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Am J Physiol Heart Circ Physiol 294: H2129-H2136, 2008. First published February 29, 2008; doi:10.1152/ajpheart.01399.2007
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Hyperemic flow heterogeneity within the calf, foot, and forearm measured with continuous arterial spin labeling MRI

Wen-Chau Wu,1,6 Jiongjiong Wang,1 John A. Detre,1,2 Felix W. Wehrli,1 Emile Mohler, 3rd,3 Sarah J. Ratcliffe,4 and Thomas F. Floyd2,5

Departments of 1Radiology, 2Neurology, 3Medicine, 4Biostatistics and Epidemiology, and 5Anesthesiology and Critical Care, The Hospital of University of Pennsylvania, Philadelphia, Pennsylvania; and 6Graduate Institute of Clinical Medicine, National Taiwan University, Taipei, Taiwan

Submitted 4 December 2007 ; accepted in final form 28 February 2008


   ABSTRACT
TOP
 ABSTRACT
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 GRANTS
 REFERENCES
 
Arterial spin labeling (ASL) is a noninvasive magnetic resonance imaging (MRI) technique for microvascular blood flow measurement. We used a continuous ASL scheme (CASL) to investigate the hyperemic flow difference between major muscle groups in human extremities. Twenty-four healthy subjects with no evidence of vascular disease were recruited. MRI was conducted on a 3.0 Tesla Siemens Trio whole body system with a transmit/receive knee coil. A nonmagnetic orthopedic tourniquet system was used to create a 5-min period of ischemia followed by a period of hyperemic flow (occlusion pressure = 250 mmHg). CASL imaging, lasting from 2 min before cuff inflation to 3 min after cuff deflation, was performed on the midcalf, midfoot, and midforearm in separate sessions from which blood flow was quantified with an effective temporal resolution of 16 s. When muscles in the same anatomic location were compared, hyperemic flow was found to be significantly higher in the compartments containing muscles known to have relatively higher slow-twitch type I fiber compositions, such as the soleus muscle in the calf and the extensors in the forearm. In the foot, the plantar flexors exhibited a slightly delayed hyperemic response relative to that of the dorsal compartment, but no between-group flow difference was observed. These results demonstrate that CASL is sensitive to flow heterogeneity between diverse muscle groups and that nonuniform hyperemic flow patterns following an ischemic paradigm correlate with relative fiber-type predominance.

skeletal muscle; magnetic resonance imaging

PREVIOUS STUDIES IN nondiseased skeletal muscle have shown that blood supply is not uniformly distributed between muscle groups during exercise (19, 21, 22). Variability in muscle fiber populations results in different metabolic (43) and microvascular profiles (14, 16, 46). These differences may impact muscle flow responses to ischemia and exercise. Muscles composed principally of highly oxidative red fibers are reported to have larger maximal flow responses after contraction than muscles with a relatively higher percentage of glycolytic white fibers (23, 27). Consequently, flow measurements made for a physiological and diagnostic purpose using whole limb dilution techniques are unable to resolve physiological or pathophysiological effects in specific muscle groups.

Measures of whole extremity blood flow have been achieved with plethysmography (38), thermodilution (29, 31), and Doppler ultrasound (42). An assessment of regional microvascular flow (or perfusion) is more complicated, however, and there remains no gold standard. Published methods include sestamibi (45), thallium (25), positron emission tomography (6, 32), contrast-enhanced magnetic resonance (MR) imaging (MRI) (26), Doppler ultrasound (5, 11), and optical methods (34). The ability to interrogate microvascular function and integrity may yield important physiological and clinical diagnostic information in various disease states including diabetes, atherosclerosis, myopathies, muscular dystrophies, and skeletal muscle tumors.

The measurement of skeletal muscle blood flow at rest is challenging because the resting flow rate is very low (<5 ml·100 g–1·min–1) (41) and below the threshold of detection for most measurement approaches. Furthermore, resting blood flow appears to be well preserved even in the presence of disease (6). Blood flow after exercise or following a period of ischemia, the hyperemic flow response, is severalfold higher than resting flow and is more easily measurable in both physiological and pathophysiological investigations (26, 35).

Arterial spin labeling (ASL) (9) is a noninvasive MRI technique for perfusion measurement and has been widely used in the brain. ASL makes use of the protons in arterial blood as endogenous tracers, which are magnetically labeled by radio frequency pulses' yielding quantitative measurements with temporal and spatial resolution on the order of seconds and millimeters, respectively. Few groups have adapted this technique to study the blood flow in skeletal muscle, and to date, studies investigating the spatial flow difference between muscle groups have been very limited (4, 12, 24). Hyperemic flow paradigms, when combined with ultrasound or the measurement of systolic pressure indexes, have been used extensively in the diagnosis of peripheral vascular disease in conduit vessels. The same paradigm combined with ASL may enable the diagnosis of early microvascular disease within muscle, such as occurs in diabetes mellitus. To further such work, it is important to characterize differences in microvascular hyperemic flow between muscle groups in a disease-free population.

In the present work, we tested the hypothesis that heterogeneity in hyperemic flow exists between large muscles or between functional muscle groups exposed to an ischemic stimulus using a continuous scheme for ASL (CASL). Specifically, we investigated the hyperemic flow difference between two well-studied calf muscles, the soleus (Sol) and gastrocnemius, considering that the former is composed of a relatively higher proportion of slow-oxidative red fibers, whereas the latter is populated with a greater fraction of fast-glycolytic white fibers (10, 13). We then used CASL to systematically study hyperemic perfusion and its differences between major muscle groups in the calf, foot, and forearm.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 APPENDIX
 GRANTS
 REFERENCES
 
MR experiments. Twenty-four healthy subjects (ages 25–49 yr; 11 women and 13 men) were scanned after Institutional Review Board approval and appropriate consent were obtained. None of the subjects had evidence of vascular disease as assessed by medical history and noninvasive vascular systolic pressure indexes or the San Diego Claudication Questionnaire. The subjects were instructed to avoid intense exercise 2 h before the experiment, and none had endurance or resistance training within 6 mo before the recruitment. Studies were conducted on a 3.0 Tesla Siemens Trio whole body MR system (Erlangen, Germany) with a custom-designed dual-tuned proton/phosphorous transmit/receive knee coil (Nova Medical, Wakefield, MA).

Imaging was performed on the midcalf (n = 19), midfoot (n = 9), and midforearm (n = 15) in separate sessions. The subjects were placed supine with the knee slightly flexed for studies of the midcalf and transmetatarsal. The subjects were placed prone in a swimming position for the midforearm imaging. Extremities were positioned within the coil and fixed in position with a VacFix vacuum-cushion bead bag (PAR Scientific A/S, Houston, TX). The midcalf, midforearm, and midfoot or transmetatarsal cross sections were the anatomic targets. For each anatomic location, the target slice was determined in two steps. Following a scout scan composed of three orthogonal image slices, a second scout scan was performed to obtain five axial slices. Among these, the image that best distinguished different muscle groups was chosen for functional perfusion imaging. Perfusion-weighted images were obtained using a single-slice version of the CASL sequence (2) with a single-shot gradient-echo echoplanar readout [field of view (FOV), 22 cm; in-plane matrix size, 64 x 64; slice thickness, 1 cm; repetition time (TR), 4 s; echo time (TE), 17 ms; and flip angle, 90°]. The labeling plane was 6 cm proximal to the imaging slice. The tagging duration equaled 2 s, and the postlabeling delay (PLD) equaled 1,900 ms. For the control scan, the inversion plane was placed 6 cm distal to the imaging slice to compensate for the magnetization transfer effect (2).

Considering that exercise may preferentially stimulate certain muscle groups and complicate flow comparisons between them, we elected to employ a brief ischemic-hyperemic paradigm to more uniformly challenge all muscle groups. This approach is used extensively in the diagnosis of vascular disease since it may also be applied to nonambulatory patients. Temporary occlusion was achieved with a Zimmer 1000 (Warsaw, IN) nonmagnetic orthopedic tourniquet system, with the cuff placed on the thigh or upper arm to create a 5-min period of ischemia followed by a period of hyperemic flow. The occlusion pressure was 250 mmHg. CASL imaging commenced 2 min before cuff inflation and ended 3 min after cuff deflation. A two-dimensional spoiled gradient-echo sequence (TR/TE, 50/3.4 ms; FOV, 220 mm; in-plane matrix size, 256 x 256; and flip angle, 50°; 4 averages) was then used to acquire a high-resolution anatomic image of the slice where CASL imaging was performed.

Data processing and analysis. Subject motion during the scan was retrospectively corrected using the built-in motion correction function of the scanner. Complex data were then reconstructed to yield magnitude images, which were exported to a workstation for analysis using Voxbo (http://www.voxbo.org/) and custom-programmed scripts in IDL (RSI, Boulder, CO). Signals for CASL were generated by the pairwise subtraction of tag and control images. After subtraction, two adjacent data points in time were averaged due to gradient cycling, resulting in an effective temporal resolution of 16 s. The CASL signals were then converted to quantitative flow following the model in reference (44), assuming T1 = 1.6 s and T2* = 100 ms for arterial blood at 3.0 Tesla.

The high-resolution spoiled gradient-echo anatomic image was used to hand draw regions of interest (ROIs) for six muscle groups in the calf and to define flexor and extensor ROIs within the forearm and foot (Fig. 1). ROIs in the calf included muscles within the anterior, lateral, deep posterior, and superficial posterior compartments. The anterior compartment (AC) contains extensor muscles including the tibialis anterior, extensor digitorum longus, extensor hallicus longus, and peroneus muscles. The lateral compartment (LC) contains the peroneus longus and peroneus brevis muscles. The deep posterior compartment (DPC) contains the flexor digitorum longus, tibialis posterior, flexor hallicus longus, and popliteus. The posterior compartment contains the Sol and medial and lateral gastrocnemius muscles. Muscles within each compartment of the leg were grouped together in the analysis except within the posterior compartment, where the Sol, medial (Gstrc-M), and lateral (Gstrc-L) gastrocnemius muscles could be easily discerned. Within the forearm, the forearm flexors (F-Flx) were analyzed as a group and included the flexor carpi radialis, flexor carpi ulnaris, flexor digitorum (superficial and profundus), flexor pollicis longus, and palmaris longus muscles. The forearm extensors (F-Ext) were also analyzed as a group and included the extensor carpi radialis, extensor carpi ulnaris, extensor digitorum, extensor digit minimi, and brachioradialis and abductor extensor pollicis and pollicis longus muscles. In the foot, the dorsal group (D-Ext) analyzed consisted primarily of the interosseus muscles. The plantar flexor (P-Flx) group analyzed, also in the foot, included the flexor digitorum brevis, abductor hallicus, abductor digiti minimi, flexor digiti minimi, flexor hallicus, quadratus plantae, and lumbricale muscles. Any vessels visually discernible on the anatomic image were excluded from analysis.


Figure 1
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  		Fig. 1. The 3 anatomic sites and regions of interest (ROIs) analyzed. The colored ROIs are superimposed upon high-resolution gradient echo images (axial slices). A: right midforearm: extensor (F-Ext; red) and flexor (F-Flx; purple). B: right transmetatarsal foot: plantar flexors (P-Flx; red) and dorsal extensors (D-Ext; purple). C: left midcalf: medial gastrocnemius (Gstrc-M; red) and lateral gastrocnemius (Gstrc-L; pink), soleus (Sol; blue), deep posterior compartment (DPC; yellow), and anterior (AC; light blue) and lateral (LC; green) compartments. D: the mean hyperemic flow image obtained at the slice shown in C.

 
For each muscle group, five indexes were computed, and their definitions are described as follows: 1) hyperemic period (in s): after cuff deflation, the period measured from the time when flow increases above the baseline (tbeg) to the time when flow returns to the baseline (tend). Here the baseline refers to the average flow before cuff inflation; 2) peak hyperemic flow (in ml·100 g–1·min–1): the peak flow observed during hyperemia; 3) mean hyperemic flow (in ml·100 g–1·min–1): the mean flow during the hyperemic period; 4) hyperemic flow volume (in ml/100 g): the integrated flow over the hyperemic period; and 5) time to peak (in s): measured from tbeg to the time when hyperemic flow peaks.

For flow calculation, we adopted a single-compartment model, assuming that the tagged spins arriving in the capillary bed did not diffuse into interstitial space by the time of data acquisition. To estimate the error from this approximation requires a priori knowledge of exchange time (Tex, the water molecules stay in the capillary bed before entering interstitial space), which is usually unknown. Previous studies have shown that water exchange between intra- and extravascular compartments accounts for a minor error relative to other factors such as transit delay and slice profile (7). The error originating from the different relaxation times in the two compartments has been reported with detailed numerical simulation and is beyond the scope of this study. Yet, to estimate the effect of different relaxation times between muscle groups on flow quantification, we conducted the calculation described below. The tagged water diffuses into extravascular space with a delay time after entering the capillary bed with a finite exchange rate. Therefore, a fraction of tags may reside in the vessel during image acquisition, whereas the rest exit the vascular and enter the tissue compartment. For simplicity, we assume that there are two discrete populations of tags; those exiting the vessel do so immediately after having entered the capillary bed, i.e., Tex = 0, yielding for the difference in magnetization between tag and control:

Formula 1(1)
where {Delta}Mex is the ASL signal from the extravascular compartment, M0b is the longitudinal magnetization of arterial blood at thermal equilibrium, {lambda} is the blood/tissue partition coefficient, {alpha} is the tagging efficiency, T1b and T1t are the longitudinal relaxation time of arterial blood and tissue, respectively. {tau} is the tagging duration, and {Delta}t is the transit delay from the tagging region to the imaging region.

For the second population of tags, Tex = infinite, i.e., they never leave the vascular compartment:

Formula 2(2)
where {Delta}Min is the ASL signal from the intravascular compartment. The overall ASL signal ({Delta}M) is the weighted sum of the signals from the two fractions, i.e.,

Formula 3(3)
where 0 less double equals {xi} less double equals 1. For a derivation of Eqs. 1 and 2, see Appendix.

For a given flow (fth), {Delta}M can be generated by Eqs. 1-3 and then used to calculate the flow based on the single compartment model (fcal). To evaluate the quantitative flow variability originating from the model and varying tissue T1 relaxation times (T1t), we conducted the calculation for the midcalf for a range of T1t (1,430–1,490 ms; according to our unpublished data, T1t = 1,492, 1,445, and 1,434 ms for the deep flexor, Sol muscle, and gastrocnemius, respectively) and reported the results as apparent flow (fapp):

Formula 4(4)
PLD = 2 s and {tau} = 2 s. We assumed that {Delta}t = 1 s, fth = 60 ml·100 g–1·min–1, and T1b = 1.6 s.

Statistical analysis. Flow measures between the extensor and flexor groups within the forearm and within the foot were tested for significant differences using repeated-measures ANOVA, where muscle type was used to define the repeated measurements for each subject. In the calf, a linear mixed-effects regression model (17) was conducted to test the heterogeneity between the six muscle groups. This method is similar to a repeated-measures ANOVA, with adjustment for the subject-dependent correlation between multiple measurements (random effects); however, all available measurements from each subject can be utilized in the analysis, not just the subjects with complete measurements on all muscle groups. The mixed-effects model was used to test the dependence of each flow parameter (peak flow, mean flow, etc.) on a muscle group (fixed effect), having adjusted for the subject-dependent correlation. Utilizing the repeated-measures design of this study for within-subject ROI comparisons afforded a more statistically powerful comparison of population ROI means than that obtained by standard ANOVA. Lastly, given the fact that muscle fiber populations have been well described for the gastrocnemius muscle, the regression model was constructed so that for each muscle group comparisons were made between that group and the gastrocnemius muscle reference group.


   RESULTS
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 APPENDIX
 GRANTS
 REFERENCES
 
Figure 1 depicts the mean hyperemic flow image (Fig. 1D) with ROIs for the midcalf shown in color (Fig. 1C). The relatively greater flow within the Sol is easily discernable. The flow measurements and results of the linear mixed model analysis are summarized in Tables 1 and 2, respectively. The muscle group was found to have a significant effect on the dependent variable peak flow (P < 0.0001), mean hyperemic flow (P < 0.0001), and hyperemic flow volume (P < 0.0001) but was not important in determining the time-to-peak (P = 0.76) or hyperemic (P = 0.74) period (not listed in GoTable 2). When individual muscle groups were addressed, DPC, Sol, and LC were consistently and significantly different from Gstrc-L in peak flow, mean hyperemic flow, and hyperemic flow volume measurements. The Sol and DPC regions had flow measurements considerably in excess of all other groups studied. Gstrc-M and Gstrc-L were not statistically different across any of the flow indexes measured. The flow measurements and results of repeated-measures ANOVA in the foot and forearm are summarized in Table 3. For the foot, only the time to peak was significantly different between muscle groups. In the forearm, there was a marked difference in flow between F-Flx and F-Ext muscle groups in all flow measurements except for time to peak.


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  		Table 1.
 

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  		Table 2.
 

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  		Table 3.
 
Figure 2 shows the temporal evolution of flow in the forearm and foot. In the forearm, peak flow, mean hyperemic flow, and hyperemic flow volume are significantly larger in the F-Ext. In the foot, the P-Flx exhibited a delayed hyperemic response compared with that of the D-Ext. Figure 3 shows the flow-time curves obtained from the more superficial muscles in the midcalf. A high similarity is found in the temporal evolution between the Gstrc-L and Gstrc-M and between the AC and LC regions. By comparison, deep muscles exhibit significantly higher peak hyperemic flow at ~100 ml·100 g–1·min–1 in the DPC and Sol muscle groups, as opposed to <80 ml·100 g–1·min–1 in the more superficial muscles (Fig. 4). It is also noted that the DPC has higher resting flow than other muscles and a slightly delayed hyperemic response. Slightly different peak flow values are shown in the figures and in Table 1 because time to peak varies between subjects.


Figure 2
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  		Fig. 2. Flow-time curves obtained from forearm (black, F-Ext; and gray, F-Flx; n = 15; A) and foot (black, D-Ext; and gray, P-Flx; n = 9; B). Error bars indicate the standard error of the subjects included. Thick black lines indicate the occlusion period. TR, repetition time.

 

Figure 3
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  		Fig. 3. Flow-time series obtained from midcalf (n = 19). A: gastrocnemius (black, Gstrc-M; and gray, Gstrc-L). B: superficial extensors (black, AC; and gray, LC). Error bars indicate the standard error across subjects. Thick black lines indicate the occlusion period.

 

Figure 4
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  		Fig. 4. Flow-time series comparison between 4 muscle groups in the calf (black, Gstrc-L; green, AC; blue, Sol; and red, DPC). The horizontal black bar indicates the occlusion period.

 
Figure 5 shows the numerical estimates of the variability due to T1 differences between muscle groups and the uncertainty in the degree of water extraction. By the use of a single compartment model (assuming blood to remain in the vascular compartment), flow is underestimated when the tagged water molecules are allowed to diffuse into the extravascular space. The T1-related quantitative variability increases with increased water extraction, reaching 5% in the most extreme case.


Figure 5
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  		Fig. 5. Simulated effects of heterogeneous longitudinal relaxation time (T1) between muscle groups on flow calculation.

 

   DISCUSSION
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
APPENDIX
 GRANTS
 REFERENCES
 
Few investigations have employed ASL to study skeletal muscle perfusion (4, 12, 24), and despite evidence suggesting nonuniformity of blood supply (37, 43), little attention has been paid to the heterogeneity between muscle groups, especially following an ischemic paradigm. The results of this study suggest that there is significant heterogeneity between muscle groups in the hyperemic response to limb ischemia as measured by ASL.

The accuracy of ASL-based perfusion measurements principally relies on two factors. One is the PLD, the time between spin labeling and data acquisition. If the PLD is too short, the label remaining in large vessels leads to regional flow overestimation, whereas the signal-to-noise ratio can be compromised if the PLD is too long. Our previous study suggested that arterial transit times in the skeletal muscle may be prolonged and that venous outflow of unextracted label during hyperemia occurs (48). These confounds can be minimized by using a relatively long PLD (1,900 ms) to maximize inflow along with a relatively long TR (4 s) to allow unextracted label to fully decay. These parameters were employed in the present study.

The calculated flow rates further depend on the relaxation times (T1 and T2*) used in the quantitative model. To distinguish the flow heterogeneity between muscle groups, the quantitative variability, as a consequence of T1 and T2* discrepancy, must be smaller than the actual flow variability between muscles. To estimate whether this condition is met, we conducted computer simulations to assess the maximum T1-related flow variability. As shown in Fig. 5, T1 variations in muscle groups are responsible for 5% variability if all tagged spins diffuse into the extravascular space. On the other hand, T2* variations ranging from 25 to 33 ms can cause errors as large as 10% (unpublished data). With the consideration that the vascular-extravascular exchange rate in skeletal muscle lies between 1 and 3 s–1 (33), it is reasonable to expect the T1/T2* related quantitative flow variability to be <10%. As a result, the observed differences in hyperemic flow rates between Sol, deep flexor, and superficial muscles as shown in Fig. 4 are unlikely to be due to differences in relaxation parameters between muscle groups. The description, however, does assume that the water extraction rate does not vary among muscle groups (Fig. 5), which is probably reasonable. Under these conditions, even though the single compartment model can underestimate flow by as much as 30%, such an error would not affect the relative flow differences between muscle groups.

Previous studies have suggested that the capillary supply to skeletal muscle is determined by its metabolic profile, oxidative capacity, and fiber size (40, 43) and that feeding artery reactivity may be greater with type I than type II muscle (47, 49), although the difference in capillary supply to slow- and fast-twitch fibers is less distinct in humans than in other mammals (36). Our data in the midcalf show that the Sol muscle has a significantly higher hyperemic flow response to a 5-min occlusion than the more superficial muscles, including the gastrocnemius. This observation is in agreement with another recent MR study comparing Sol and gastrocnemius muscles (26) and can be attributed to the fact that the Sol is composed of ~70–80% slow-twitch type I fibers with a higher capillarity and oxidative capacity, whereas the gastrocnemius is more evenly composed of type I (50–60%) and type II fibers (10, 13). The flow pattern in the gastrocnemius muscle is less distinct from AC and LC than from the Sol muscle. Gregory et al. (15) reported that the percentage of type I fibers is similar in the anterior tibialis and Sol, whereas the succinic dehydrogenase activity is significantly higher in the Sol, which suggests that muscle oxidative capacity may also contribute to the flow difference between muscles. The DPC in the midcalf also exhibits a peak hyperemic flow comparable with that in the Sol muscle and a resting flow significantly higher than the value in other muscle groups (~40 ml·100 ml–1·min–1 vs. undetectable). We have not found documentation in the literature of the fiber type distribution and physiological/metabolic characteristics of the deep flexor. Similar perfusion patterns of high resting flow and high exercise hyperemia were reported in the deep muscles in rats (22), although a direct comparison with our measurements is not possible since the muscle fiber is extremely stratified in rats (8), whereas in humans muscle fibers exhibit a distribution between muscles. In a study of miniature pigs with a fiber distribution similar to that in humans, high post-exercise flow was found in muscles with a high composition of oxidative slow-twitch fibers (3). In Fig. 1, one notices more large vessels passing through the deep flexor. These vessels have been carefully excluded from our data analysis, although partial volume effects could be responsible for some residual intravascular contamination. Overall, our data suggest that the fiber composition in the DPC group may be more similar to that in the Sol muscle.

In the forearm, the F-Ext was found to have a significantly larger hyperemic response than that of the F-Flx. Data from the literature regarding the differences in fiber type between muscles in the forearm are scant. A primate study documented a slightly higher proportion of type I fibers in the extensor communis, brachioradialis, extensor carpi ulnaris, and flexor carpi radialis muscles (28). Although the ulnar head of the flexor carpi ulnaris is composed of predominantly type II fibers (71% vs. 27% type I fibers), the humeral head contains a larger proportion of type I fibers (58% vs. 40% type II fibers). In the foot, no significant flow difference between the D-Ext and the P-Flx muscle groups was observed, and we have found little supportive fiber composition data that would allow us to differentiate these muscle groups. Nevertheless, the substantial difference in the onset and recovery of postischemic hyperemia between D-Ext and P-Flx suggests that the differential involvement of these muscles might be observed in patients with small vessel disease such as diabetes.

The peak hyperemic flow measured in our study (80–140 ml·100 g–1·min–1 at large) is in good agreement with the measurements (123 ± 42 ml·100 g–1·min–1) of Lebon et al. (24) using a similar paradigm. Furthermore, we were able to measure the hyperemic flow across multiple muscle groups and demonstrated differences in hyperemic flow responses, which appear to correlate with existing data on differences in fiber composition between these groups. In contrast, Frank et al. (12) reported no clear pattern of flow heterogeneity among muscle groups in their study comprising only four subjects. The discrepancy is likely due to the employment of a cuff occlusion in our work versus a plantar-flexion exercise paradigm in the design employed by Frank et al. (12). As mentioned previously, exercise is likely to stimulate specific muscle groups, which may introduce several confounds such as the different relationship between O2 supply and muscle oxidative capacity in trained and untrained individuals (20, 30).

One concern regarding the present study is the temporal evolution of relaxation times and flow-dependent water extraction. Although the mechanism is not fully understood, T2 (T2*) changes have been shown to correlate with variability in the water exchange between compartments, presumably driven by the increased metabolism and the consequent increase in tissue osmolarity and intracellular water. In the study of Frank et al. (12), ASL was used to measure the hyperemia in the midcalf after rigorous exercise. The authors suggested that the errors in the estimates of T1, T2, and Tex of as much as 20% tend to cancel each other out, yielding a worst case estimated error of 12% (12). Also of interest, radioactive tracer studies suggest that a significant fraction of labeled water is actually shunted by vessels and unavailable for extraction (18, 39). The degree to which this may be exacerbated by a hyperemic paradigm is not known. To improve the accuracy of ASL measurement in skeletal muscle, further investigation of the dynamics of water extraction and Tex is necessary. Another factor that could potentially confound data interpretation in this study is the lack of histological correlation, since muscle biopsy was not included in our protocol. Although the distribution of muscle fibers may vary between individuals, previous studies (1) showed that training could cause a temporary alteration between type IIa and type IIb fibers, whereas the percentage of type I and type II fibers remains unchanged. The latter effect should be absent or minor because we only recruited subjects who did not undergo training within 6 mo. Individual variability is expected to reduce the statistical significance without altering the results and conclusions since the composition ratio between type I and type II fibers is very unlikely to change significantly.

In summary, we have shown that CASL can be employed to noninvasively measure flow heterogeneity between diverse muscle groups. Furthermore, we have demonstrated that flow patterns following an ischemic paradigm correlate with relative fiber-type dominance. The application of this technique and these findings may be of value in further physiological and pathophysiological studies.


   APPENDIX
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
GRANTS
 REFERENCES
 
The signal difference between control and tag images, {Delta}M, can be expressed as

Formula A1(A1)
where {alpha} is the tagging efficiency, M0b is the longitudinal magnetization of arterial blood at thermal equilibrium, {lambda} is the blood/tissue partition coefficient, f is blood flow, and {otimes} denotes convolution. C(t) is the normalized arterial concentration of magnetization arriving at the voxel at time t. R(t) is the residue function that describes the fraction of tagged water remaining in the voxel at a time t after the arrival. For CASL,

Formula A2(A2)
where {Delta}t is the transit delay, T1b is the longitudinal relaxation time of arterial blood, and {tau} is the tagging duration. For tags that enter the interstitial space immediately after entering the capillary bed, the longitudinal relaxation of tags is dictated by the T1 of tissue (T1t), i.e.,

Formula A3(A3)

By applying Eqs. A2 and A3 to Eq. A1, we get {Delta}M for the extravascular component of ASL signal ({Delta}Mex) after a PLD:

Formula A4(A4)

Similarly, the intravascular component of the ASL signal ({Delta}Min) can be obtained by replacing Eq. A3 by Eq. A5

Formula A5(A5)
in which the longitudinal relaxation of tags follows T1b, leading to

Formula A6(A6)


   GRANTS
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 GRANTS
REFERENCES
 
This study was supported by the National Heart, Lung, and Blood Institute Grant 5-R01-HL-075649-05 and the National Center for Research Resources Grant 5P41-RR002305.


   FOOTNOTES
 

Address for reprint requests and other correspondence: T. F. Floyd, Dept. of Anesthesiology and Critical Care, The Hospital of Univ. of Pennsylvania, 3400 Spruce St., Philadelphia, PA 19104 (e-mail: Thomas.Floyd{at}uphs.upenn.edu)

The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.


   REFERENCES
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 GRANTS
 REFERENCES
 

  1. Adams GR, Hather BM, Baldwin KM, Dudley GA. Skeletal muscle myosin heavy chain composition and resistance training. J Appl Physiol 74: 911–915, 1993.[Abstract/Free Full Text]
  2. Alsop DC, Detre JA. Reduced transit-time sensitivity in noninvasive magnetic resonance imaging of human cerebral blood flow. J Cereb Blood Flow Metab 16: 1236–1249, 1996.[CrossRef][Web of Science][Medline]
  3. Armstrong RB, Delp MD, Goljan EF, Laughlin MH. Progressive elevations in muscle blood flow during prolonged exercise in swine. J Appl Physiol 63: 285–291, 1987.[Abstract/Free Full Text]
  4. Boss A, Martirosian P, Claussen CD, Schick F. Quantitative ASL muscle perfusion imaging using a FAIR-TrueFISP technique at 3.0 T. NMR Biomed 19: 125–132, 2006.[CrossRef][Web of Science][Medline]
  5. Bragadeesh T, Sari I, Pascotto M, Micari A, Kaul S, Lindner JR. Detection of peripheral vascular stenosis by assessing skeletal muscle flow reserve. J Am Coll Cardiol 45: 780–785, 2005.[Abstract/Free Full Text]
  6. Burchert W, Schellong S, van den Hoff J, Meyer GJ, Alexander K, Hundeshagen H. Oxygen-15-water PET assessment of muscular blood flow in peripheral vascular disease. J Nucl Med 38: 93–98, 1997.[Abstract/Free Full Text]
  7. Buxton R. Introduction to functional magnetic resonance imaging. New York: Cambridge Univ. Press, 2002, p. 377–378.
  8. Delp MD, Duan C. Composition and size of type I, IIA, IID/X, and IIB fibers and citrate synthase activity of rat muscle. J Appl Physiol 80: 261–270, 1996.[Abstract/Free Full Text]
  9. Detre JA, Leigh JS, Williams DS, Koretsky AP. Perfusion imaging. Magn Reson Med 23: 37–45, 1992.[Web of Science][Medline]
  10. Edstrom L, Kugelberg E. Histochemical mapping of motor units in experimentally re-innervated skeletal muscle. Experientia 25: 1044–1045, 1969.[CrossRef][Web of Science][Medline]
  11. Fleischer AC, Niermann KJ, Donnelly EF, Yankeelov TE, Canniff KM, Hallahan DE, Rothenberg ME. Sonographic depiction of microvessel perfusion: principles and potential. J Ultrasound Med 23: 1499–1506, 2004.[Abstract/Free Full Text]
  12. Frank LR, Wong EC, Haseler LJ, Buxton RB. Dynamic imaging of perfusion in human skeletal muscle during exercise with arterial spin labeling. Magn Reson Med 42: 258–267, 1999.[CrossRef][Web of Science][Medline]
  13. Gollnick PD, Sjodin B, Karlsson J, Jansson E, Saltin B. Human soleus muscle: a comparison of fiber composition and enzyme activities with other leg muscles. Pflügers Arch 348: 247–255, 1974.[CrossRef][Web of Science][Medline]
  14. Gray SD, Renkin EM. Microvascular supply in relation to fiber metabolic type in mixed skeletal muscles on rabbits. Microvasc Res 16: 406–425, 1978.[CrossRef][Web of Science][Medline]
  15. Gregory CM, Vandenborne K, Dudley GA. Metabolic enzymes and phenotypic expression among human locomotor muscles. Muscle Nerve 24: 387–393, 2001.[CrossRef][Web of Science][Medline]
  16. Groom AC, Ellis CG, Wrigley SJ, Potter RF. Capillary network morphology and capillary flow. Int J Microcirc Clin Exp 15: 223–230, 1995.[Web of Science][Medline]
  17. Gueorguieva R, Krystal JH. Move over ANOVA: progress in analyzing repeated-measures data and its reflection in papers published in the Archives of General Psychiatry. Arch Gen Psychiatry 61: 310–317, 2004.[Abstract/Free Full Text]
  18. Johnson JA, Cavert HM, Lifson N. Kinetics concerned with distribution of isotopic water in isolated perfused dog heart and skeletal muscle. Am J Physiol 171: 687–693, 1952.[Free Full Text]
  19. Kalliokoski KK, Kemppainen J, Larmola K, Takala TO, Peltoniemi P, Oksanen A, Ruotsalainen U, Cobelli C, Knuuti J, Nuutila P. Muscle blood flow and flow heterogeneity during exercise studied with positron emission tomography in humans. Eur J Appl Physiol 83: 395–401, 2000.[CrossRef][Web of Science][Medline]
  20. Knight DR, Schaffartzik W, Poole DC, Hogan MC, Bebout DE, Wagner PD. Effects of hyperoxia on maximal leg O2 supply and utilization in men. J Appl Physiol 75: 2586–2594, 1993.[Abstract/Free Full Text]
  21. Laaksonen MS, Kalliokoski KK, Kyrolainen H, Kemppainen J, Teras M, Sipila H, Nuutila P, Knuuti J. Skeletal muscle blood flow and flow heterogeneity during dynamic and isometric exercise in humans. Am J Physiol Heart Circ Physiol 284: H979–H986, 2003.[Abstract/Free Full Text]
  22. Laughlin MH, Armstrong RB. Muscular blood flow distribution patterns as a function of running speed in rats. Am J Physiol Heart Circ Physiol 243: H296–H306, 1982.[Abstract/Free Full Text]
  23. Laughlin MH, Armstrong RB. Rat muscle blood flows as a function of time during prolonged slow treadmill exercise. Am J Physiol Heart Circ Physiol 244: H814–H824, 1983.[Abstract/Free Full Text]
  24. Lebon V, Carlier PG, Brillault-Salvat C, Leroy-Willig A. Simultaneous measurement of perfusion and oxygenation changes using a multiple gradient-echo sequence: application to human muscle study. Magn Reson Imaging 16: 721–729, 1998.[CrossRef][Web of Science][Medline]
  25. Lin CC, Ding HJ, Chen YW, Huang WT, Kao A. Usefulness of thallium-201 muscle perfusion scan to investigate perfusion reserve in the lower limbs of Type 2 diabetic patients. J Diabetes Complications 18: 233–236, 2004.[CrossRef][Web of Science][Medline]
  26. Lutz AM, Weishaupt D, Amann-Vesti BR, Pfammatter T, Goepfert K, Marincek B, Nanz D. Assessment of skeletal muscle perfusion by contrast medium first-pass magnetic resonance imaging: technical feasibility and preliminary experience in healthy volunteers. J Magn Reson Imaging 20: 111–121, 2004.[CrossRef][Web of Science][Medline]
  27. McDonough P, Behnke BJ, Padilla DJ, Musch TI, Poole DC. Control of microvascular oxygen pressures in rat muscles comprised of different fibre types. J Physiol 563: 903–913, 2005.[Abstract/Free Full Text]
  28. McIntosh JS, Ringqvist M, Schmidt EM. Fiber type composition of monkey forearm muscle. Anat Rec 211: 403–409, 1985.[CrossRef][Medline]
  29. Poole DC, Gaesser GA, Hogan MC, Knight DR, Wagner PD. Pulmonary and leg VO2 during submaximal exercise: implications for muscular efficiency. J Appl Physiol 72: 805–810, 1992.[Abstract/Free Full Text]
  30. Richardson RS, Noyszewski EA, Kendrick KF, Leigh JS, Wagner PD. Myoglobin O2 desaturation during exercise. Evidence of limited O2 transport. J Clin Invest 96: 1916–1926, 1995.[Web of Science][Medline]
  31. Richardson RS, Poole DC, Knight DR, Kurdak SS, Hogan MC, Grassi B, Johnson EC, Kendrick KF, Erickson BK, Wagner PD. High muscle blood flow in man: is maximal O2 extraction compromised? J Appl Physiol 75: 1911–1916, 1993.[Abstract/Free Full Text]
  32. Ruotsalainen U, Raitakari M, Nuutila P, Oikonen V, Sipila H, Teras M, Knuuti MJ, Bloomfield PM, Iida H. Quantitative blood flow measurement of skeletal muscle using oxygen-15-water and PET. J Nucl Med 38: 314–319, 1997.[Abstract/Free Full Text]
  33. Saab G, Thompson RT, Marsh GD, Picot PA, Moran GR. Two-dimensional time correlation relaxometry of skeletal muscle in vivo at 3 Tesla. Magn Reson Med 46: 1093–1098, 2001.[CrossRef][Web of Science][Medline]
  34. Sandberg M, Zhang Q, Styf J, Gerdle B, Lindberg LG. Non-invasive monitoring of muscle blood perfusion by photoplethysmography: evaluation of a new application. Acta Physiol Scand 183: 335–343, 2005.[CrossRef][Web of Science][Medline]
  35. Schmidt MA, Chakrabarti A, Shamim-Uzzaman Q, Kaciroti N, Koeppe RA, Rajagopalan S. Calf flow reserve with H215O PET as a quantifiable index of lower extremity flow. J Nucl Med 44: 915–919, 2003.[Abstract/Free Full Text]
  36. Sjogaard G. Capillary supply and cross-sectional area of slow and fast twitch muscle fibres in man. Histochemistry 76: 547–555, 1982.[CrossRef][Web of Science][Medline]
  37. Stamler JS, Meissner G. Physiology of nitric oxide in skeletal muscle. Physiol Rev 81: 209–237, 2001.[Abstract/Free Full Text]
  38. Thijssen DH, Bleeker MW, Smits P, Hopman MT. Reproducibility of blood flow and post-occlusive reactive hyperaemia as measured by venous occlusion plethysmography. Clin Sci (Lond) 108: 151–157, 2005.[Medline]
  39. Thompson AM, Cavert HM, Lifson N, Evans RL. Regional tissue uptake of D2O in perfused organs: rat liver, dog heart and gastrocnemius. Am J Physiol 197: 897–902, 1959.[Abstract/Free Full Text]
  40. Toussaint JF, Kwong KK, M'Kparu F, Weisskoff RM, LaRaia PJ, Kantor HL. Interrelationship of oxidative metabolism and local perfusion demonstrated by NMR in human skeletal muscle. J Appl Physiol 81: 2221–2228, 1996.[Abstract/Free Full Text]
  41. Utriainen T, Nuutila P, Takala T, Vicini P, Ruotsalainen U, Ronnemaa T, Tolvanen T, Raitakari M, Haaparanta M, Kirvela O, Cobelli C, Yki-Jarvinen H. Intact insulin stimulation of skeletal muscle blood flow, its heterogeneity and redistribution, but not of glucose uptake in non-insulin-dependent diabetes mellitus. J Clin Invest 100: 777–785, 1997.[Web of Science][Medline]
  42. Van Leeuwen BE, Lubbers J, Barendsen GJ, de Pater L. Calf blood flow and posture: Doppler ultrasound calibrated by plethysmography. J Appl Physiol 72: 1668–1674, 1992.[Abstract/Free Full Text]
  43. Vandenborne K, Walter G, Ploutz-Snyder L, Staron R, Fry A, De Meirleir K, Dudley GA, Leigh JS. Energy-rich phosphates in slow and fast human skeletal muscle. Am J Physiol Cell Physiol 268: C869–C876, 1995.[Abstract/Free Full Text]
  44. Wang J, Zhang Y, Wolf RL, Roc AC, Alsop DC, Detre JA. Amplitude-modulated continuous arterial spin-labeling 3.0-T perfusion MR imaging with a single coil: feasibility study. Radiology 235: 218–228, 2005.[Abstract/Free Full Text]
  45. Wann LS, Hellman C, Dorros G. Evaluation of leg perfusion during exercise using technetium 99m sestamibi. A new test for peripheral vascular disease. Echocardiography 9: 547–552, 1992.[Web of Science][Medline]
  46. Weber MA, Krakowski-Roosen H, Delorme S, Renk H, Krix M, Millies J, Kinscherf R, Kunkele A, Kauczor HU, Hildebrandt W. Relationship of skeletal muscle perfusion measured by contrast-enhanced ultrasonography to histologic microvascular density. J Ultrasound Med 25: 583–591, 2006.[Abstract/Free Full Text]
  47. Williams DA, Segal SS. Feed artery role in blood flow control to rat hindlimb skeletal muscles. J Physiol 463: 631–646, 1993.[Abstract/Free Full Text]
  48. Wu WC, Wang J, Detre JA, Ratcliffe SJ, Floyd TF. Transit delay and flow quantification in muscle with continuous arterial spin labeling perfusion MRI. J Magn Reson Imaging. In Press.
  49. Wunsch SA, Muller-Delp J, Delp MD. Time course of vasodilatory responses in skeletal muscle arterioles: role in hyperemia at onset of exercise. Am J Physiol Heart Circ Physiol 279: H1715–H1723, 2000.[Abstract/Free Full Text]



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W.-C. Wu, E. Mohler III, S. J. Ratcliffe, F. W. Wehrli, J. A. Detre, and T. F. Floyd
Skeletal Muscle Microvascular Flow in Progressive Peripheral Artery Disease: Assessment With Continuous Arterial Spin-Labeling Perfusion Magnetic Resonance Imaging
J. Am. Coll. Cardiol., June 23, 2009; 53(25): 2372 - 2377.
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