain activation signal detection in fMRI. Chapter
15 provides an overview of 2-D and 3-D image analysis methods for mea-
suring the deformation of the heart in tagged images.
PartIIIdiscusses methodsforspectralestimationandanalysisofMRS
signals and their applications. Chapters 16 and 17 provide an overview of
one-dimensional and multidimensional spectroscopic signal processing
methods, respectively. The linear prediction, the state-space model, and the
maximum entropy based methods described in these chapters can overcome
several disadvantages of the conventional Fourier transform-based method
and provide improved spectral quantification. Chapter 18 reviews several
spectroscopic imaging methods that can provide fast data acquisition and
improve image quality compared to the traditional Fourier transform-based
chemical shift imaging method. Chapter 19 reviews techniques for brain
tissuecharacterizationandtumoridentificationbyanalysisoftheMRspectra
based on statistical pattern recognition algorithms. Chapter 20 describes a
wavelet-pocket based algorithm for analysis of metabolic peak parameters
Copyright 2002 by Marcel Dekker, Inc. All Rights Reserved.
Preface vii
in MR spectra. Chapter 21 presents the Crame´r–Rao theory for analyzing
thelowerboundsofstatisticalerrorsinparameterestimationinspectroscopic
signal processing. Error bounds can be used to determine the quality of an
input signal or the performance of a parameter estimation procedure.
Sophisticated signal processing methods can serve as useful tools for
a cost-effective and timely solution to real-world problems. This book pre-
sents a number of ready-to-use algorithms, as well as new research results
and directions in MR signal processing. Its intended audience includes re-
searchers, graduate students, and senior undergraduate students working on
MRI- and MRS in radiology, physics, chemistry, electrical engineering, and
computer science, and hardware and software engineers working on MRI-
and MRS-related products and services. Readers of this book will be able
to utilize the techniques presented to solve the problems at hand and find
inspiration and encouragement to develop new or improved methods for
MRI and MRS.
I would like to thank the authors for their contributions to this book.
I am grateful to Mrs. Inge Rogers for her clerical work and proofreading
and to several referees for providing technical comments on the chapters.
Most of the editing was done when I was on leave in Hong Kong from the
University of Sydney and support from a research grant from theFacultyof
Science and Engineering of the City University of Hong Kong was greatly
appreciated.Finally,IwouldliketothanktheeditorsatMarcelDekker,Inc.,
for their help during the writing of this book.
Hong Yan
Copyright 2002 by Marcel Dekker, Inc. All Rights Reserved.
Contents
Series Introduction K. J. Ray Liu iii
Preface v
Contributors xiii
PART I. IMAGE RECONSTRUCTIONAND
RESTORATION
1. Introduction to Image Reconstruction 1
Zhi-Pei Liang, Jim Ji, and E. Mark Haacke
2. Wavelet-Based Multiresolution Local Tomography 25
F. Rashid-Farrokhi and K. J. R. Liu
3. The Point Spread Function of Convolution Regridding
Reconstruction 59
Gordon E. Sarty
4. Mapping Motion and Strain with MRI 91
Yudong Zhu
ix
Copyright 2002 by Marcel Dekker, Inc. All Rights Reserved.