Guest Editorial: Advanced Signal Processing in MRI. Part I

Image processing in the purest sense deals with images after they arecreated, to produce specific enhancements in the information contentwith respect to certain desired parameters. However, in magneticresonance imaging (MRI), there is a particularly tight interplaybetween the images which are to be created and manipulated and theinfinite number of possibilities for generating the image data. Thus,the acquisition itself is driven by a wide variety of signal-processingconsiderations. In a related fashion, there are a number of quantitieswhich may be derived from image data and which can accordinglyform the basis for optimal acquisition strategies. These image char-acteristics include, among others, flow, nuclear spin relaxationtimes, diffusion, and spectral data which may be associated witheach pixel. These considerations are in addition to the tradeoffbetween speed of acquisition, signal to noise ratio, and resolutionwhich are found in virtually all imaging modalities.

For this Special Issue on Advanced Signal Processing in MRI,contributors were given the option of presenting their new results,suitable for publication by the usual criteria of peer review, in abroader context. Thus, this collection of articles should serve asuseful background for those members of theIJIST readership whoare proficient in image analysis but have somewhat less backgroundin the specifics of MRI.

Response to the solicitation of manuscripts exceeded our highestexpectations. Accordingly, the Special Issue is being published intwo parts, with Part 1 described below.

A great deal of effort in MRI has gone into the estimation of thetissue parameters known as spin–lattice and spin–spin relaxationtimes, and conventionally designated byT1 and T2, respectively.These two parameters contain important information about the mo-lecular motions of the sample. Of necessity, a proper analysis ofparameter estimation must take into account the characteristics ofthe noise in the system. One feature of MRI is that the conventionalimage display mode, that of signal absolute value or magnitude, isformed from two orthogonal detector channels. While the noise oneach channel may generally be assumed to be Gaussian, the noisedistribution of the magnitude image is Rician. Accordingly, in thefirst article in this volume, Sijbers, den Dekker, Raman, and VanDyck reexamine the problem of parameter estimation accountingexplicitly for the true noise distribution to minimize bias in theestimates. This is of particular importance in emerging areas of MRIin which signal to noise is limited, since it is that regime in whichthe Gaussian and the Rician distributions differ significantly.

Simulation of MRI experiments is of great importance, given theinfinite number of pulse sequences available and still being devel-oped. Here, Petersson and Christoffersson present recent work ontheir general approach to the problem of simulating the results of anarbitrary MRI pulse sequence. They describe an addition to thek-space concept, which was introduced by Tweig in 1983 and whichformed a major conceptual advance for setting the framework forMRI data acquisition. Thek-space concept essentially formalized

the link between data acquisition and characteristics of the resultingMRI image. The result of the present article is a versatile, general,and flexible procedure for analysis of bulk motion in MRI.

Kerwin and Prince build on the current activity in analyzingmotion in another way—that is, by application of radiofrequencytagging and tracking of the tag motion. It was recognized early onthat automated tagging tracking was necessary for the technique tohave any real utility, and accordingly, a great deal of work has goneinto just this task. Kerwin and Prince push this to a new level ofaccuracy and sophistication in their article, which shows how toovercome existing limitations in locating and tracing tags in severalimportant ways.

While the original MRI techniques were based on projectionreconstruction methods, the two-dimensional Fourier techniquesoon became the dominant modality for data acquisition and anal-ysis. In fact, it is this which led naturally to the development of thek-space formalism. However, it has long been recognized that Fou-rier analysis has certain limitations in the context of MRI, andaccordingly, non-Fourier encoding techniques continue to receiveattention. In their contribution, Panych, Zientara, and Jolesz presentrecent results on MRI encoding by spatially selective radiofrequencyexcitation. They are particularly concerned with establishing a gen-eral framework for MRI encoding to facilitate further developmentof non-Fourier techniques.

Generally, MRI images are acquired according to a predeter-mined protocol in which the sequence of radiofrequency pulses anddata acquisitions are independent of the object to be imaged. How-ever, there is in principle a great deal of information about thesample which can be extracted from the imaging data as it is beingproduced. This can be used to modify subsequent steps in the dataacquisition to increase overall efficiency. The focus of the article byZientara, Panych, and Jolesz, which is a companion article to theabove, is to describe an approach to such dynamically adaptive MRIin which the spatial encodings are not selected from a predeterminedbasis set, but rather, are computed during the imaging procedure.The number of spatial encodings is also determined during dataacquisition rather than being specified in advance. This provides anapproach to reduction of scan times for MRI.

Functional MRI (fMRI) is a topic of increasing activity in MRIin which brain activation, as reflected by local blood flow, is as-sessed on a regional basis. A number of techniques are underdevelopment for this purpose, and the basic goal remains the im-provement of spatial and temporal localization. In their contribution,Moser, Baumgartner, Barth, and Windishberger creatively apply themethods of fuzzy cluster analysis to fMRI data. The potential of thismethod is clearly outlined.

In the contribution by Weaver, the use of monotonic fits betweenimage extrema has been applied to achieve effective noise reductionin MRI. The method described is conceptually simple, but importantimprovements are shown in fMRI data by application of the algo-

International Journal of Imaging Systems and Technology, Vol. 10, 107–108 (1999)© 1999 John Wiley & Sons, Inc. CCC 0899–9457/99/020107-02

rithm. Another application centers on reducing phase error; phase isa frequently hidden feature of MRI images, which are generallydisplayed as pixel magnitudes with concomitant loss of phase in-formation. However, phase is used in many applications, such asflow field determinations. Here, it is shown that the error in thephase can be significantly reduced by use of the noise reductionalgorithm presented. Finally, improvement of contrast to noise inMRI is demonstrated.

While MRI has many advantages over other techniques forspecific applications, it remains a relatively insensitive technique.This leads to intrinsic limitations on spatial resolution, related pri-marily, though not exclusively, to signal-to-noise considerations; forsmaller picture or volume elements, less of the desired signal isreceived, while noise continues to be produced from the entiresample. Nevertheless, in certain circumstances spatial informationon a scale smaller than the nominal voxel size can be derived.Hwang and Wehrli show how this may be done to obtain estimatesof volume fractions in the case of two-phase materials at subvoxelresolution. Their chosen application, bone mineral density, is idealsince it both fits the assumptions of their model, allowing excellentquantitative results to be obtained, and because it represents animportant approach to evaluation of osteoporosis, which is currentlythe subject of a great deal of medical interest.

Another specific emerging application of MRI is in the detectionof breast cancer. However, before the method achieves widespreaduse, a number of potential sources of artifacts must be overcome.One of the problems specifically addressed in the article by Fischer,Otte, Ehritt-Braun, Laubenberger, and Hennig is the requirement forincreasing the diagnostic sensitivity and specificity of contrastagent-enhanced MRI mammography without sacrificing spatial res-olution. This requires an algorithm for registration of images thatdoes not require pixel averaging. However, global alignment using,for example, a rigid-body motion formulation is inapplicable tobreast tissue. Hence, the authors develop a local elastic matchingalgorithm which is suitable for automated analysis and effectivelycompensates for motion.

Richard Spencer is Chief of the Nu-clear Magnetic Resonance Unit of theNational Institute on Aging of the Na-tional Institutes of Health in Baltimore,Maryland. He obtained his Ph.D. inMedical Physics from the Massachu-setts Institute of Technology in 1987,working with Professor Joanne Ingwallat the NMR Laboratory for Physiolog-ical Chemistry of Harvard MedicalSchool, and his M.D. from HarvardMedical School in 1988. He was apostdoctoral fellow with ProfessorRobert Griffin at the Francis Bitter Na-tional Magnet Laboratory of the Mas-

sachusetts Institute of Technology before joining the National Insti-tutes of Health. He completed medical residency training at JohnsHopkins Bayview Medical Center in Baltimore, and is a Diplomat ofthe American Board of Internal Medicine.

His current research interests are in three areas. The first areais magnetic resonance spectroscopy and imaging methodology, in-cluding a statistical approach to experimental design for parameterestimation in the presence of noise. He is also doing work in tissueengineering in which neocartilage is characterized by MRI-derivedparameters, tissue biochemical assays, and mechanical testing. Fi-nally, he is continuing work in cardiac and peripheral musclemetabolism, recently including MR spectroscopic evaluation of theresponse of peripheral muscle ischemia to gene therapy delivery ofvascular endothelial growth factor.

Richard SpencerNuclear Magnetic Resonance UnitNational Instiute on AgingBaltimore, MA 21224

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