Ultrashort echo time spectroscopic imaging (UTESI): an efficient method for quantifying bound and free water

Ultrashort echo time spectroscopic imaging(UTESI): an efficient method for quantifyingbound and free waterEric Diaz, Christine B. Chung, Won C. Bae, Sheronda Statum,Richard Znamirowski, Graeme M. Bydder and Jiang Du*

Biological tissues usually contain distinct water compartments with different transverse relaxation times. In thisstudy, two‐dimensional, multi‐slice, ultrashort echo time spectroscopic imaging (UTESI) was used with bi‐componentanalysis to detect bound and free water components in musculoskeletal tissues. Feasibility studies were performedusing numerical simulation. Imaging was performed on bovine cortical bone, human cadaveric menisci and theAchilles’ tendons of volunteers. The simulation study demonstrated that UTESI, together with bi‐component analy-sis, could reliably quantify both T2* and fractions of the short and long T2* components. The in vitro and in vivo stud-ies each took less than 14min. The bound water components showed a short T2* of ~0.3ms for bovine bone, ~1.8msfor meniscus and ~0.6ms for the Achilles’ tendon. The free water components showed about an order of magnitudelonger T2* values, with ~2ms for bovine bone, ~14ms for meniscus and ~8ms for the Achilles’ tendon. Bound waterfractions of up to ~76% for bovine bone, 50% for meniscus and ~75% for the Achilles’ tendon were measured. Thecorresponding free water components were up to ~24% for bovine bone, 50% for meniscus and ~25% for the Achil-les’ tendon by volume. These results demonstrate that UTESI, combined with bi‐component analysis, can quantifythe bound and free water components in musculoskeletal tissues in clinically realistic times. Copyright © 2011 JohnWiley & Sons, Ltd.

Keywords: ultrashort echo time spectroscopic imaging; bi‐component analysis; bound and free water; T2* relaxation time;bone; meniscus; Achilles’ tendon

INTRODUCTION

Biological tissues usually contain distinct water compartmentswith different transverse relaxation times (T2 or T2*) (1–10).However, conventional clinical T2 relaxometry is usually basedon single‐component analysis of signals obtained with multi‐echo spin echo sequences (11). This is generally partly becauseof technical difficulties and time constraints associated with clin-ical MRI. Carl–Purcell–Meiboom–Gill (CPMG) sequences, whichare typically used to quantify T2 of biological tissues in vitro orin vivo, usually have initial TEs of 10ms or longer on whole‐bodyclinical MR scanners. These TEs are generally too long to imageand quantify fast relaxation time components of the bound wa-ter found in a variety of biological tissues (7,10). In addition,multi‐component analysis without a priori assumptions is verysensitive to the signal‐to‐noise ratio (SNR) (12–14). In this situa-tion, SNRs two to three orders of magnitude higher than thoseachievable with conventional clinical MRI may be required to re-duce fitting errors to less than 2% (10).There are several technical approaches that can be used to ad-

dress the above problems. Firstly, minimization of TEs using ul-trashort TE sequences with nominal TEs of several hundredmicroseconds or less can aid in the detection of short T2 compo-nents using clinical MR systems (15–20). Secondly, replacing amulti‐component fitting model, which has no limits on the num-ber of components in the solution space, with a bi‐componentfitting model, in which only bound and free water componentsare assumed, should greatly improve the reliability of solutions

and reduce the sensitivity to SNR (12,21–23). The Shannon num-ber governs the maximum information obtainable from the mea-sured signal. Any attempts to extract more information or ahigher resolution are bound to yield unreliable results (24).Thirdly, by acquiring a large number of images at different TEs,the fitting error is expected to be further reduced (10,12). Previ-ously, we have reported a technique, ultrashort echo time spec-troscopic imaging (UTESI), which is capable of doing this forshort T2 species in a time‐efficient way (25–27). This techniqueappears to be well suited for the evaluation of the bound andfree water components.

In this article, we describe the use of a two‐dimensional,multi‐slice, UTESI sequence with a minimum nominal TE of8 µs and bi‐component T2* analysis to quantify both T2* andfractions of the bound and free water components in musculo-skeletal (MSK) tissues with short T2 values. A numerical simulationstudy was performed to assess the reliability of the technique. Invitro evaluation of bovine cortical bone and human menisci, and

* Correspondence to: J. Du, University of California, San Diego, Department ofRadiology, 200 West Arbor Drive, San Diego, CA 92103–8226, USA.E‐mail: [email protected]

E. Diaz, C. B. Chung, W. C. Bae, S. Statum, R. Znamirowski, G. M. Bydder, J. DuDepartment of Radiology, University of California, San Diego, CA, USA

Abbreviations used: Carl‐Purcell‐Meiboom‐Gill, maximum likelihood estima-tion; PBS, phosphate‐buffered saline; radiofrequency, region of interestsignal‐to‐noise ration, ultrashort echo time spectroscopic imaging.

NMR Biomed. 2012; 25: 161–168 Copyright © 2011 John Wiley & Sons, Ltd.

Research Article

Received: 8 December 2010, Revised: 11 March 2011, Accepted: 17 March 2011, Published online in Wiley Online Library: 15 July 2011

(wileyonlinelibrary.com) DOI 10.1002/nbm.1728

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in vivo evaluation of healthy and diseased Achilles’ tendons, werealso performed.

MATERIALS AND METHODS

Pulse sequences

A previously reported two‐dimensional multi‐slice UTESI se-quence was implemented on a 3‐T Signa TwinSpeed scanner(GE Healthcare Technologies, Milwaukee, WI, USA) with a maxi-mum gradient performance of 40mT/m and 150mT/m/ms (26).The UTESI sequence employed a half‐sinc radiofrequency (RF)pulse (duration, 472 µs; bandwidth, 2.7 kHz) which, together withvariable rate selective excitation (15–17), radial ramp samplingand fast transmit/receive switching, reduced the nominal TE to8 µs (25–27).

Although details of the UTESI sequence have been describedpreviously (25–27), a brief summary is given below. The se-quence combines highly undersampled interleaved projectionreconstruction with a single‐ or multi‐echo ultrashort TE acquisi-tion at progressively increasing TEs. It employs a single or doublehalf RF pulse for signal excitation, followed by radial ramp sam-pling, which starts almost immediately after the end of the halfRF pulse. Single echo or multiple echoes with an echo spacingof ΔTE (typically 4–6ms) are collected after each half pulse exci-tation. The single or multiple echoes are progressively delayedwith a delay time of Δt (typically 80–300 µs). The full set of pro-jections (e.g. 2025) is interleaved into multiple groups (e.g. 45groups), with each group having a TE that is progressively in-creased by a multiple of Δt. For multi‐echo acquisitions, all theechoes are shifted simultaneously by Δt, but the echo spacingis kept constant at ΔTE. The small number of projections ineach group sparsely but uniformly samples k space. Imagescorresponding to each TE are reconstructed from eachgroup of projections by regridding of the raw data onto aCartesian grid using a Kaiser–Bessel kernel, followed by in-verse two‐dimensional fast Fourier transformation to gener-ate spectroscopic images. The high‐frequency projectiondata from neighboring interleaved groups uniformly coverthe periphery of k space and are shared to reduce streak arti-facts in the time domain. There is no view sharing among thelow spatial frequency projection data, but progressively in-creased sharing of high spatial frequency projection data amongthe nearest neighbor interleaves. This strategy efficiently sup-presses undersampling streak artifacts for each image obtainedat a given TE without affecting significantly its T2* or spectral be-havior (25). The UTESI sequence provides high spatial resolutionspectroscopic images in clinically acceptable scan times. For bi‐component analysis, we used single half RF pulses, instead ofdouble half pulses (28), to avoid differences in the degree ofbound and free water excitation because of their different relax-ation times (29). A single‐echo sequence was used for corticalbone because of its extremely short T2* (30–32). A multi‐echosequence was used for the meniscus and Achilles’ tendon be-cause of their longer T2* (16,25,33,34).

Bi‐component model

Because multi‐component fitting is sensitive to the image SNR,the number of independent components, minimum TE, numberof echoes and separation of the T2 values (10,12–14), three differ-ent approaches were employed.

Firstly, only two components, namely bound water with a shortT2* and free water with a longer T2*, were assumed and bi‐exponential fittingwas used. Compared with a typical non‐negativeleast‐squares fitting, which makes no a priori assumption aboutthe number of relaxation components, the two‐componentapproach was expected to greatly increase the reliability of thesolution and reduce the sensitivity to fitting errors. This is a rea-sonable approximation if there is a distinct difference betweenthe T2* values of bound and free water, and much less betweendifferent bound or free water components.Secondly, UTESI was employed for data acquisition. This pro-

vides earlier and more frequent sampling (25–27). UTESI time‐domain images typically provide signal starting with a near‐zeronominal TE of 8 µs, thereby improving the detection of boundwater components. The UTESI time‐domain signals were normal-ized before bi‐component fitting, so that the sum of the ampli-tudes of the bound and free components equaled unity. Thisregains one degree of freedom in the fitting. UTESI provides alarge number of images at different TEs, greatly enhancing thefitting confidence level and reducing errors.Thirdly, background noise was initially estimated using a com-

prehensive four‐step noise estimation algorithm to avoid fittingbackground noise and so regain another degree of freedom inthe subsequent fitting. In step I of the noise estimation, Otsuthresholding was performed on a mean image obtained fromthe entire series of images to segment signal from noise (35).The background pixels were concatenated across all images inthe series. This increased the number of background samples.In step II, Chang’s method of smoothing was applied using a con-volution of the data with a Gaussian kernel (36). In step III, thefirst maximum on the left of the smoothed histogram was usedas an initial estimate of the standard deviation (σ). Brummer’scut‐off, designated as 2σ, was then used to discard all datagreater than this for least‐squares distribution fitting of a partialhistogram of the second‐order sample moment (37). This hasbeen shown to be a more robust approach than taking the histo-gram of the image, and largely represents pure noise (38,39). Instep IV, the data were fitted using various maximum likelihoodestimations (MLEs) (MATLAB MLE function), including Rayleigh,Rice and Gaussian distributions. The MLE distribution with thesmallest negative log likelihood was chosen to represent thedata. Background noise can be robustly estimated with thisfour‐step processing algorithm.After the above three steps, the normalized UTESI signal was

fitted using the following commonly employed bi‐componentmodel (12,21):

S* TEð Þ ¼ FB TEð Þ � e−TE=T2*B þ 1−FBð Þ � e−TE=T2*F [1]

where S*(TE) is the noise‐free signal decay. Only three fittingparameters, namely T2* of the bound water component (T2*B),T2* of the free water component (T2*F) and the fraction of thebound (FB) or free (FF = 1 – FB) water component, are fitted in thismodel. However, the use of this noise‐free model by itself is in-trinsically incorrect for T2 fitting because the probability of theaccompanying noise distribution actually equaling zero is zero.An alternative model that many researchers use for improvedbi‐component fitting is to add a constant term to Equation [1]to fit noise (11,22,23). However, the constant term and theT2* terms are typically nonorthogonal (13,14), which meansthat they may change each other’s parameter estimation.

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Furthermore, this model gives up a degree of freedom, andhence the confidence of estimates will be reduced.Miller and Joseph (40) have proposed the following noise‐

corrected fitting model to fit T2:

SNC TEð Þ ¼ S* TEð Þ − 2σ2 ¼ e− TE= T2=2ð Þ½ � [2]

where SNC(TE) is the noise‐corrected normalized signal and σis the background noise. This is a region‐based method, in whichthe signal intensity obtained at each TE is first averaged over theregion of interest (ROI) and then fitted to a noise‐corrected expo-nential decay. To allow noise‐corrected bi‐component fitting ona voxel basis, we adopted the following model given by (41,42):

S* TE;σð Þ ¼ σ �ffiffiffiπ2

r� e−α � 1þ 2αð Þ � I0 αð Þ þ 2α � I1 αð Þ½ � [3]

where

α ¼�� S* TEð Þ

2σ

��2 [4]

S*(TE) represents the noise‐free bi‐exponential model inEquation [1] and In is the modified Bessel function of the firstkind of nth order. When this model is used with the least‐squaresfitting algorithm, it minimizes the difference between the pixel’ssignal intensity and the expectation value of Bloch’s signal modelaffected by a Rician bias (38,42). This improves significantly theaccuracy, efficiency and precision of residual minimization andthe parameter estimates. To further improve the accuracy of fit-ting, the formulae for the derivatives of the noise‐correctedmodel were symbolically derived and provided to MATLAB’slsqcurvefit algorithm. A recent report by Raya et al. (42) showedthat this noise‐corrected model is the most accurate and precisemethod for T2 calculation, especially for low‐SNR images.

Simulation

Numerical simulation was performed on a bi‐exponential modelin which a shorter T2* of 2ms with a fraction of 50%, as well as alonger T2* of 20ms with a fraction of 50%, were assumed. A totalof 108 images with TEs spaced uniformly from 8µs to 16ms wasgenerated, and Gaussian white noise was added to produceimages with a representative SNR of 50. The simulated data werethen fitted with the noise‐corrected bi‐component model shownin Equation [3]. One hundred trials with different noise realiza-tions were performed to calculate each mean and standard devi-ation of the short and long T2* values, as well as the fractions ofeach component.

In vitro study

The UTESI sequence was applied to the following two differenttypes of in vitro specimens: bovine femur (n=4) and cadaverichuman menisci (n=4). All specimens were obtained fresh andstored in phosphate‐buffered saline (PBS) solution. Bovine frag-ments with approximate dimensions of 20 × 8 × 4mm3 werecut from mature bovine femur which had been cleaned of exter-nal muscle and soft tissue, using a low‐speed diamond saw(Isomet 1000, Buehler, Illinois, USA) under constant water irriga-tion. The fragments were stored in PBS solution. All the speci-mens were subsequently frozen to −40°C in an ultralow freezer(Bio‐Freezer; Forma Scientific, Marietta, OH, USA), and wereallowed to thaw for 3 h at room temperature prior to rehydrationand MRI. Each specimen was placed in PBS solution for 24 h

(rehydration), and then removed from PBS solution and placedin perfluoro‐octyl bromide solution to minimize susceptibilityeffects at the tissue–air interface. Typical imaging parameterswere as follows: TR = 200ms; field of view, 10 cm; reconstructionmatrix, 512 × 512; 2025 half projections; slice thickness, 2mm formenisci and 5mm for cortical bone. Data with 45 TEs rangingfrom 8µs to 4.5ms were obtained for bone, and data with 54TEs ranging from 8 µs to 45ms were obtained for menisci. The3‐in receive coil was used for meniscus imaging. A home‐built1‐in‐diameter birdcage coil was used for signal excitation and re-ception for bone imaging.

In vivo study

The UTESI sequence was applied to the Achilles’ tendon of threehealthy volunteers and a patient with a normal left Achilles’ ten-don and an abnormal right Achilles’ tendon that had been surgi-cally repaired from a rupture. Typical imaging parameters were asfollows: TR = 200ms; field of view, 12 cm; slice thickness, 2mm; 10slices; reconstruction matrix, 512 × 512; 2025 half projections;four echoes with ΔTE of 4.32ms; 27 interleaved groups of halfprojections; Δt=160 µs; resulting in 108 TEs ranging from 8 µsto 17ms in a total scan time of 13.5min. A 100% slice gap wasemployed to minimize signal degradation caused by slice profileimperfection associated with the slice‐selective half RF pulseexcitations. The 3‐in receive coil was used for signal reception.

Imaging analysis

The single‐ and bi‐component analysis algorithms were writtenin MATLAB and were executed offline on Dicom imagesobtained using the protocols described above. The programallowed the placement of ROIs on the first image of the series.These were then copied to each subsequent image. The meanintensity within each of the ROIs was used for subsequent curvefitting. Bi‐exponential signal decay fitting based on Equation [4]was performed for T2* and fraction measurements for boundand free water components.

RESULTS

Simulation results based on the noise‐corrected bi‐componentfitting model are shown in Fig. 1. An estimated short T2* of1.94 ± 0.01ms, with a fraction of 51.7%, and a long T2* of19.16 ± 1.84ms, with a fraction of 48.3%, were obtained. The sim-ulation demonstrated that the bi‐exponential fitting model canestimate T2* and the fractions of the two components using datawith a clinically achievable SNR of 50, with a fitting error of lessthan 4%.

Figure 2 shows UTESI images of a bovine femur segment (A–F)with a relatively high SNR of 56 in images obtained with a nom-inal TE of 8 µs. Bi‐component fitting of the UTESI images demon-strated a short T2* of 0.35 ± 0.01ms and a longer T2* of2.25 ± 0.02ms with fractions of 75.7% and 24.3%, respectively.The majority of bone signal is from bound water.

Figure 3 shows selected UTESI images (A–F) of a meniscussample, as well as bi‐component fitting (G, H) of the region of in-terest (ROI) shown in Fig. 3A. Distinct features of the fiber struc-ture of the meniscus, including lamella, circumferential andradial fibers, are well depicted by UTESI imaging with high spatialresolution and SNR. The bi‐component model fits the signal de-cay curve with less than 1% residual signal. A short T2* of

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1.86 ± 0.07ms and a long T2* of 14.29 ± 2.84ms are shown, withfractions of 50.5% and 49.5%, respectively.

Figure 4 shows selected in vivo UTESI images of the Achilles’tendon from the normal left ankle and abnormal right ankle ofa 58‐year‐old male patient, as well as bi‐component fitting ofthe UTESI signals from ROIs drawn in Fig. 4B and 4I, respectively.A high signal was demonstrated within the Achilles’ tendon, withthe majority of signal from the tensile tendon decaying quickly

with increasing TE. The endotenon decayed at a much slowerrate, consistent with a much longer T2*.Following rupture and surgical repair, the cross‐sectional area

of the abnormal Achilles’ tendon was approximately twice thatof the normal Achilles’ tendon. There were also distinct morpho-logical differences noticeable, with large circular fibers of scar tis-sue with a relatively long T2*. There was significant fat wateroscillation on the selected UTESI images. The residual signals

Figure 1. Numerical simulation using a bi‐component model with a shorter T2* of 2ms and a fraction of 50%, and a longer T2* of 20ms and a fractionof 50%, signal‐to‐noise ratio (SNR) of 50 and added with Rician noise (A). The simulation data were fitted using Equation [3], and yielded a fitted shorterT2* of 1.94 ± 0.01ms, with a fraction of 51.7%, and a longer T2* of 19.16 ± 1.84ms, with a fraction of 48.3% (B).

Figure 2. Ultrashort echo time spectroscopic imaging (UTESI) of a bovine femur segment (A–F) and bi‐component fitting of the UTESI images (G). Thefitting showed a short T2* of 0.35 ± 0.01ms and a long T2* of 2.25± 0.02ms with fractions of 75.7% and 24.3%, respectively.

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Figure 3. Selected ultrashort echo time spectroscopic imaging (UTESI) of a meniscus sample (A–F), with bi‐component fitting (G, H) of the region ofinterest (ROI) shown in (A). The bi‐component model fitted the signal decay curve with less than 1% residual signal. A short T2* of 1.86 ± 0.07ms and along T2* of 14.29± 0.84ms were found with fractions of 50.5% and 49.5%, respectively.

Figure 4. Selected ultrashort echo time spectroscopic imaging (UTESI) of the normal (A–G) and abnormal (H–N) Achilles’ tendon with TEs ranging from8µs to 17ms, and bi‐component fitting (O, P) of the UTESI signals from regions of interest (ROIs) shown in (B) and (I). A high signal is demonstrated inthe Achilles’ tendon with the majority of the tendon signal decaying very quickly with increasing TE. There is significant signal oscillation caused by fat–water oscillation within the imaging slice, out‐of‐slice signal contamination, or both. A short T2* of 0.61± 0.06ms and long T2* of 8.23 ± 1.29ms werefound for the normal tendon, with fractions of 76.2 ± 6.3% and 23.8 ± 5.1%, respectively. The abnormal tendon has a short T2* of 0.65 ± 0.04ms and along T2* of 4.72± 0.77ms, with fractions of 77.4 ± 5.2% and 22.6 ± 4.8%, respectively.



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were considerably less than 3%, showing good bi‐component fit-ting. A short T2* of 0.61 ± 0.06ms and a long T2* of 8.23 ± 1.29ms,with fractions of 76.2% and 23.8%, respectively, were demon-strated for the normal tendon. A short T2* of 0.65 ± 0.04ms anda long T2* of 4.72 ± 0.77ms, with fractions of 77.4% and 22.6%,respectively, were demonstrated for the abnormal tendon.

Figure 5 shows mapping of the short and long T2* compo-nents of the normal and abnormal Achilles’ tendons. Thecorresponding fractions and linear combination of the shortand long T2* components were also calculated. The linear com-bination approximates T2* of conventional single‐component fit-ting. UTESI allows multi‐slice pixel‐by‐pixel bi‐componentanalysis with a relatively high fitting confidence level in a clini-cally acceptable scan time of around 13min.

DISCUSSION

The preliminary results demonstrate that reliable and fastevaluation of the bound and free water components can beachieved in MSK tissues by bi‐component fitting of UTESIimages both in vitro and in vivo. The simulation study shows thatbi‐component analysis can reduce errors to less than 4% with anSNR of 50, which is consistent with the results published byRaya et al. (42). In vivo studies may suffer from increased out‐of‐slice contamination and motion artifacts; however, the largenumber of images and relatively high SNR allow reliable map-ping of the bound and free water components, as shownin Fig. 4. This is a significant improvement over previouslyreported multi‐component analysis techniques, where anultrahigh SNR is required for mapping of each component ofbiological tissues (10).

There are several advantages of the UTESI approach overexisting clinical techniques, including CPMG, gradient echoand conventional ultrashort TE methods. Simulations by Ana-stasiou and Hall (12) using Cramer–Rao lower bound theoryshowed that fitting errors are linearly dependent on the mini-mum TE, inversely proportional to SNR and exponentially re-duced by increasing the number of echoes. UTESI allows aminimum nominal TE of 8 µs, which is significantly shorter than

that of conventional CPMG or gradient echo sequences on clini-cal scanners. UTESI provides a large number of echo images ina time‐efficient way. It is much faster than regular T2* mappingbased on ultrashort TE acquisitions at progressively increasingTEs (17–20). The large number of images is expected to reducefitting errors. The simulation results by Anastasiou and Hall(12) also demonstrated that the lower bounds increase dramati-cally when the T2 values of the two components approach eachother. In our model, only two components, namely the boundand free water components, are assumed. There is typicallyabout an order of magnitude difference in their T2* values. Com-pared with multi‐component analysis with no limit on the num-ber of components being solved for (1–10), our approach isexpected to extract information with greatly increased reliabilityand reduced fitting sensitivity to SNR. Our model only fits threeparameters, eliminating background noise fitting and one ofthe two component fractions, thus greatly improving the fittingconfidence level (10). Furthermore, UTESI is based on a gradientecho sequence with a much higher duty cycle and lower spe-cific absorption ratio compared with the CPMG sequence,and is thus more suitable for high‐field imaging.The bound and free water T2* values and the corresponding

fractions are broadly consistent with those in the literature. Forexample, single‐component analysis of cortical bone showed aT2* value of 0.3–0.5ms at 3 T (43,44). Bi‐component analysis ofUTESI data showed a bound water T2* of 0.35ms and a free wa-ter T2* of 2.25ms with fractions of 75.7% and 24.3%, respectively.The majority of the Achilles’ tendon (about 76%) had a very shortT2* of 0.6ms, which is roughly consistent with the T2* valuesreported in the literature (16,26,33,34). About half of the menis-cus signal is from bound water, and the other half is from freewater. Linear combination of the shorter T2* (~1.86ms) and lon-ger T2* (~14.29ms) provides a mean T2* of around 8ms, which isin good agreement with single‐component T2* values reportedin the literature (16,17,26).The ability to quantify bound and free water T2* and their

fractions may play an important role in the diagnosis of osteopo-rosis. It is assumed that the long T2* water component in boneoccupies spaces within the Haversian and lacuno‐canalicularsystem, whereas the short T2* component represents water

Figure 5. Bi‐exponential analysis of ultrashort echo time spectroscopic imaging (UTESI) of normal (top row) and abnormal (bottom row) Achilles’ ten-dons of a 58‐year old patient. These show the UTESI images (A, G), maps for short T2* (B, H), short T2* fraction (C, I), long T2* (D, J), long T2* fraction (E, K)and a linear weighted combination of T2* and fractions (F, L). The short T2* component increased by approximately 8%, whereas the long T2* compo-nent decreased by 43%, on the diseased side, probably as a result of the presence of scar tissue.

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bound to the collagen matrix (45,46). Information on bone freewater may be related to porosity, which has been shown to beclosely associated with bone mechanical competence and therisk of bone fracture (47). Information on bone bound watermay be related to bone collagen within the organic matrix andmineralization (46). Quantification of bone bound and free watermay prove to be of central importance in the assessment ofcortical bone.The UTESI bi‐component analysis approach may also play an

important role in the diagnosis of osteoarthritis. The early stageof osteoarthritis is associated with breakdown of the cartilagecollagen matrix, a decrease in proteoglycans and an increase inwater content (7,10,11). The breakdown of the collagen matrixmay result in a significant change in T2* and the bound waterfraction (19,20), and the increase in water content may result inan increase in the free water fraction and an overall increase inT2*. All of these changes may be detected by a single UTESI se-quence, together with the noise‐corrected bi‐componentanalysis.Bi‐component analysis of UTESI images is a T2* technique,

which are known to be more sensitive than T2‐based techniques,such as the CPMG sequence, to local field inhomogeneity. T2*and T2 are related by the equation 1/T2* = 1/T2 + 1/T2’, where T2’reflects the B0 field inhomogeneity and susceptibility. In general,with short T2 species, T2* approaches T2 and T2’ plays a much re-duced role. As a result, the sensitivity to local field inhomogene-ity may be reduced, improving the robustness of bi‐componentanalysis for short T2 species.The UTESI sequence is based on half pulse excitation and

two‐dimensional radial ramp sampling, and is subject toeddy currents and slice profile distortion (48), leading toout‐of‐slice long T2 signal contamination, which may affect theaccuracy of bi‐component analysis. This error can be reducedby measuring the slice selection gradient and following thisby pre‐compensation. The measured readout gradient canthen be used for regridding (48,49). Another limitation is thatthe UTESI sequence used in this study sampled a large numberof echoes, but with uniform echo distribution. Several recentstudies have shown that optimization of the echo distributionmay reduce fitting errors significantly (22,23). Therefore, thereis scope for further improvement by the optimization of TEs.The third limitation is that UTESI required a scan time of around13min. As a result, patient motion may degrade the analysissignificantly.

CONCLUSIONS

The two‐dimensional multi‐slice UTESI bi‐component T2*technique is able to quantify both T2* and the fractions of thebound and free water components in MSK tissues, such as corti-cal bone, menisci and Achilles’ tendon, with good spatial resolu-tion in acceptable times using a clinical 3‐T MR system. It haspotential for the evaluation of MSK diseases and for monitoringthe effects of therapy.

Acknowledgement

The authors are grateful for grant support from GE Health-care and the Radiological Society of North America (RSNAresearch scholar).

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Ultrashort echo time spectroscopic imaging (UTESI): an efficient method for quantifying bound and free water