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Can one assess muscle strength through imaging (e.g., MRI)? If so, though which type of imaging and how accurate is it?

Can one assess muscle strength through imaging (e.g., MRI)? If so, though which type of imaging and how accurate is it?


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Can one assess muscle strength through imaging (e.g., MRI)? If so, though which type of imaging and how accurate is it?

On How does muscle size relate to strength?, I read this answer from Moses (mirror):

Strength training will increase the size and quantity of myofibrils, and subsequently increase the size of the respective muscle fibers; this process is called hypertrophy, and it results in larger and stronger muscles.

As a result, I assume that a medical imaging processing the physical strength of a muscle should assess both the size and quantity of myofibrils. That being said, it can be easier to assess the size as an approximation of strength, and if so, I wonder about 1) the quality of such an approximation 2) how to measure the size of a muscle that what cannot directly easily measured with a tape ( e.g., gluteus minimus or piriformis).


Direct measurement of muscle strength something isn't possible or advised, e.g. if the tendon is injured. Also, some muscles are difficult to isolate when testing. In the case of a tendon injury, assessing the muscle strength would be useful to find a good balance between muscle strengthening and tendon recovery.


Measuring Obesity

What’s the best way to determine whether a body is fat or fit? Body fat can be measured in several ways, with each body fat assessment method having pros and cons.

  • The most basic method, and the most common, is the body mass index (BMI). Doctors can easily calculate BMI from the heights and weights they gather at each checkup BMI tables and online calculators also make it easy for individuals to determine their own BMIs.
  • The BMI and other so-called “field methods”-among them, waist circumference, waist-to-hip ratio, skinfold thicknesses, and bioelectrical impedance-are useful in clinics and community settings, as well as in large research studies.
  • More sophisticated methods, such as magnetic resonance imaging or dual energy X-ray absorptiometry, are so-called “reference measurements”-techniques that are typically only used in research studies to confirm the accuracy of (or as scientists say, to “validate”) body measurement techniques.
  • Several methods can’t be used in children or pregnant women, due to safety concerns, or are less accurate in people who are very overweight. (1)

Here is a brief overview of some of the most popular methods for measuring body fat-from basic body measurements to high-tech body scans-along with their strengths and limitations. (Adapted from (1))

Body Mass Index (BMI)

Body mass index (BMI) is the ratio of weight to height, calculated as weight (kg)/height (m 2 ), or weight (lb)/height (in 2 ) multiplied by 703.

  • Easy to measure
  • Inexpensive
  • Standardized cutoff points for overweight and obesity: Normal weight is a BMI between 18.5 and 24.9 overweight is a BMI between 25.0 and 29.9 obesity is a BMI of 30.0 or higher
  • Strongly correlated with body fat levels, as measured by the most accurate methods
  • Hundreds of studies show that a high BMI predicts higher risk of chronic disease and early death.

Limitations

  • Indirect and imperfect measurement-does not distinguish between body fat and lean body mass
  • Not as accurate a predictor of body fat in the elderly as it is in younger and middle-aged adults
  • At the same BMI, women have, on average, more body fat than men, and Asians have more body fat than whites

Waist Circumference

Waist circumference is the simplest and most common way to measure “abdominal obesity”-the extra fat found around the middle that is an important factor in health, even independent of BMI. It’s the circumference of the abdomen, measured at the natural waist (in between the lowest rib and the top of the hip bone), the umbilicus (belly button), or at the narrowest point of the midsection.

  • Easy to measure
  • Inexpensive
  • Strongly correlated with body fat in adults as measured by the most accurate methods
  • Studies show waist circumference predicts development of disease and death

Limitations

  • Measurement procedure has not been standardized
  • Lack of good comparison standards (reference data) for waist circumference in children
  • May be difficult to measure and less accurate in individuals with a BMI of 35 or higher

Waist-to-Hip Ratio

Like the waist circumference, the waist-to-hip ratio (WHR) is also used to measure abdominal obesity. It’s calculated by measuring the waist and the hip (at the widest diameter of the buttocks), and then dividing the waist measurement by the hip measurement.

  • Good correlation with body fat as measured by the most accurate methods
  • Inexpensive
  • Studies show waist-to-hip ratio predicts development of disease and death in adults

Limitations

  • More prone to measurement error because it requires two measurements
  • More difficult to measure hip than it is to measure waist
  • More complex to interpret than waist circumference, since increased waist-to-hip ratio can be caused by increased abdominal fat or decrease in lean muscle mass around the hips
  • Turning the measurements into a ratio leads to a loss of information: Two people with very different BMIs could have the same WHR
  • May be difficult to measure and less accurate in individuals with a BMI of 35 or higher

Skinfold Thickness

In this method, researchers use a special caliper to measure the thickness of a “pinch” of skin and the fat beneath it in specific areas of the body (the trunk, the thighs, front and back of the upper arm, and under the shoulder blade). Equations are used to predict body fat percentage based on these measurements.

  • Convenient
  • Safe
  • Inexpensive
  • Portable
  • Fast and easy (except in individuals with a BMI of 35 or higher)

Limitations

  • Not as accurate or reproducible as other methods
  • Very hard to measure in individuals with a BMI of 35 or higher

Bioelectric Impedance (BIA)

BIA equipment sends a small, imperceptible, safe electric current through the body, measuring the resistance. The current faces more resistance passing through body fat than it does passing through lean body mass and water. Equations are used to estimate body fat percentage and fat-free mass. (1)

  • Convenient
  • Safe
  • Relatively inexpensive
  • Portable
  • Fast and easy

Limitations

  • Hard to calibrate
  • The ratio of body water to fat may be change during illness, dehydration or weight loss, decreasing accuracy
  • Not as accurate as other methods, especially in individuals with a BMI of 35 or higher

Underwater Weighing (Densitometry)

Individuals are weighed in air and while submerged in a tank. (1) Researchers use formulas to estimate body volume, body density, and body fat percentage. Fat is more buoyant (less dense) than water, so someone with high body fat will have a lower body density than someone with low body fat. This method is typically only used in a research setting.

Limitations

  • Time consuming
  • Requires individuals to be submerged in water
  • Generally not a good option for children, older adults, and individuals with a BMI of 40 or higher

Air-Displacement Plethysmography

This method uses a similar principle to underwater weighing but can be done in the air instead of in water. (1) Individuals sit in a small chamber wearing a bathing suit one commercial example is the “Bod Pod.” The machine estimates body volume based on air pressure differences between the empty chamber and the occupied chamber.

  • Relatively quick and comfortable
  • Accurate
  • Safe
  • Good choice for children, older adults, pregnant women, individuals with a BMI of 40 or higher, and other individuals who would not want to be submerged in water

Limitations

Dilution Method (Hydrometry)

Individuals drink isotope-labeled water and give body fluid samples. Researchers analyze these samples for isotope levels, which are then used to calculate total body water, fat-free body mass, and in turn, body fat mass. (1)

  • Relatively low cost
  • Accurate
  • Safe
  • Can be used in individuals with a BMI of 40 or higher, as well as in children and pregnant women

Limitations

  • The ratio of body water to fat-free mass may change during illness, dehydration, or weight loss, decreasing accuracy

Dual Energy X-ray Absorptiometry (DEXA)

X-ray beams pass through different body tissues at different rates. So DEXA uses two low-level X-ray beams to develop estimates of fat-free mass, fat mass, and bone mineral density. (1) DEXA is typically only used for this purpose in research settings.

Limitations

  • Equipment is expensive and cannot be moved
  • Cannot accurately distinguish between different types of fat (fat under the skin, also known as “subcutaneous” fat vs. fat around the internal organs, or “visceral” fat)
  • Cannot be used with pregnant women, since it requires exposure to a small dose of radiation
  • Most current systems cannot accommodate individuals with a BMI of 35 or higher

Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI)

These two imaging techniques are now considered to be the most accurate methods for measuring tissue, organ, and whole-body fat mass as well as lean muscle mass and bone mass. (1) CT and MRI scans are typically only used for this purpose in research settings.

  • Accurate
  • Allows for measurement of specific body fat compartments, such as abdominal fat and subcutaneous fat

Limitations

  • Equipment is extremely expensive and cannot be moved
  • CT scans cannot be used with pregnant women or children, due to the high amounts of ionizing radiation used
  • Some MRI and CT scanners may not be able to accommodate individuals with a BMI of 35 or higher

References

1. Hu F. Measurements of Adiposity and Body Composition. In: Hu F, ed. Obesity Epidemiology. New York City: Oxford University Press, 2008 53–83.


Abstract

Diffusion MRI-based tractography is the most commonly-used technique when inferring the structural brain connectome, i.e., the comprehensive map of the connections in the brain. The utility of graph theory—a powerful mathematical approach for modeling complex network systems—for analyzing tractography-based connectomes brings important opportunities to interrogate connectome data, providing novel insights into the connectivity patterns and topological characteristics of brain structural networks. When applying this framework, however, there are challenges, particularly regarding methodological and biological plausibility. This article describes the challenges surrounding quantitative tractography and potential solutions. In addition, challenges related to the calculation of global network metrics based on graph theory are discussed.Evidence Level: 5Technical Efficacy: Stage 1

UNDERSTANDING THE BRAIN in terms of structural and functional networks has become one of the frontier topics in neuroscience this is evidenced by considerable investment internationally to prompt the innovation of advanced neuroimaging techniques, to enable identification of the human connectome, i.e., the comprehensive description of brain structural or functional connections. 1-4 There has been a rapid rise in activity in the field of connectomics, where researchers seek to discover the neural substrates underlying cognition and behavior, in either healthy or diseased states. 1, 5-12

As the human brain has a dense neural architecture comprising billions of neurons to form one of the most complex network systems in the world, it is an outstanding challenge to obtain its connectivity patterns in vivo with the elements and connections in different levels for instance, at the microscale (single neurons), mesoscale (a group of neurons), and macroscale (distinct brain gray matter (GM) regions). 3, 4, 13 Modern noninvasive neuroimaging modalities have enabled both functional and anatomical connectivity information to be measured in the living human brain (Fig. 1). Among those techniques, diffusion magnetic resonance imaging (MRI) is the main in vivo technique for inferring white matter (WM) fiber connectivity due to its noninvasive ability to delineate WM pathways in the brain, using so-called fiber-tracking or tractography. 14 To date, diffusion MRI-based tractography has become an essential component of the field of connectomics, for the investigation of WM connectivity in the healthy brain, 15-17 as well as how connectivity is disrupted by brain disorders. 18

Much effort toward investigating human brain connectomics focuses on the application of graph theoretical analysis, which provides a range of metrics that characterize the topology of the network. 19 Such metrics facilitate explorations of the information integration, segregation, and propagation in the brain. With these approaches, researchers have found a nonrandom architecture of brain networks, such as small-worldness, 20 efficiency, 21 modularity, 22 network hubs, 23 and rich-club organization. 24

  • Building an Individual's Connectome From DWIs—presents a general overview of a processing pipeline used for processing diffusion MRI data (page 3).
  • Tractogram Generation—discusses two types of known tractography biases, namely, subsection Streamline Termination Bias (page 4) and subsection Streamline Quantification Bias (page 5). Advanced tractography techniques that aim at tackling these sources of bias will be introduced, and then the outcomes of subsequent graph theory analysis following the application of these existing methods will be discussed (subsection Effects of Bias Correction on Downstream Connectivity Analysis page 6).
  • Connectome Construction—focuses on decisions that need to be made in the course of connectome construction. These include the choice of a brain parcellation scheme to define brain regions-of-interest (ROIs) (Defining Nodes page 6) the definition of inter-areal connectivity (Defining Edges page 8) the mechanism to associate streamlines with brain GM ROIs (Streamline-to-Node Assignment page 9), and then followed by the influence of the Disparities Between Tissue Segmentation and Brain Parcellation (page 9) on the efficacy of connectome generation finally, the need for Assessing Reproducibility of Connectome Construction (page 11).
  • Connectivity Analysis Using Graph Theory—begins with a discussion of the validity and potential implications of performing some processing steps on tractography-based connectomes, such as applying a threshold and/or binarizing the connectome, covered in the subsection Structural Connectome: Binary vs. Weighted (page 12) and Weighted Structural Connectome: Dense vs. Sparse (page 12). Then the section provides the authors’ viewpoints on the computation of weighted graph theory metrics and other topological properties, including a discussion of the role of quantitative tractogram processing in connectomics research (subsection Graph Theoretical Analysis: From Binary to Weighted Metrics page 13). The section ends with some remarks on the analysis and interpretation of group differences in connectomics metrics (subsection Every Bias Correction Matters page 14).
  • Summary—highlights challenging issues and recommended strategies in structural connectome and highlights future perspectives and demand in this rapidly growing field (page 14).

Affiliations

Department of Geriatrics, Neurosciences and Orthopedics, Catholic University of the Sacred Heart School of Medicine, Rome, Italy

Matteo Tosato, Emanuele Marzetti, Giulia Savera, Roberto Bernabei, Francesco Landi & Riccardo Calvani

Gérontopôle, Centre Hospitalier Universitaire de Toulouse, Toulouse, France

Université de Toulouse III Paul Sabatier, Toulouse, France

Novartis Institutes for Biomedical Research, Basel, Switzerland

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Materials and Methods

Modeling Assumptions

To simulate low-field acquisition from data acquired at high field strength, we make six assumptions, listed in Table 1, and explained below.

(1) Body noise dominance.

We assume that body thermal noise is the dominant noise source at all field strengths under investigation (0.1–3.0 T). The validity of this assumption depends on field strength, imaging volume and the receiver coil. It has been shown that body noise dominance can be achieved at frequencies as low as 4 MHz in system sizes compatible with human extremity [15,16], suggesting the feasibility of performing most human scans with body noise dominance at 0.1 T or above.

(2) Consistent field.

We assume that the uniformity of RF transmission is consistent across field strengths. Since the RF operating frequencies go down at low field, the flip angle variation is expected to be smaller in real low-field imaging compared to our simulation.

(3) Consistent field.

We assume that the receiver coils have the same geometry and noise covariance at different field strengths. In order to simulate arbitrary field, it would require accurate coil maps and noise covariance at both acquired and simulated fields, which one may not have.

(4) Consistent B0 homogeneity.

We assume the same off-resonance in parts-per-million (ppm) at different field strengths. This results in less off-resonance in Hz at lower field.

(5) Single species dominance or PDw.

We use a single global relaxation correction function to account for the signal change at different field strengths. Because it is difficult to separate different species from k-space data, this assumption requires similar relaxation patterns at different field strengths for anything that contributes a significant portion to the signal in the region of interest. Although it may be unrealistic for some applications, this restriction can be relaxed in certain cases. For PDw imaging, the simulation is still valid when multiple major species are present (see Appendix for details).

(6) Steady state acquisition.

If the signals are not acquired at steady state, the magnetization relaxation will be determined not only by the sequence parameters but also by the initial state. As a result, a single global relaxation correction cannot be applied and a more complicate time-depend function would need to be calculated.

Simulation of Low Field Acquisition

The process for simulating low-field data from high-field acquired data is illustrated in Fig 1, and described here. The acquired high-field k-space data can be written as: (1) Where sh and nh are pure signal and noise respectively. Under body noise dominance, both the real and imaginary parts of the k-space noise nh can be modeled as multivariate normal distributions: (2) Where Σ ∈ ℝ k×k is the noise covariance matrix for a k-channel receiver coil and is easily measured by data acquisition with RF turned off. Since the thermal noise variance is proportional to B0 2 and readout bandwidth BW, the simulated noise at low field becomes: (3) where l and h stand for low and high field respectively. The pure k-space signal at low field can be modeled as: (4) Where f is a function that represents the signal change due to different relaxation behaviors at different fields. This can be determined with knowledge of the sequence parameters and the dominant species’ relaxation times. The details of calculating f for common sequences are provided in the Appendix. Given f, the simulated low field k-space data can be written as: (5) we can rewrite it as: (6) Where , and from Eqs (2) & (3), we have (7)

High-field k-space data yh and pure noise nh are first acquired and served as input. yh is then scaled by a 2 and f to account for signal magnitude change and different relaxation behaviors at different field strengths. f can be determined based on steady state signal equations for different types of sequences (see Appendix for details). To simulate low-field data additional noise , as calculated in the text, is added to compensate for the different noise levels.

A MATLAB implementation based on the process above as well the examples in this article are available at http://mrel.usc.edu/share.html.

Phantom Validation

To validate the proposed framework, a standard resolution phantom was scanned using a product sequence on 1.5T, 3T, and 7T whole body scanners, all from the same manufacturer (General Electric, Waukesha, WI). The phantom contains NiCl2*H2O and H2O. T/R birdcage head coils (30cm diameter) were used at all field strengths. The 1.5T and 3T coils were single-channel. The 7T coil has two receive channels with nearly identical sensitivities data from only one channel was used. We also scanned the same phantom on a 0.35T ViewRay scanner with a 12-channel torso coil. A single image was formed from all channels using sum of square. The scanner has a cylindrical bore similar to the 1.5T/3T/7T scanners, but is manufactured by a different vendor. Due to its primary function as a MRI-guided radiation therapy instrument, the 0.35T scanner has a unique RF coil design to minimally obstruct the radiation source.

Identical acquisition parameters were used on all four scanners: 2D FSPGR with 62.5% partial k-space acquisition FA 10° TE/TR 3.1/10 ms BW 31.25 KHz FOV 25.6 cm matrix size 256x160 slice thickness 5 mm. T1 and T2 values were measured using inversion recovery SE and SE sequence respectively. Homodyne reconstruction [17] was performed for all images. SNRs were measured in all cases on the magnitude images. For simulated images, the mean and standard deviation of SNR of twenty different simulations were calculated.

Real-time Upper Airway Imaging

For sleep apnea patients, airway compliance is measure of muscle collapsibility. This involves ultrafast 2D axial imaging of the airway and simultaneous airway pressure measurement [9]. During the process, negative pressure is generated by briefly blocking inspiration for one to three breaths. Under these circumstances, airway motion is extremely rapid, requiring about 10 frames per second and millimeter resolution. A custom sequence using 2D golden-angle radial FLASH [18] was implemented on the 3T scanner to acquire an oropharyngeal axial slice of one sleep apnea patient with a 6-channel carotid coil. Imaging parameters: 5° flip angle, 6 mm slice thickness, 1 mm 2 resolution, TE/TR 2.6/4.6 ms, BW 62.5 KHz. A separate scan with RF turned off was performed to calculate the noise covariance. Acquisitions at various low field strengths were simulated using the same imaging parameters.

Twenty-one spokes were used to reconstruct each temporal frame. Conventional gridding [19] was performed on the acquired 3T data and all simulated low-field data. CG-SENSE [20] was also performed with a temporal finite difference sparsity constraint [21]. The NUFFT toolbox [22] was used during algorithm implementation.

Fat-Water Separation

Fully sampled k-space data were collected using an investigational IDEAL sequence. An 8-channel cardiac receiver coil was used to scan one adult volunteer at 3 T. Slice thickness 5 mm, TE 1.4/2.3/3.2 ms, TR 9 ms, flip angle 3°, BW 62.5KHz. To achieve the same phase shift between fat and water, the product of B0 and TE needs to remain the same. Therefore TE’s were set to be (B0,h/B0,l) times longer when simulated at low fields. Bandwidths were also set to (B0,h/B0,l) times shorter, enabled by longer TE’s. Images were reconstructed using the graph cut field-map estimation method [23] from the ISMRM fat-water toolbox [24].

All studies involved were approved by the Institutional Review Board of Children's Hospital at Los Angeles and University of Southern California. Written informed consents were obtained from the participants.


Introduction

The loss of muscle function and mass in the ageing population is increasingly recognized as relevant, common and modifiable. Depending on the definition, the prevalence of sarcopenia in older persons 60–80 years old is often reported in the range of 5–13% [1]. Sarcopenia is increasing to more than 30% in persons 80 years old and older [2]. Many common medical conditions such as stroke, hip fracture and diabetes mellitus and medications like corticosteroids or α-blockers increase the incidence of sarcopenia [3–5]. Public bodies and authorities such as the World Health Organisation, the United States National Institute of Health, the European Medicines Agency and the United States Food and Drug Administration are currently discussing different definitions for sarcopenia. One major point is the question whether sarcopenia should be recognized as a disease entitiy in the revised International Classification of Diseases and Related Health Problems or as a syndrome. An even bigger challenge is the definition of sarcopenia. A European formalized consensus group has made an attempt to come up with a clinical definition for screening [6]. A key issue of a definition is the relationship between muscle mass, impairment and function. There are considerable disparities in studies how and where to measure muscle mass, muscular impairment and functional loss caused by sarcopenia. Many studies have pragmatically measured total body muscle mass using dual-energy X-ray absorptiometry (DXA) with limited accuracy. At the same time muscle function was measured looking at lower extremity function such as habitual gait speed or sometimes hand grip strength as a surrogate marker. The pharmaceutical industry has started trials for the development and evaluation of new medications [7] and non-pharmaceutical interventions [8,9]. In summary, effective treatments to prevent, slow and/or reverse the progression of sarcopenia could be very relevant to influence ageing trajectories. However, in order to assess incidence, prevalence and treatment effects accurate measures are required to measure, screen, assess and follow older persons and certain patient groups. This requires methods to quantify muscle mass and relevant functional measures.

The current gold standard to measure muscle mass is magnetic resonance imaging (MRI) [10]. For muscle mass screening and other purposes other methods such as DXA or bioelectrical impedance analysis (BIA) may be required but their limitations must be realized. To measure muscle strength, power and functional loss caused by sarcopenia several methods have been established and are widely accepted but often not vigorously applied. There is consensus that muscle strength parameters, such as hand grip strength and isometric measurements of the lower extremity, should be measured. Functional performance tests, such as the assessment of chair rise time and gait speed are required and accepted to study the effects of sarcopenia on the loss of independence. All of these tests have been instrumented to increase their accuracy and objectivity [11,12]. Although leg muscle power has been identified as an important determinant of mobility skills in older adults [13] and although leg muscle power has been shown to be associated with muscle volume in young persons [14], at present this measure is often not used to show the efficacy of strategies to counteract sarcopenia. The standard equipment to measure muscle power is the Nottingham Power Rig, where power is calculated from a single leg push in a seated position (Bassey & Short, 1990). Recently it has been shown that the sit-to-stand (STS) power performance, as a functional measure of the lower extremity, can objectively be assessed in older persons by a linear encoder [15,16] or by body-worn sensors [17]. The measurement of muscle power is often neglected even though the relevance is obvious [13,18,19]. The association between muscle mass, strength, function and power is still understudied. Often, authors implicitly state that these measures can be used as surrogate markers interchangingly. While this might be justified for epidemiological studies or sometimes in clinical practise it is certainly questionable in efficacy studies on new medications and non-pharmaceutical RCTs.

The aim of this post-hoc analysis was to investigate and describe the hierarchy of the association between thigh muscle volume and measurements of functional performance in older women. In particular we were interested in the consensus based use of hand grip strength and habitual gait speed to screen for sarcopenia and to compare these with measures of muscle power. We hypothesized that leg muscle power is a potent measure in this hierarchy.


Computed Tomography (CT) and Magnetic Resonance Imaging (MRI)

CT and MRI are cross-sectional imaging modalities providing 2D or 3D maps of pixels allowing for the in vivo measurement of lean mass and total adipose tissue and its subdepots (subcutaneous, intermuscular, and visceral).

CT presents great practical significance due to its routinely use for diagnosis and follow-up in various diseases and allows an accurate quantification of whole-body composition (Figure 2).

Figure 2. CT images of the android region. CT image slices of the android region showing changes in adiposity distribution (visceral fat𠅊rrowheads subcutaneous fat𠅊rrows) depending on age and sex: (A) young male, (B) old male, (C) young female, (D) old female. With advancing age, there is a redistribution of fat mass compartment with increase of visceral compartment for both sexes (in particular for males) it is also noteworthy that the subcutaneous compartment is prevalent in females, both in young and old age. Images are kindly provided by IRCCS Rizzoli Orthopedic Institute, Unit of Diagnostic and Interventional Radiology (2019).

Being a volumetric technique, CT allows to measure body components at tissue-level using pre-established Hounsfield Units (HU) to recognize different tissue density (soft tissue: 30� HU fluid-sovrafluid: 0� HU adipose tissue: � HU bone and calcification: 100� HU).

CT imaging at L3 level provides total, visceral or subcutaneous adipose fat area, visceral adipose volume, total psoas area, and skeletal muscle index (SMI) (38). Moreover, according to ethnicity- and sex-specific data, CT has been used to derive a predictive cardio-metabolic risk (CMR) equation (39). This type of evidence endorsed other specific research, analyzing pericardial fat, intrathoracic fat and epicardial fat, showing the potential contribution in CMR stratification (40). Also, because CT images targeted on the III lumbar vertebra are similar to those on chest, they could be tentatively performed solely. As CT usage has now increased in clinical practice, the radiation exposure should be taken in mind, since it represents a risk factor for oncologic disease development.

Differently from CT that is calibrated against the Hounsfield scale, signal intensities in MRI are often non-quantitative because image intensity values do not reflect physical properties of the imaged body. MRI allows to measure body fat-free mass such as skeletal muscle mass at arms, legs and trunk level, specific organ masses, and provides also an estimate of bone marrow adipose tissue (41). From a technical point of view body composition measurement with MRI is based on the different magnetic properties of hydrogen nuclei contained in water and fat. Several MRI sequences have been developed to measure body fat, using variations in radiofrequency pulse to differentiate between adipose tissue and fat-free mass (27). A variety of pulse sequences are thus available to generate contrast between fat and non-adipose tissue (42). Adipose tissue is characterized by a short T1 and a long T2 relaxation time in T1-weighted spin-echo sequence, fat appears as a high signal (white) because of a high concentration of relative immobile protons, thus differentiating it from muscles, fluids, bone and internal organs, which appear as gray signals (43). The time of acquisition for such sequences is relatively long and implies some issues, such as respiratory/motion artifacts. Variations of this sequence have been developed in order to reduce the acquisition time. Nowadays, a whole-body MRI scan of an individual can be obtained in about 5 min, allowing for the detailed evaluation of total and regional fat depots. Whole-body scanning is the most accurate and reproducible protocol to obtain an accurate quantitative map of body fat distribution and content, but it has been mainly limited to research studies due to the high scan costs and the need of time-consuming image analysis (44). In fact, the amount of data generated by whole-body MRI requires a complex analysis, generally not manually feasible, except for very small studies. In the last years, this has led to the development of semiautomated or automated methods for MRI-based body composition analysis. Furthermore, single-slice and region-specific multi-slice protocols were developed to make data analysis easier and faster (Figure 3) (43, 45). An alternative to whole-body imaging is the acquisition of the solely abdominal region, which allows to measure fat depots frequently associated with CMR factors, like visceral adiposity (46). Multi-slice protocols have become the preferred method for large population studies, while single-slice protocols have been mainly used in small cohort studies, even if a number of protocols differ in the landmarks to be used for acquisition the level of L4–L5 has been the most commonly reported anatomical landmark for single-slice imaging, while a level close to L2–L3 has been considered by several authors as the preferable site to evaluate visceral adipose tissue depot (41, 43). A poor prediction of visceral and subcutaneous tissue changes was reported in a longitudinal study with single-slice MRI evaluation at L4–L5 level (47).

Figure 3. MR T1-weighted image slices of the gynoid region showing age-related muscle changes in both sexes (poor muscle quality and fat infiltration𠅊rrows). In addition larger subcutaneous adipose tissue are observed in the gynoid region of an old female (D) compared to a young female (C) on the contrary the representation of subcutaneous compartment in the same region is the same both for a young male (A) and an old male (B). Images are kindly provided by IRCCS Rizzoli Orthopedic Institute, Unit of Diagnostic and Interventional Radiology (2019).

There is an increasing interest in using MRI to evaluate age-related muscle changes to understand the contribution of poor muscle quality and fat infiltration in sarcopenia. Recently, Yang et al. demonstrated that a single slice cross-sectional area at mid-femur can be used in clinical practice for a fast and non-invasive diagnosis of sarcopenia in old adults (48). Compared to other imaging techniques, a key advantage of MRI is the ability to detect changes in the muscle structure occurring during the aging process or during disease progression, making this technique a powerful tool in longitudinal studies. Quantitative magnetic resonance imaging (QMRI) can be achieved by proton nuclear magnetic spectroscopy or magnetic resonance spectroscopy (MRS), which allows the accurate measurement of intramyocellular lipid and extramyocellular lipid in muscle fibers. MRS can precisely discriminate adipose and lean tissue by enhancing contrast, offering the possibility to estimate the accumulation of tryglicerides in non-adipose tissue (ectopic lipid). Diffuse fat infiltration in organs and lean tissue can be also estimated using “quantitative fat-water imaging,” which is based on Dixon imaging, a gradient recalled echo imaging method which uses the chemical shift between proton resonance frequencies in water and in fat (44). MRI shows the best contrast between fat and muscle tissue, allowing for an accurate evaluation of muscle quality. It has been shown to possess a higher sensitivity compared to CT in detecting early fatty replacement in muscles (49). Differently from DXA, QMRI has the great advantage to be independent of fat-free mass hydration level, showing great accuracy and low-minimal changes detectable in longitudinal studies. However, underestimation of fat mass and overestimation of fat-free mass by QMRI compared with a 4-compartments model has been reported (50). In old adults infiltration of adipose tissue is recognized as a predictor of poor muscle and mobility functions. MRI was used to study intramuscular adipose tissue in frail and non-frail individuals, showing that more muscle fat infiltration was detectable in older frail subjects (18.0 vs. 11.7%) (51). In women over 50 years old, MRI-measured muscle fat infiltration was reported to be positively associated with increased fracture risk (52), while lower extremity muscle fat infiltration was shown to be negatively associated with performance based measures of physical function (53).

Currently, MRI represents the most advanced and accurate technique for the study of body composition, by allowing the measurement and quality assessment of muscle volume and cross-sectional analysis. Its ability to detect changes in the muscle structure occurring with aging makes this technique extremely fascinating to understand age-related progressive loss of muscle strength and quality. MRI, together with CT, represents the gold-standard technique in exploring muscle mass and quality for research purpose, however the limited access to the equipment, the complexity of data analysis and high cost, limit the use of MRI routinely in clinical practice (54). A strong methodological weakness is represented by the lack of a standardized evaluation protocol in image analysis, limiting comparison between studies (55).


Should We MRI All Shoulder Pain?

Today we continue with our Expert Series where 4 Orthopedic Surgery experts in the area of shoulder surgery will continue answering a number of questions about the rotator cuff.
Jeffery Berg, MD : Website, Twitter

As with most diagnosis in medicine, for shoulder problems the patient’s “story” is usually the most important factor in determining the diagnosis. For me, the exam is then next most important. After that the response or failure to treatments (if appropriate for nonoperative care) and finally, imaging studies, including MRIs. In most shoulder problems, MRIs are typically only confirmations of the suspected diagnosis. In younger patients, MRIs are only fair in their ability to accurately identify the common shoulder problems this age group suffers. In older patients, because of the common and often asymptomatic “wear and tear” that is common in these patients, MRI’s often overemphasize the importance of common asymptomatic issues, such as degenerative rotator cuff tears. These are incidentally identified and often do not require any treatment.

As a result, in most cases I try to develop a diagnosis from the history and exam. I use MRIs with shoulder problems in the following situations: 1) Concern for time sensitive or limb or life threatening diagnosis that is unclear from the history and physical exam, 2) Failure to respond to nonoperative treatment and the diagnosis still remains unclear, 3) Need to better define, classify or further assess a known diagnosis, and 3) Preoperative surgical planning (for both me and my patients).

MRI is currently the best way to image the rotator cuff tendons. Not every shoulder pain patient needs an MRI. A good history and physical exam will usually lead to a reasonable diagnosis without advanced imaging. Deciding to obtain a MRI depends on many factors including the time course and severity of the problem, patient age and activity level, and patient desires. Many painful conditions of the shoulder will respond well to non-surgical treatment. If there is no suspicion that there is a serious underlying problem and/or the problem has been present for a short time, a MRI is usually not necessary. If it seems like there is a serious problem that may require surgery or longstanding pain is continuing and increasing despite good care, a MRI may be appropriate.

When I first see a patient over 40 with shoulder pain, my first treatment is anti-inflammatories, physical therapy, and frequently a subacromial steroid injection. I will then see the patients back 6-8 weeks later. If they are much better, there is no need for any further treatment. If they aren’t much better, I get an MRI to evaluate their rotator cuff. I usually do not order an MRI the first time I see them because, even if they do have a small rotator cuff tear, I would like to see if non-operative treatment would help them. If it does, GREAT! They may have avoided a surgery. I only order MRIs if they aren’t getting better. However, if I have a patient with a long history of shoulder pain and weakness and they are very weak on examination, I sometimes do order an MRI after the first office visit.

Because a good history and physical examination are very good at picking up shoulder rotator cuff issues, I typically do not initially get an MRI of a shoulder I’m trying to treat non-operatively. I use MRI’s when the diagnosis is in doubt or for pre-surgical planning. If the patient had a violent trauma with a lot of shoulder dysfunction, I might get an MRI in that setting, as acute rotator cuff repair for a complete tear would be reasonable. Likewise, if the patient is an overhead athlete with months of shoulder pain before I see them, I might get an MRI or even an MRI arthrogram (where dye is injected into the joint), to look for a SLAP tear (tear of the labral cartilage), which can occur with internal impingement.

Everyone who presents to an Orthopedist’s office with shoulder pain will not require an MRI. As our experts discussed, we may choose to MRI your shoulder if :

  • Your diagnosis is in doubt
  • You have not responded to physical therapy and we want to assess the quality of your rotator cuff
  • You have suffered a severe injury and present with weakness

Next time you visit an Orthopedist for shoulder pain do not be upset if they do not order an MRI. They are rarely necessary to successfully treat the majority of people with shoulder pain.

Disclaimer: this information is for your education and should not be considered medical advice regarding diagnosis or treatment recommendations. Some links on this page may be affiliate links. Read the full disclaimer.


Affiliations

Department of Mechanics, Royal Institute of Technology, Stockholm, Sweden

Clara Körting, Marius Schlippe, Kangqiao Zhao & Ruoli Wang

Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden

Department of Clinical Science Intervention and Technology, Karolinska Institutet, Stockholm, Sweden

Karolinska Institutet, Department of Clinical Sciences, Danderyd Hospital, Division of Rehabilitation Medicine, Stockholm, Sweden

The Swedish School of Sport and Health Sciences, Stockholm, Sweden

Olga Tarassova & Anton Arndt

Department of CLINTEC, Karolinska Institutet, Stockholm, Sweden

Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland

Department of Children’s and Women’s Health, Karolinska Institutet, Stockholm, Sweden

KTH Biomex Center, Royal Institute of Technology, Stockholm, Sweden

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Contributions

C.K., K.Z. and M.S. developed the DTI fascicle tracking pipeline, US-MR alignment and contributed to the image post-processing. S.P., G.V.P., O.T., A.A. and R.W. participated in the study design and data collection. T.F. contributed to the image post-processing. All authors participated in the manuscript preparation.

Corresponding author


Conclusions

Focal white matter lesions, which are hyperintense on T2-weighted scans, are among the pathological hallmarks of multiple sclerosis, and MRI is formally included in the diagnostic work-up of patients with suspected multiple sclerosis ( McDonald et al., 2001). Current MRI criteria for multiple sclerosis are based on imaging features that are characteristic of the disease, but are not sufficiently specific. Over time, revisions of the multiple sclerosis diagnostic criteria have improved the sensitivity, particularly adding the capability to confirm the diagnosis at first clinical presentation.

However little attention has been given to describing the imaging features included in these criteria in detail, and guiding neurologists and neuroradiologists in correctly interpreting them. In patients with few lesions, there is a particularly increased risk of misdiagnosis based on MRI. We hope that the guidelines provided will minimize the risk of inappropriate image interpretation and increase the awareness of redflags.

As mentioned earlier, these criteria should only be used in the appropriate clinical context, when onset is characterized by clinical manifestations typical of multiple sclerosis.

Different scanners and field strengths, upgrades in equipment, and changes in acquisition parameters could influence lesion evaluations. However, although high-field MRI enables the detection of a higher number of white matter lesions in CIS and multiple sclerosis patients ( Wattjes et al., 2006), field strength has been shown not to affect fulfilment of criteria for dissemination in space and time, also in a multicentre setting ( Wattjes et al., 2008 Hagens et al., 2018).

Accordingly, if MRI studies are performed on scanners with a minimum field strength of 1.5 T and the MRI protocols are standardized using appropriate sequences to obtain good quality images with adequate resolution, lesion assessment and longitudinal monitoring can be performed robustly and independently of these confounding factors.

In challenging situations (e.g. low numbers of lesions and with confounding comorbidities) both the specific characteristics of each individual lesion as well as the overall patterns of lesions (e.g. symmetric central lesion in the pons and deep white matter lesions in ischaemic small-vessel disease) should be taken into account to support the diagnosis of multiple sclerosis or other conditions.

Emerging data suggest that advanced MRI sequences can enhance our ability to distinguish key, previously established characteristics of multiple sclerosis (e.g. cortical or perivenular lesions) that will enhance diagnosis because they are highly specific.

Although we focused the discussion on the 2017 revision of the McDonald criteria framework, the technical developments, combined with recent discoveries about the links between lesion characteristics and multiple sclerosis pathogenesis, will likely drive future improvements to—and perhaps even rethinking of—current criteria.


Watch the video: Μαγνητική Τομογραφία (September 2022).


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