TITLE:

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

AUTHORS:

Damon, Bruce M.

Institute of Imaging Science and= the Departments of Radiology and Radiological

Sciences, Biomedical Engineering, and Molecular Physiology and Biophysics

Vanderbilt University=

Nashville, TN USA

bruce.damon@vanderbilt.edu

Li, Ke

Institute of Imaging Science and= the Department of Radiology and Radiological

Sciences

Vanderbilt University=

Nashville, TN USA

ke.li@vanderbilt.edu

Dortch, Richard D.

Institute of Imaging Science and= the Department of Radiology and Radiological

Sciences

Vanderbilt University=

Nashville, TN USA

richard.d.dortch@vanderbilt.edu

Welch, E. Brian

Institute of Imaging Science and= the Department of Radiology and Radiological

Sciences

Vanderbilt University=

Nashville, TN USA

brian.welch@vanderbilt.edu

Park, Jane H.

Institute of Imaging Science and= the Departments of Molecular Physiology and

Biophysics and Radiology and Radiological Sciences

Vanderbilt University=

Nashville, TN USA

jane.park@vanderbilt.edu

Buck, Amanda K.W.

Institute of Imaging Science and= the Department of Radiology and Radiological

Sciences

Vanderbilt University=

Nashville, TN USA

amanda.buck@vanderbilt.edu

Towse, Theodore F.

Institute of Imaging Science and= the Departments of Physical Medicine and

Rehabilitation and Radiology and Radiological Sciences

Vanderbilt University=

Nashville, TN USA

theodore.f.towse@vanderbilt.edu

Does, Mark D.

Institute of Imaging Science and= the Departments of Biomedical Engineering and

Radiology and Radiological Scien= ces

Vanderbilt University=

Nashville, TN USA

mark.does@vanderbilt.edu

Gochberg, Daniel F.

Institute of Imaging Science and= the Departments of Radiology and Radiological

Sciences and Physics and Astrono= my

Vanderbilt University=

Nashville, TN USA

daniel.gochberg@vanderbilt.edu

Bryant, Nathan D.

Institute of Imaging Science and= the Department of Radiology and Radiological

Sciences

Vanderbilt University=

Nashville, TN USA

nathan.bryant@vanderbilt.edu

CORRESPONDING AUTHOR:

Damon, Bruce M.

Institute of Imaging Science and= the Departments of Radiology and Radiological

Sciences, Biomedical Engineering, and Molecular Physiology and Biophysics

Vanderbilt University=

Nashville, TN USA

bruce.damon@vanderbilt.edu

SHORT ABSTRACT:

Neuromuscular diseases often exh= ibit a temporally varying, spatially heterogeneous, and multi-faceted pathology. The goal of this protocol is to characterize this pathology using non-invasive magnetic resonance imaging methods.

LONG ABSTRACT:=

Quantitative magnetic resonance imaging (qMRI) describes the development and us= e of MRI to quantify physical, chemical, and/or biological properties of living systems. Neuromuscular diseases often exhibit a temporally varying, spatial= ly heterogeneous, and multi-faceted pathology. The goal of this protocol is to ch= aracterize this pathology using qMRI methods. The MRI acquisition protocol begin= s with localizer images (used to locate the position of the body and tissue of interest within the MRI system), quality control measurements of relevant magnetic field distributions, and structural imaging for general anatomical characterization. The qMRI portion of the protocol includes measurements of= the longitudinal and transverse relaxation time constants (T1 and T2, respectively). Also acquired = are diffusion-tensor MRI data, in which water diffusivity is measured and used = to infer pathological processes such as edema. Quantitative magnetization transfer imaging is used to characterize the relative tissue content of macromolecul= ar and free water protons. Lastl= y, fat-water MRI methods are used to characterize fibro-adipose tissue replace= ment of muscle. In addition to describing the data acquisition and analysis procedures, this paper also discusses the potential problems associated with these methods, the analysis and interpretation of the data, MRI safety, and strategies for artifact reduction and protocol optimization.

KEYWORDS:<= /p>

DT-MRI, MT, FWMRI, atrophy, fat replacement, biophysical basis, neuromuscular disorders, inflammatory myopathies, muscular dystrophy

INTRODUCTION:

Quantitative magnetic resonance imaging (qMRI) describes the development and us= e of MRI to quantify physical, chemical, and/or biological properties of living systems. QMRI requires that one adopt a biophysical model for the system, composed of the tissue of interest and an MRI pulse sequence. The pulse sequence is designed to sensitize the images’ signal intensities to t= he parameter of interest in the model. MRI signal properties (signal magnitude, frequency, and/or phase) are measured and analyzed according to the model. = The goal is to produce an unbiased, quantitative estimate of a physical or biological parameter having continuously distributed, physical units of measurement. Often the equations describing the system are analyzed and fit= ted on a pixel-by-pixel basis, producing an image whose pixel values directly reflect the values of the variable. Such an image is referred to as a parametric map.

A common use of qMRI is the development and application of biomarkers. Biomarkers can be used to investigate a disease mechanism, establish a diagnosis, determine a prognos= is, and/or assess a therapeutic response. They may take the form of the concentrations or activities of endogenous or exogenous molecules, a histological specimen, a physical quantity, or an internal image. Some general requirements of bioma= rkers are that they objectively measure a continuously distributed variable using physical units of measurement; have a clear, well understood relationship w= ith the pathology of interest; are sensitive to improvement to and worsening of clinical state; and can be measured with suitable accuracy and precision. Non-invasive or minimally invasive biomarkers are particularly desirable, as they promote patient comfort and minimally disturb the pathology of interest.

A goal for developing image-based biomarkers for muscle disease is to reflect muscle disease in ways that are complementary to, more specific than, more spatially selective than, and/or less invasive than existing approaches. One particular advantage of qMRI in this regard is that it has the potential to integrate multiple types of information and thus potentially characterize m= any aspects of the disease process. This ability is very important in muscle diseases, which frequently exhibit a spatially variable, complex pathology = that includes inflammation, necrosis and/or atrophy with fat replacement, fibros= is, disruption of the myofilament lattice (“Z-disk streaming”), and membrane damage. Another adva= ntage of qMRI methods is that qualitative or semi-quantitative descriptions of contrast-based MR images reflect not just pathology, but also differences in image acquisition parameters, hardware, = and human perception. An example of this last issue was demonstrated by Wokke et al., who showed that semi-quantitative assessments of fat infiltration are highly variable and frequently incorrect, when compared with quantitative fat/water MRI (FWMRI)¹.

The protocol described here incl= udes pulse sequences for measuring the longitudinal (T₁) and transverse (T₂) relaxation time constants, quantitative magnetization transfer (qMT) parameters, water diffusion coefficients using diffusion tensor MRI (DT-MRI), and muscle structure using structural images= and FWMRI. T₁ is measure= d by using an inversion recovery sequence, in which the net magnetization vector= is inverted and its magnitude is sampled as the system returns to equilibrium.= T₂ is measured by repea= tedly refocusing transverse magnetization using a train of pulses generated accor= ding to the Carr-Purcell Meiboo= m-Gill (CPMG) method², and sampling the resulting spin-echoes. T₁ and T₂ data can be analyzed using non-linear curve-fitting methods that either assume a number= of exponential components a priori (typically between one and three) or by using a linear inverse approach whi= ch fits the observed data to the sum of a large number of decaying exponential= s, resulting in a spectrum of signal amplitudes. This approach requires a non-negative least square (NNLS) solution³, and typically includ= es additional regularization to produce stable results. T₁ and T2 measurements have been widely used to study muscle diseases and injury^=
4-9. T₁ values are typica= lly decreased in fat-infiltrated regions of muscle and elevated in inflamed reg= ions^4-6; T₂ values are elevat= ed in both fat-infiltrated and inflamed regions¹⁰.

QMT-MRI characterizes the free w= ater and solid-like macromolecular proton pools in tissues by estimating the rat= io of macromolecular to free water protons (the pool size ratio, PSR); the intrinsic relaxation rates of these pools; and the rates of exchange between them. Common qMT approaches include pulsed satu= ration¹¹ and selective inversion recovery^12,13 methods. The protocol below describes use of the pulsed saturation approach, which exploits the broad linewidth of the macromolecular proton signal, relative to the narrow linew= idth of the water proton signal. By saturating the macromolecular signal at resonance frequencies sufficiently different from the water signal, the water signal is reduced as a result of magnetization transfer between the solid and free water proton pools. The data are analyzed using a quantitative biophysical model. QMT has been developed and applied in healt= hy muscles^14,15, and a recent abstract appeared describing its implementation in muscle disease¹⁶. QMT = has been used to study small animal models of muscle inflammation, wherein it h= as been shown that inflammation decreases the PSR¹⁷. Inasmuch as MT reflects both macromolecular and water contents, MT data may also reflect fibrosis^18=
,19.

DT-MRI is used to quantify the anisotropic diffusion behavior of water molecules in tissues with ordered, elongated cells. In DT-MRI, w= ater diffusion is measured in six or more different directions; these signals are then fitted to a tensor model²⁰. The diffusion tensor, D, is diagon= alized to obtain three eigenvalues (which are the three principal diffusivities) a= nd three eigenvectors (which indicate the directions corresponding to the three diffusion coefficients). These and other quantitative indices derived from = D provide information about tissue structure and orientation at a microscopic level. The diffusion properties = of muscle, especially the third eigenvalue of D and the degree of diffusion anisotropy, reflect muscle inflammation^{17<=
/sup>
and muscle damage due to experimental injury²¹, strain injury22}, and disease^23,24. Other potential influences on the diffusion properties of musc= le include changes in cell diameter²⁵ and membrane permeability changes.

Lastly, muscle atrophy, without = or without macroscopic fat infiltration, is a pathological component of many muscle diseases. Muscle atrophy can be evaluated by using structural images= to measure muscle cross-sectional area or volume and FW-MRI to assess fatty infiltration. Fat infiltration can be qualitatively described in T₁- and T₂-weighted images²⁶, but fat and water signals are best measured by forming images that exploit the different resonance frequencies of fat and water protons^27-29. Quantitative fat/water imaging methods have been applied in muscle diseases such as musc= ular dystrophy^1,30,31, and can predict the= loss of ambulation in these patients³¹.

The qMRI protocol described here uses all of these measurements to characterize musc= le condition in the autoimmune inflammatory myopathies dermatomyositis (DM) and polymyositis (PM). Further de= tails of the protocol, including its reproducibility, have been published previou= sly³². The protocol includes standard pulse sequences as well as radiofrequency (R= F) and magnetic field gradient objects specifically programmed on our systems. The authors anticipa= te that the protocol is also applicable in other neuromuscular disorders characterized by muscle atrophy, inflammation, and fat infiltration (such as the muscular dystrophies).

PROTOCOL:

NOTE: The reader is reminded that all research involving human subjects must be approved by the local Institutional Review Board (IRB) for the Use of Human Subjects in Research. Research participants must be informed of the purpose, procedures, risks, a= nd benefits of the proposed research; the availability of alternative treatmen= ts or procedures; the availability of remuneration; and of their rights to pri= vacy and to withdraw their consent and discontinue their participation. Prior to= the MRI testing session, an investigator must present a potential research part= icipant with an IRB-approved informed consent document (ICD), explain its contents,= and ask the potential research participant if he/she wishes to participate in t= he study. If so, the participant= will have to sign and date the ICD prior to completing any of the steps of the protocol here.

1. Actions Pr= ior to the Day of Testing

1.1) Restrict Lifestyle Habits that Could Confound the Data

1.1.1) Instruct the participant = not to perform moderate or heavy exercise during the 48 hours prior to testing. Instruct the participant to abstain from over-the-counter medication and alcohol intake during the 24 hours prior to testing. Instruct the participa= nt to refrain from tobacco use or caffeine consumption during the 6 hours prio= r to testing.

1.1.2) Prior to testing, confirm that the participant has been compliant with these instructions.=

1.2) Prepare = the MRI System

1.2.1) Ensure the availability of all necessary equipment, as listed in the Table of Materials and Equipment.=

1.2.1) Define = an MRI protocol; suggested parameters are found in Tables 1-5

2. Day of Testing: Prepare for MRI Data Acquisition

2.1) Conduct Safety Screening

2.1.1) Screen for potential haza= rds in the MRI environment by having an MRI safety-trained healthcare worker present the research participant with a suitable MRI safety form, such as t= hat found at www.mrisafety.com.

2.1.2) If there any implanted magnetic or magnetically sensitive objects, ensure that they are safe for MRI scanning.

2.2) Prepare = the MRI System

2.2.1) Ensure = that all personnel have removed all magnetic and magnetically sensitive objects before entering the room that houses the MRI system. Conduct this check eve= ry time that someone enters the MRI room.

2.2.2) Prepare= the MRI system by placing the receive coil on the patient bed of the MRI system. Also, place a mattres= s with sheet and pillow with pillowcase on the bed. Have straps available to place around the thighs and bolsters or pillows to place under the knees.

2.2.3) Start the software interf= ace, enter patient data, and open the imaging protocol.

2.3) Position= the Research Participant on the MRI Scanner Table

2.3.1) Observe the research participant as he/she checks his/her person and clothing for magnetically sensitive objects. Secure the= se objects outside of the MRI room in a lockable container. Enter the MRI room with the research participant immediately after completing this step.<= /o:p>

2.3.2) Position the participant on the patient bed in a supine, feet-first position. Place = the body part to be imaged as close to the midline of the table as practical. P= lace bolsters or pillows under the knees to provide strain relief for the lower = back and place a pillow under the head. To limit motion, gently but effectively secure the thigh, leg, and f= eet and ensure that the participant is comfortable.

2.3.3) Place t= he RF receiver coil around the participant’s thighs and connect it to the MRI system.

2.4) Instruct= the Participant and Complete Final Pre-testing Steps

2.4.1) Give instructions about h= ow to communicate with the investigators. Provide the participant with hearing protection and a signaling device that can be used to call for attention if needed. Instruct the particip= ant of the need to stay still during and between all imaging sequences.

2.4.2) Advance= the patient bed into the MRI scanner such that the body part to be imaged is aligned to the center of the MRI scanner.

2.4.3) After= exiting the MRI room, confirm that the patient communication system is work= ing and see that the participant is comfortable. Throughout the protocol, communicate regularly with the participant to ensure his/her comfort and compliance with instructions.

3. Day of Testing: Acquire the MRI Data

3.1) Preparat= ory Steps

3.1.1) As the = MRI system determines the instrumental settings and calibrations prior to each imaging sequence (center frequency, receiver gain calibration, etc), supervise these processes and ensure that each = step is being performed correctly.

3.1.2) Using a suitable software interface, acquire a set of localizer images (also known = as pilot or scout images); using suggested parameters presented in Table 2.

3.1.3) Determi= ne where to place the center slice for qMRI data acquisitions, by identifying areas of damage and/or by referencing the slice position relative to reproducible anatomical landmarks. <= /p>

3.2) Transmit= and Receive Coil Calibration Steps

3.2.1) For the= se steps as well as all of the subsequent imaging steps, define region of anat= omy in which to optimize the homogeneity of the static magnetic field (B_{0<=
/sub>),
a process known as “shimming”. See Figure 1A for the typical
placement of the shimming volume of interest (VOI) used in the present stud=
ies.}

3.2.2) If the MRI scanner has a multi-element transmission coil, acquire an RF calibration dataset.

3.2.3) If the MRI scanner has a multi-element receive coil, acquire a spatial sensiti= vity map of the coils.

3.3) Acquire Structural MRI Data

3.3.1) Acquire high resolution, multi-slice, T1-weighted images using a fast spin-echo (FSE) sequence; the imaging parameters used in the present studies are provided in Table 1.

3.3.2) Acquire high resolution, multi-slice, T2-weighted images using an FSE sequence; the imaging parameters used in the present studies are provided in Table 2.

3.4) Acquire = Data for Real-time Quality Control and Making Post-processing Corrections

3.4.1) Acquire three-dimensional (3D) multiple gradient-echo data for the calculation of B₀ field maps. The imag= ing parameters used in the present studies are provided in Table 3. =

3.4.2) Examine= the field maps to ensure that there are no deviations of greater than ±6= 0 Hz (approximately 0.5 parts per million at 3 Tesla) across the image. If there are, adopt an alternative approach to shimming (different method, different placement of VOI, etc.).

3.4.3) Acquire= 3D data for the calculation of nutation angle maps. The imaging parameters use= d in the present studies are provided in Table 2.

3.4.4) Examine= the field maps to ensure that there are no areas that deviate excessively from = the nominal nutation angle. For the RF pulses that are used in this protocol, deviations greater than ±30% of the nominal nutation angle are considered excessive.

3.5) Acquire the qMRI Data=

3.5.1) Acquire= 3D images for calculation of the T1, using an inversion recovery sequence. The imaging parameters used in the present studies are presented in Table 3.&= nbsp;

3.5.2) Repeat the T₁ measurement in the presence of fat signal suppression (FS; this parameter is abbreviated T₁<= sub>,FS).

3.5.3) Acquire single-slice images for calculation of the T₂, using a multiple spin-echo Carr-Purcell Meiboom-Gill sequence. Use the imaging parameters pre= sented in Table 3.

3.5.4) Repeat the T₂ measurement in the presence of FS (T_2,FS).

3.5.5) Acquire= 3D images for calculation of qMT parameters, using= a pulsed saturation sequence with FS and the imaging parameters given in Table 4.

3.5.6) Acquire multi-slice data for calculation of diffusion-tensor parameters, using a se= ries of diffusion-weighted images. The imaging parameters used in these studies are given in Table 4.

3.5.7) Acquire= 3D data for calculation of fat/water images, using a series of six gradient-ec= ho images. The imaging parameters used in these studies are given in Table 5.<= o:p>

3.6) After Completing the qMRI = Protocol

3.6.1) Ensure = that all images are of suitable quality by examining them for potentially correctable artifacts and by measuring the sufficient signal-to-noise ratio= .

3.6.2) For eac= h qMRI dataset, define several regions of interest (ROI= s) in the image series and examine the signal as a function of the relevant param= eter (for example, for the T₁= -dependent data acquired in steps 3.5.1 and 3.5.2, plot the signal as a function of TI= and ensure that the data follow the inversion-recovery function listed below in step 4.1.2).

3.6.3) After completing a personal screening for magnetically sensitive objects, enter the MRI room. Remove the participant from the magn= et, remove all straps and padding, and assist the participant in exiting the MRI scanner and the MRI room.

3.6.4) Transfer the data, using methods compliant with local health privacy laws, to a local workstation for processing; data may be exported as Digital Imaging Communi= cations in Medicine (DICOM) files or in the vendor’s proprietary format (the method used in this protocol).

4. Analyze th= e qMRI Data

4.1) Calculate the Parametric Maps

4.1.1) Use a computer program designed for scientific computing and image analysis. By examining a histogram of the si= gnal intensities in the image, form a signal threshold-based image mask that delineates areas of signal from areas of noise. Complete the steps below for every pixel in the signal portions of the images.

4.1.2) Analyze the T₁ data by measuring the signal intensity S for each inv= ersion time (TI). Then, fit the values for S to an inversion-recovery with reduced pre-delay model:

where M₀ is a signal intensity representing the magnetization at the equilibrium sta= te, S_f is the inversion rat= io, and TD is the pre-delay time. T= hen, fit the data with FS to the same model, allowing determination of the longitudinal relaxation time constant with FS, T₁_,FS.

4.1.3) Analyze theT₂ data by measuring S at each TE. Then, fit the data t= o a mono-exponential decay model:

where N is the signal offset at baseline. The reader may also decide to fit the dat= a to a multi-exponential model, such as that below:

where J is the number of exponential components and f and T_2,j are the sig= nal fraction and T₂ valu= es associated with the j^th component. Or, the reader may use a non-negative least squares (NNLS) metho= d³. In the latter case, the Multi-exponential Relaxation Analysis (MERA) toolbox³³ is freely available; other programs are available too. Repeat these analyses for the = data with and without FS.

4.1.4) To analyze the qMT data, measure S for each irradiation power and frequency offset. Correct the nominal irradiation powers (represented by w₁ in the equation below) using the nutation angle maps. Correct the frequency offsets (Df in the equation below) by using= the B₀ maps to adjust the applied offset frequencies. Then, fit the = data to the following model^34,35:

where is the exchange rate from the macromolecular pool to the free water pool, is the longitudinal relaxation rat= e of the free water pool, is the longitudinal relaxation rate of the macromolecular pool (assumed to be 1 s<= sup>-1), is the PSR, is the T₂ of the free water pool, and w_1CWPE is the average power of the saturation pulse. The saturation rate of the longitudinal magnetization of = the macromolecular pool, , is described by a super-Lorentzian model, as described in work by Henkel= man and colleagues^34,35.

4.1.5) To analyze the DTI data, first use an affine transformation algorithm^{36
to register each diffusion-weighted image to the corresponding non-diffusion
weighted image. Then, for each pixel, measure the values for S in the non-diffusion weighted im=
age
and in each diffusion-weighted direction. Form a matrix composed of the
diffusion encoding directions.
Using multivariate, weighted least squares regression, regress the
signal data on the diffusion encoding matrix and form D. Diagonalize D and perform a magnitude-sorting of the eigenvalues and their
eigenvectors. Then calculate =
the
mean diffusivity (MD) as:}

where l₁, l₂, and l₃ are the eigenvalues of the diffusion tensor. Also calcul= ate the fractional anisotropy (FA) as:

4.1.6) Analyze the FWMRI data us= ing a quantitative approach that separates water and fat signals based on chemi= cal shift (such as the FattyRiot algorithm, availab= le for free download from http s://github.com/welcheb/FattyRiot).

4.2) Define Regions of Interest for Analysis

4.2.1) Specify ROIs on the anatomical images (by defining the boundaries of each muscle of interest). An example is shown in Figure 1.

4.2.2) Resize the ROIs to match = the matrix size of the qMRI images. As necessary, a= djust the alignment of the ROIs to match the qMRI map= (for example, if the participant moved between acquisitions, a translation of the ROI position might be required to avoid overlapping the muscle boundaries).=

4.2.3) Examine each ROI. If necessary, ensure that no pixels are included that contain par= tial volume artifacts, non-contractile tissue, and flow artifacts; please see Fi= gure 1 for examples.

4.2.4) Calculate the mean and standard deviation of the qMRI values in all pi= xels within the selected ROIs.

REPRESENTATIVE RESULTS:

Figure 1 shows representative axial anatomical images acquired at the mid-thigh of a patie= nt with dermatomyositis. Also shown is the location of the in-plane projection of the shim volume.&nb= sp; Representative parameter maps for each qMRI method, all obtained from this same patient, are provided from Figures 2-7.

Figures 2A an= d 2B show the DB₀ and nutation angle field maps, respectively. The B₀ field map demonstrat= es a strong spatial coincidence between its area of highest field homogeneity and the placement of the VOI for shimming, as indicated in Figure 1A. Within the muscles, the DB₀ values ranged from -40 to 52 Hz, with a mean of 9.3 Hz and a standard deviation of 11.2 Hz. In the nutation angle map, the val= ues ranged from 84.7 to 122.3% of the nominal nutation angle. By comparing the nutation angle ma= p with the structural images in Figure 1, it can be seen that the deviations from ideal nutation angle are more severe in the posterior muscles and are not obviously correlated to the presence of fat in a voxel.

Sample T₁ relaxometry data are presented in Figure 3. The images have been masked to exclude the subcutaneous fat and noise regions of the image. Figure 3A shows sa= mple T₁ data and Figure 3B s= hows sample T₁_,FS data. It is evident in Figure 3A that t= he T₁ of fat is considerab= ly lower than that of muscle; therefore the T₁ values for muscle measured without using FS are lower than the T_{1,FS =}values. Also, the use of FS results in substantial loss of signal from regions of f= at replacement or subcutaneous fat. Consequently, there are pixels in these im= age regions that are either devoid of fitted parameters, that represent residual water signals following FS, or in which the parameters are poorly estimated= .

Figure 4A sho= ws a masked parametric map of the T_=
2_,FS values and Figure 4B shows the T₂ values. Figure 4C shows a sample T₂-dependent signal dec= ay from a single pixel and the best fit of the data to a = monoexponential model. A deviation from monoexponential relaxat= ion behavior is noted. Figure 4D = shows the results of NNLS analysis of these same signal data, with a single broad peak that likely includes both fat and water components. =

Figures 5, 6,= and 7 present examples of qMT, DTI and FWMRI data, respectively. For the qMT data, only the PSR is shown. The application of a s= ignal threshold to these FS-data restricts curve-fitting to those voxels containi= ng primarily muscle, resulting in dropout from the parameter map. Heterogeneit= y in the muscle values for PSR is also noted. Although the method also estimates= the water T₂ and the exc= hange rate between the macromolecular and free water proton pools, these are not presented because the T₂= is better estimated using dedicated imaging sequences and because the excha= nge rate is both unreliably estimated and insensitive to pathology. =

Figure 6A presents a parametric map of the MD, and Figure 6B presents a map of the FA values. MD values are elevate= d in blood vessels. Also, FA values are reduced in the regions corresponding to reduced PSR. As with other quantities, both the MD and the FA are inaccurat= ely estimated in fat-replaced portions of muscle, where FS causes signal fallout. Also, FA is elevated outside of the shim volume. L= astly, a fat fraction map, calculated from the FWMRI data, is shown in Figure 7. T= hese data quantify the qualitatively observed fat infiltration patterns noted in Figure 1. The corresponding water fraction map is simply equal to (1 – Fat) and is not shown.

Figure 1: Sam= ple anatomical images from a patient with dermatomyositis. All of the data shown in Figures 2-7 were acquired at th= is slice position from this participant. A. T₁-weighted imag= e, with the in-plane projection of the shim volume overlaid as the cyan-colore= d, semi-transparent rectangle. B. T₂-weighted image. Overlaid on the image in green is a sample ROI for the vastus medialis muscle.&nbs= p; Through the semi-transparent ROI, areas of high signal, correspondin= g to fat replacement, are noted. The yellow arrow indicates an intramuscular ten= don, and the magenta arrow indicates the region of the neurovascular bundle of t= he thigh. The images should be i= nspected for flow artifacts that may occur along the phase-encoding dimension and in line with the artery. Connective tissues such as fat and tendon are recommendation for exclusion from ROIs; also, if flow artifacts exist, they should be excluded.

Figure 2: DB₀ and nutation angle maps, from the same patient depicted in Figure 1.= A. DB₀ map, with the color scale indicating the deviation of the B0 field from the center frequency in Hz.&nbs= p; B. Nutation angle map, = with the color scale indicating the percentage of the nominal nutation angle. Im= ages have been masked to exclude the values from the noise regions of the image.

Figure 3: Sam= ple T₁ data, from the same patient depicted in Figure 1. A. Map of the T₁ values, estimated by fitting the data to the inversion recovery with reduced pre-delay model. The color scale indicates = the T₁ value in s. B. Map of the T₁= _,FS values, estimated by fitting FS data to the same model. The color scale indicates the T₁ value in s. The images have been masked to exclude values from the subcutaneous = fat, the contralateral leg, and the noise regions of the image. Note that the T₁ values are increased= when fat signal suppression is used.

Figure 4: Sam= ple T₂ data, from the same patient depicted in Figure 1. A. Map of the T₂ values, estimated by fitting the data to the monoexponential decay with noise term model. The color scale indicates the T₂ value in ms.&n= bsp; B. Map of the T_2,FS values, estimated by fitting the data to the same model. In A and B, the images have been ma= sked to exclude values from the subcutaneous fat, the contralateral leg, and the noise regions of the image.   C. Sample T₂ signal decay from a pixel in panel C and line of best fit to the monoexponential model (but note the deviation of the = signal from the model, which indicates a non-monexponential T₂). Abbreviation not previously noted: AU, arbitrary units. D. Non-negative least-squares analysis of the same raw signal decay data depic= ted in C.

Figure 5: Sam= ple qMT data, from the same patient depicted in Figure 1.= The color scale indicates the PSR,= a dimensionless quantity reflecting the ratio of macromolecular to free water protons. The use of FS methods results in substantial signal dropout from those regions of muscle that have been replaced by fat.

Figure 6: Sam= ple diffusion data, from the same patient depicted in Figure 1. Panel A sho= ws the mean diffusivity, with the color scale indicating the diffusivity with unit= s of 10^-3 mm²/s. Panel B shows the fract= ional anisotropy, which is a dimensionless quantity indicating the deviation of t= he diffusion system from purely isotropic diffusion.

Figure 7: Sam= ple FWMRI data, from the same patient depicted in Figure 1. The color scale indicates the f= at fraction; the corresponding water fraction map is simply (1 – Fat) an= d is not shown.

Table 1: Parameters used for localizer imaging and structural imaging. All sequences use the quadrature body coil for transmitting RF fields. Parameters such as TR, TE, echo spaci= ng, and number of echoes can be adjusted slightly according to experimental nee= ds while still maintaining T₁- and T₂- weighting. Abbreviation not previously noted: = g, parallel imaging acceleration factor. Large field of view images are recommended, as they can be used to localize pathology and identify locatio= ns for qMRI sequences. The sagittal and coronal acquisitions are particularly helpful in this regard.

Table 2: Parameters used for DB₀ and nutation angle mapping. Both sequences use the quadrature-body coil for transmit= ting the RF field; neither sequence uses parallel imaging.

Table 3: Parameters used for T₁ and T₂ mapping. T₁ and T₂ data are acqu= ired with and without FS. Both sequences use the quadrature body coil for transmitting RF fields and a six-element cardiac coil for signal reception. The repetition time varies for the T₁-ma= pping sequence because it uses a fixed pre-delay time with variable inversion tim= e.

Table 4: Parameters used for qMT and DTI. Both sequences use the quadrature-body coil for transmitting the RF field and a six-element cardiac coil for signal reception. Abbreviation not previously noted: EPI, echo-pla= nar imaging.

Table 5: Parameters used for FW-MRI.

DISCUSSION:

Muscle diseases such as the musc= ular dystrophies and idiopathic inflammatory myopathies constitute of group of diseases that are heterogeneous in etiology and, as individual entities, ra= re in their incidence. For examp= le, Duchenne muscular dystrophy – the most common form of muscular dystro= phy – has an incidence of 1 in 3,500 live male births^37,38; dermatomyositis, imaging examples of which were shown here, has an incidenc= e of 1 in 100,000³⁹. The higher collective incidence of these diseases, however, and their often overlapping pathological signs – atrophy, inflammation, fat infiltrat= ion, membrane damage, and fibrosis – support the development and applicati= on of a common set of methods for quantitatively characterizing these diseases= .

QMRI is able to characterize man= y of these pathophysiological changes non-invasively. As with any scientific met= hod, qMRI studies must be implemented in a careful manner. A fundamental issue is safety. Also, each qMRI method described here has associated sources of = error; and for obvious reasons, it is important to understand and recognize these errors. Lastly, many of the measurements have a complex interpretation. The= se issues are discussed here. In presenting the Discussion, we note that the protocol presented here describes what we feel is the best experimental approach for our purposes. We recognize that others may have differing views, additional knowledge, or may choose to weigh the potential outcomes of protocol optimization differently than we have. Also, the reader’s MRI system may not have all of the options described in the protocol available; or the reader may have additio= nal options that are not available on our system. We have noted which aspects o= f our protocol have been custom-programmed on our system. The reader is advised to consider all of the literature completely, examine all relevant options on his/her system, and make decisions that result in the best possible protocol for his/her experimental aims.

MRI Safety Issues

MRI uses several types of magnet= ic fields. The B₀field strength of the system used in the studies described here is 3.0 Tesla, or approximately 15,000 times the Earth’s field of ~0.2 mTesla. Pulsed RF magnetic fields (B₁) are used to introdu= ce energy into the spin system and create the resonance phenomenon. Gradient magnetic fields are turne= d on and off during the imaging sequence and are used for several purposes. They are used to create a linear relationship between NMR frequency and spatial position for the purpose of spatial encoding and are also used to eliminate unwanted sources of signals.

Each of these types of magnetic fields has safety concerns associated with it. A major safety concern associated with the B₀ field is the acceleration of magnetic objects toward the magnet. The B₀ field is always pres= ent. Because the strength of a magnetic field varies as a function of 1/d³, where d is the distance from the source = of the field, the B₀ field increases rapidly as one approaches the MRI system. Ferromagnetic objects can be accel= erated toward the MRI system with little or no warning and can cause severe injury= or death. Therefore, they must be removed and secured outside of the MRI room. Other hazards associated with the B= ₀ field are the placement of abnormal torques on implanted magnetic objects a= nd erasure or other damage to magnetically sensitive devices. B₁ fields can heat tissues, and this effect may be enhanced in the areas around implanted metal objects. Gradient fields can induce electri= cal currents in conductive objects (such as nerves and implanted medical device= s). The switching of gradient fields a= lso generates potentially loud and unpleasant acoustic noise. Government regula= tory agencies have placed strict limits on the levels and durations of exposure = to these various types of magnetic fields, and human imaging systems have intrinsic software controls that ensure compliance with these guidelines.

The reader should know that this presentation is somewhat cursory. It is incumbent upon all personnel associ= ated with MRI testing to be fully aware of all relevant safety issues and how to prevent accidents. Also, all personnel associated with MRI testing should be screened for potentially hazardous implanted metals or medical devices.

Pre-testing Lifestyle Restrictions=

Exerting as much experimental control over pre-testing lifestyle behaviors as possible is an important component of this protocol. The case of T₂ measurements is provided as an example of why this control is needed. T₂ is considered a lead= ing MRI biomarker of neuromuscular disease⁴⁰. However, the muscle water proton T₂ can be elevated for several reasons. In qMRI studies for neuromuscular diseases, theT₂ measured in the pres= ence of FS is generally presumed to reflect a state of chronic inflammation rela= ted to disease severity, while the non-FS T₂ can also reflect fat infiltration. However, T₂ c= an also undergo medium-term elevations because of eccentric exercise^{41,
which may affect patients and healthy subjects
differently⁴². For this reason, the authors recommend restricting
moderate or heavy exercise for 48 hours prior to testing. T₂
can also undergo shorter-lived elevations as a result of acute bouts of
exercise^43,44. For a patient with sev=
ere
muscle loss, walking could constitute exercise intense enough to elevate T₂. For this reason, the
authors recommend that on the day of testing, patients be transported over =
long
distances using an MRI-compatible wheelchair.}

Data Acquisition and Analysis: General Issues=

An important point is that caref= ul pilot testing, first in healthy persons and then in persons with the diseas= e of interest, is essential. Many experimental options are highly specific to the MRI system (including but n= ot limited to B₀ field strength, shimming strategies, RF coil options, maximum magnetic field gradient stren= gth, and the availability of advanced options such as RF pulse shape). Sequence-specific pilot testing goals are described below. Other issues that affect data quality are biological in nature, such as the type of disease a= nd the expected types of pathology, age of the patient population, and even the body part to be imaged. All of these factors should be considered during pi= lot testing.

During data acquisition itself, a frequently encountered problem is motion. The imaging portion of the protoc= ol presented here can require as much as one hour. Some of the sequences (such as echo-planar imaging) are insensitive to bulk motion; but other sequences are long, require exact image alignment for accurate parameter estimation, and/= or have signals that are intrinsically motion-sensitive. As noted in the proto= col, taking steps to instruct the participant and promote his/her comfort is an important way to prevent both voluntary and involuntary movements. Another strategy is to limit motion with padding and gently, but effectively, placed straps that attach to the patient bed. Image registration techniques are available for post-processing; because muscles are easily deformable organs, non-rigid registration techniques are often required. Non-rigid registration will always= be required for diffusion imaging methods based on echo-planar imaging. Despite the general usefulness of image registration techniques, any method for preventing motion or reducing artifacts will be superior to solutions that require extensive post-processing. Defining the best available motion-reduc= tion strategy in the subject population of interest should be a goal for pilot testing.

Good reproducibility requires consistency of slice placement. In the protocol steps, we describe referencing the slice position to reproduci= ble anatomical landmarks. An effe= ctive strategy for this in the thigh is to obtain coronal images of the entire th= igh, permitting visualization of the entire femur. The image analysis tools on the MRI system typically include a digital ruler function. This can be used to measure a spec= ific point (such as the midpoint) of the head of the femur and the femoral condy= les, and place the center position of the slice stack there. This procedure is illustrated in t= he video.

Inhomogeneous B₀ and B1 fields are unavoidable problems in MRI, but strategies exist for reducing t= he levels of inhomogeneity. A fundamental strategy is to locate the body part = to be imaged at or near the magnet’s center. The protocol presented here includes the B₀ shim= ming routines that, in the authors’ experience, are most effective for the= se experimental conditions. Because the participant may move during the protoc= ol, B₀ shimming is repeated= as part of the calibration steps for every sequence. Also, many of the acquisitions use parallel imaging techniques to accelerate signal acquisiti= on and thereby reduce the DB₀-dependent differences in phase evolution that cause image distortions.&nb= sp; Because the RF transmit coil employed in these studies contain two c= oil elements, B₁ shimming methods can be used and are described in the protocol. In addition, the protocol includes= DB₀ and nutation angle field mapping sequences for real-time quality control. The protocol described above inclu= des the tolerances in DB₀ and nutation angle that are acceptable for the experimental conditions, RF pulse shapes, and gradient spoiling schemes described here. These were determined in pilot testing and re-emphasize the value of careful protocol development. <= /p>

In practice, there may be a limi= ted number of strategies available in real-time to affect the B₀ and B1 fields’ homogeneities, while maintaining the methodological consisten= cy that is necessary for good experimental design. Users are therefore advised= to investigate all options available to them with thorough pilot testing, ultimately arriving at effective and generally applicable strategies for the subject population of interest. B₀ shimming options inc= lude iterative methods that minimize a parameter such as the water peak’s line-width at half-maximum peak height and methods that calculate the optim= um shim channel settings using a DB₀ map. The former methods can be based on= a non-localized acquisition or, as in the protocol described here, the acquisition of signal from a localized volume. The goals for pilot testing = of B₀ shimming options inc= lude the best general strategy (iterative vs. image-based) as well as the particulars of how best to define the region of interest for shimming. The reader may wish to consider factors such as the size and orientation of the volume of interest, the relative amounts of muscle and fat to include in the shimming volume, and how beyond the slice stack to shim. It is worthwhile to examine the within-slice projection of the shim volume in each slice to be imaged.=

In the case of the B₁ field, the type of RF coils used for transmission and reception and the types of RF pulses used a= re important determinants of field homogeneity. The protocols described in the tab= les include the RF pulse parameters that we have found optimal for our experime= ntal conditions. Regarding coil se= lection, the protocol described here combines separate volume transmission and receive-only volume coils. The transmission coil is the quadrature-body coil that is built into the system, and creates a relatively homogeneous B₁ field across a large anatomical region. Depending on the anatomical region = to be studied, there may be a variety of receive coil options; in our case, pi= lot testing showed the six-element, phased array cardiac coil to be the best available solution. Other opt= ions available include surface coils and combination transmission/receive volume coils. Surface coils are limi= ted in depth of penetration of the B₁₌ field and we do not generally recommend their use for imaging applications. Combination transmission/receive volume coils may offer better signal-to-noise ratio (S= NR) performance and B₁ homogeneity than a built-in quadrature-body coil, but are not available for= all anatomical regions. A final comment is that when phased-array coils are available, they permit the use of parallel imaging techniques that speed up acquisition and reduce spatial distortions in techniques such as echo-planar imaging. These gains come with an SNR penalty, however, and so pilot testing should be directed toward finding the solution that provides best overall i= mage quality.

But because these strategies do = not completely compensate for inhomogeneous B₀ and B₁ fields, anoth= er use of DB₀ and nutation angle field maps i= s in post-processing. These maps c= an be used to improve the calculation of some quantitative parameters or to corre= ct image distortions. But some DB₀- and B₁-related problems may not be fully or even partly correctable in post-processing. Some examples include reduced efficacy of FS methods, gross image distortions in techniques such as echo-planar imaging,= low signal, poor refocusing efficiency in T₂ measurements or FSE methods, and poor inversion efficiency in T₁ measurements. Again, rigorous pilot testing and real-time quality control steps are essential.

Many of the sequences use fat-si= gnal suppression or water-selective excitation as a mechanism for avoiding muscle signal contamination by fat and/or for reducing the existence of artifacts caused by the different resonance frequencies of water and lipid protons. When FS is used, a combination of = up to three methods is employed. The aliphatic signals are reduced or eliminated using a spectrally selective adiabatic inversion recovery (SPAIR) pulse, which selectively inverts these signals. As the signal recovers from a signal value of –M₀ toward + M₀, there is a time at which the net signal equals zero. The imaging data are acquired at this signal nulling point. It should be no= ted that this time point depends on parameters such as the repetition time and = the number of slices, and so must be optimized separately for each sequence dur= ing the pilot testing process. Al= so, the bandwidth of the SPAIR pulse should be only wide enough to eliminate fat signals, so that the reduction of water signal amplitude is kept to a minim= um. Taking steps to maximize B₀ uniformity will be helpful in this regard. Many of the sequences also use a saturation pulse on the olefinic proton resonan= ce⁴⁵; this pulse is applied immediately prior to the imaging sequence. Where possible, a gradient reversal technique is used. In this me= thod, the sign of the slice selection gradient is reversed between the slice selection and refocusing pulses; this causes signals f= ar off-resonance from water not to be refocused. An additional advantage of this ap= proach is that unlike RF-based methods, gradient reversal does not allow fat signa= ls to recover by longitudinal relaxation during the RF pulse train. Additional= strategies, such as Dixon-based methods⁴⁶, are also available.

A common issue in data analysis = is whether to use mean ROI signal analyses (in which the signals in an ROI are averaged and then fitted to a model) or pixel-based analyses (in which the model fitting occurs on a pixel-by-pixel basis, and statistics are then calculated for the fitted parameters).&nbs= p; The advantage of the former method is that signal averaging improves= the effective SNR. If the intrinsic SNR is low, then this strategy may help to = avoid the parameter-biasing effects of the noise floor. The advantage of the latter approa= ch is that spatial heterogeneity is a common pathological feature of neuromuscular disorders. By fitting the val= ues on a pixel-by-pixel basis, this heterogeneity can be appreciated and used to characterize additional aspects of disease phenotype. If the SNR permits this type of an= alysis to be performed validly, the authors recommend this approach. Recent work by Willcocks and colleagues illustrates the value of this approach in monitori= ng disease progression⁴⁷.

Data Acquisition and Analysis: Imaging Sequence-Specific Issues

The protocol uses inversion reco= very methods for a robust measurement of T₁. A practical limitation of many implementations of the inversion recovery sequence is a long total scan time. The sequence used in th= is protocol uses a three-dimensional, Fast, Low-Angle Shot (FLASH) readout, a modest amount of parallel imaging acceleration, and a reduced pre-sequence delay to decrease the total scan time to less than two minutes. Seven inversion times are sampled, spaced in an approximately geometric progression from 50 to 6000 ms. This strategy samples the inversion-recovery signal curve most frequently during those portions of the signal recovery when the time derivative of the signa= l is highest. The sequence is repe= ated with and without FS because inflammation and fat infiltration have confound= ing effects on the overall proton T1: inflammation increases the water T<= sub>1, while fat has a lower T₁= than water. Thus measuring bo= th T₁ and T₁_,FS aids in = the interpretation of the data because it allows one to resolve between these opposing influences of fat infiltration and inflammation on T₁. Parameter estimation is accomplish= ed by using non-linear, least square regression methods in a scientific computing software package.

The T₂ measurements are conducted under FS and non-FS conditions as well, and for an analogous reason: inflammation and fat each = can increase the T₂. In addition to inflammation, patho= logic processes such as Z-disc streaming and losses to membrane integrity would a= lso be expected to influence the water = T₂ values. Although the measurem= ent of both T₂ and T₂_,FS cannot= distinguish among all of these sources of pathology, this practice does afford increased interpretability to the data by resolving between general and muscle tissue-specific pathology. An alternative strategy to measuring a water-onl= y T₂ value is to use 1H MR spectroscopy to separate water from lipids on the chemical shift axis of= the spectrum. Although this approach has significantly lower spatial resolution than imaging and may be subject to user discretion and subjectivity regardi= ng volume placement during data acquisition, it provides an unambiguous way to separate water and lipid signals.

The protocol for T₂ measurement presente= d here employs several methods to mitigate some common sources of error in T₂ measurements, namely= B₁ inhomogeneity and stimulated echo formation from imperfect refocusing pulses. Stimulated echoes are formed by any combination of three non-180° pulses.&= nbsp; Given that some level of B1 inhomogeneity always exists, and that multi-echo trains are used to sample = the T₂-dependent signal dec= ay, stimulated echoes are a potentially significant source of error in T₂ measurements. The strategies used here to elimin= ate stimulated echo formation include the use of a single slice acquisition, an optimized sequence of spoiler gradients before and after the refocusing pul= ses⁴⁸, linear echo spacing⁴⁹, and the use of the B₁-insensitive “Version-S” composite refocusing pulse⁵⁰, which significantly reduces the artifacts ca= used by imperfect refocusing and while still providing a sufficient bandwidth for refocusing both water and lipid signals.&n= bsp; In pilot testing, we observed that the optimized spoiling scheme and= the Version-S pulse significantly reduced the appearance of stimulated echoes.<= span style=3D'mso-spacerun:yes'> We note that both of these objects= have been programmed specifically on our system. The Version-S pulse does increa= se the specific absorption rate (SAR) of RF energy; thus a long TR and larger inter-echo spacing are required to remain within the safety limits for SAR. However, the authorsR= 17; experience is that well coached, comfortable patients can remain sufficient= ly still during the ~12 min. total scan time.= Also, the inter-echo spacing value of 14 ms is sufficient to detect multi-exponential relaxation, when it exists. An alter= nate approach, not employed here, is to include refocusing pulse efficiency and stimulated echoes into the fitting^{38,28,
which will provide a B₁<=
/i>
map and permit multi-slice acquisitions³⁹. The reader is also referred to sev=
eral
recent papers describing the implementation and interpretation of T₂ measurements in musc=
le
disease, which provide some similar and some different recommendations
concerning these methods^40,51.}

The protocol presented here uses= the pulsed saturation method for qMT imaging. Altho= ugh there are five fitted parameters that are generated, only the PSR is report= ed. This is because the other four parameters are either better estimated by us= ing other methods (such as the T_2<=
/sub> of the free water pool) or lack pathological sensitivity (such as the excha= nge rate between pools^52,53=). Compared with other qMT methods, 3D coverage can be achieved within a clinically feasible time for the pulse saturation method. Another advantage= to this qMT approach is its compatibility with the spatial-spectral binomial pulse methods for water-selective excitation, whi= ch was found to suppress >95% fat signals throughout the image. Both the water-selective excitation pulse and the off-resonance saturation pulses ha= ve been customized on our system. Previous numerical simulations⁵⁴ = have indicated that an additional fat component to the signal may bias qMT parameter estimates; thus FS is always recommende= d for qMT imaging in skeletal muscles. As discussed above, excessive B₁ inhomog= eneity and motion artifacts can bias qMT parameter est= imates as well.

The DT-MRI protocol is implement= ed with attention to spatial distortions in echo-planar imaging, SNR, and b-value. Here, spatial distortions= are reduced by using parallel imaging, and corrected in post-processing by usin= g an affine registration. As noted in previous works, the SNR and b-value have interactive effects o= n the estimation of D^{55-57,
with low SNR values resulting in particularly erroneous estimation of l₁, l₃, v₁, and FA^{55,57-59. In muscle, the SNR requirements for
accurate tensor estimation are lowest in the range b=3D435-725 s/mm^{2 55-57,60. Although other authors^61,62 have reported favorable results from using <=
span
class=3DSpellE>denoising}} approaches for muscle DT-MRI, the large ROIs
analyzed in this protocol have sufficient signal averaging so as not to req=
uire
these additional steps. The reader is referred to several reviews of the to=
pic
of optimal implementation of DT-MRI methods^56,63.}

Lastly, some caveats and possible sources of error related to quantitative FWMRI are noted. First, the FattyRiot fitting algorithm adopted here assumes a sp= ecific fat spectrum with nine peaks at fixed locations and relative amplitudes64. The assumed fat spectrum is not a perfect match to the true in vivo spectrum, which will vary subject to subject; however, solving for an arbitrary fat spectrum is not practical with a small number of echoes. Second, the algorithm fits for a single R₂* decay fac= tor shared by both water and fat signals. It is known that completely ignoring = R₂* confounds quantitat= ive fat signal fraction measurements, and that fitting for a single R₂* decay is adequate65. However, the exact R₂* of the water and individual fat peaks varies. Third, FWMRI separation algorith= ms using complex images are vulnerable to severe B₀ field inhomogeneity that can cause misclassificat= ion of fat and water signals. In addition to using robust spatially constrained algorithms, a smaller echo spacing allows capturing larger B₀ field variations. Algorithms using magnitude imag= es are more robust in the presence of = B₀ field inhomogeneity, but they suffer SNR penalties. Algorithms using complex images may also be confounded by eddy currents or any other time-varying ph= ase effect. Such confounding phase effects are typically worst for the first ec= ho in a multiple echo readout train and can be mitigated by simply ignoring su= ch echoes. Alternatively, a mixed magnitude and complex signal model can be adopted⁶⁶. Users of FWMRI algorithms that take complex images as input should avoid other sources of potential perturbation of the complex images such as corrections applied in the image reconstruction pipeline on = many commercial MRI scanners. Such phase corrections should be deactivated, or t= he user should reconstruct images directly from the original raw data. Finally, any estimation of fat fraction using FWMRI is actually an estimation of fat signal fraction, and thus is influenced by any factor that differentially scales the fat or water signals. The T₁ is the primary factor affecting fat and water signal levels in a typical gradient echo scan. T₁-weighting is a function of T₁,= TR, and excitation nutation angle. T1 bias in fat signal fraction estimates is worst for voxels with a nearly equ= al mix of water and fat. Increasing the TR or reducing the nutation angle can minimize the bias. T₁ bias can also be corrected retrospectively using assumed T₁ values for water and fat, as we do here (1.4 s and 0.3s for water and fat, respectively), or measured values.

Protocol Formation/Sequence Selection<= /i>

As discussed above, the muscle pathologic landscape is a complex one.&nbs= p; FWMRI is unique among the measurements in this protocol in that it h= as an unambiguous interpretation. As noted, many of the other qMRI biomarkers measured here have a non-specific pathological basis that often includes edema but may also include fat infiltration, fibrosis, membrane damage, and sarcomeric disruption. It is emphas= ized that some of these sensitivities are still just hypothesized to exist. There is a considerable amount of = work that needs to be done in order to demonstrate, quantitatively, the relative importance of these and other pathologic processes or states to each qMRI biomarker. With such understanding, the multi-parametric approach described here may allow, through the combination= of variables, more specific descriptions of individual pathologies.=

Alternatively, the reader may el= ect to adapt this protocol by selecting a subset of the measurements presented here. For example, the added = value of FS and non-FS measurements is probably low in conditions not characteriz= ed by fat replacement of muscle. This could allow for reduced imaging time for= the patient, additional measurements to be made (such as MR spectroscopy, MRI perfusion imaging, etc), or additional body par= ts to be imaged. As many muscle dis= eases present in a proximal-to-distal fashion, the protocol described here is implemented in the thighs, as disease in this region may provide an early marker of disease involvement. However, measuring pathology in both proximal and distal regions may allow improved measures of disease progression.=

Conclusions

In conclusion, this qMRI protocol allows the quantitative assessment of e= dema, fat infiltration, and atrophy, which are three major pathologic components = of neuromuscular disorders. By incorporating a broad collection of measurements (T₁, T_2<=
/sub>, diffusion, qMT, FWMRI), the interpretability of= the data is both broadened and deepened. When careful attention is paid to potential sources of error, this approach can accurately and precisely characterize several major components of neuromuscular disease.<= /span>

= DISCLOSURES:

= None of the authors has a financ= ial conflict of interest to report.

= ACKNOWLEDGMENTS:

= We acknowledge grant support from the National Institutes of Health: NIH/NIAMS R01 AR050101 (BMD), NIH/NIAMS = R01 AR057091 (BMD/JHP), NIH/NIBEB K25 EB013659 (RDD), and the Vanderbilt CTSA a= ward RR024975. We also thank the reviewers for the comments and the subject for participating in these studies.

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