REGIONAL CEREBRAL BLOOD FLOW (RCBF) CALCULATIONS IN AWAKE, BEHAVING NON-HUMAN PRIMATES USING CONTINUOUS ARTERIAL SPIN LABELING (CASL) TECHNIQUES

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1 REGIONAL CEREBRAL BLOOD FLOW (RCBF) CALCULATIONS IN AWAKE, BEHAVING NON-HUMAN PRIMATES USING CONTINUOUS ARTERIAL SPIN LABELING (CASL) TECHNIQUES by RAJIV G MENON DONALD B TWIEG, Ph. D., CHAIR EDWARD G WALSH, Ph. D. PAUL D GAMLIN, Ph. D. A THESIS Submitted to the graduate faculty of The University of Alabama at Birmingham, in partial fulfillment of the requirements for the degree of Master of Science in Biomedical Engineering (MSBME) BIRMINGHAM, ALABAMA 2007

2 REGIONAL CEREBRAL BLOOD FLOW (RCBF) CALCULATIONS IN AWAKE, BEHAVING NON-HUMAN PRIMATES USING CONTINUOUS ARTERIAL SPIN LABELING (CASL) TECHNIQUES RAJIV G MENON MASTER OF SCIENCE IN BIOMEDICAL ENGINEERING (M.S.B.M.E.) ABSTRACT MRI can be employed to measure blood flow changes, which is an important index of tissue health and function. One of the methods used to quantify perfusion is Arterial Spin Labeling (ASL) - a non-invasive method that uses arterial blood water as an endogenous contrast agent. The aim of this study was to quantify cerebral blood perfusion in awake, behaving non-human primates by using a class of techniques called Continuous Arterial Spin Labeling (CASL). This study involved the experimental design, setup and post-processing for the measurement of perfusion to facilitate validation studies in animals and compare them with existing perfusion estimates in humans. The CASL sequence was implemented and perfusion was quantified in awake, behaving non-human primates. Optimal parameters determined for perfusion quantification included a labeling power = 2W and post labeling delay=0.5s. A labeling efficiency of 88% and a labeling time of 2s were used. The fractional signal difference in gray matter (GM) was 2.52% and in white matter (WM) was 1.11%. The average quantified GM CBF was ± 5.42 ml/100g/min and WM CBF was ± 4.62 ml/100g/min. GM/WM ratio was 2.35 ± An analysis of the data suggests that functional studies in non human primates using CASL may be feasible. The results of this study will help in better understanding ii

3 quantitative perfusion estimates in awake, behaving non-human primates and how they compare with corresponding cerebral blood flow (CBF) values in humans. iii

4 DEDICATION To my parents, who have always been constant sources of inspiration iv

5 ACKNOWLEDGMENTS This work is the result of inputs, technical advice and moral support from a number of people I would like to gratefully acknowledge here. Foremost, I would like to thank my committee members. Dr. Don Twieg for being very supportive, a great mentor and guide to me, Dr. Ed Walsh for providing technical support and advice whenever I needed it and Dr. Paul Gamlin for all the encouragement and support through the project. My labmates have been equally helpful with my thesis work. I would like to gratefully thank Matt Ward for all great patience and help with the monkey experiments. Mark Bolding and Dr. Jon Grossman have been of great technical help; I have learnt a great deal from them. I would also like to acknowledge Hrishikesh Deshpande with all the help with the phantom experiments. I would like to acknowledge my unofficial mentor Dr. N. Shastry Akella, who offered practical advice when I needed it the most. I d like to thank all my friends including Mandar, Girish, Rahul and everyone who have provided great moral support. Finally and most importantly I would like to acknowledge the love and support from my Parents and my sister, Dr. Sarita Menon. They continue to be beacons of inspiration for me. v

6 TABLE OF CONTENTS Page ABSTRACT... ii DEDICATION... iv ACKNOWLEDGMENTS...v LIST OF TABLES... viii LIST OF FIGURES... ix LIST OF ABBREVIATIONS...x CHAPTER 1. INTRODUCTION LITERATURE REVIEW...3 Flow Induced Adiabatic Inversion...4 Continuous Arterial Spin Labeling Techniques...5 Basic Theory...5 Transit Time and Magnetization Transfer Effects...7 Intravascular Signal...10 Other Spin Labeling Quantification Issues...10 CBF Changes as a Tool for Functional Imaging Studies MATERIALS AND METHODS...12 Phantom Studies...13 Experiment Design for Flow Phantom Study...13 Calculating Inversion Efficiency (α) of the CASL Sequence...15 Non-Human Primate Studies...16 Data Processing...17 vi

7 4. RESULTS...18 Labeling Efficiency...18 rcbf Quantification...20 MT Bias Correction...21 Quantification DISCUSSION...25 Potential Errors in Quantified CBF Values...25 rcbf Comparison...26 Future Directions...27 Separate Labeling Coil...27 Feasibility for Functional Studies...27 PARSE CONCLUSION...29 LIST OF REFERENCES...30 APPENDICES A IACUC Approval Form...33 vii

8 LIST OF TABLES Tables Page 1 Gray Matter (GM) and White Matter (WM) Voxel Values in the ROI shown in Fig Temporal Standard Deviation varies with the number of time points per block of data (N)...23 viii

9 LIST OF FIGURES Figures Page 1 Perfusion Imaging Sequence Flow Phantom Setup Scout Images Showing the Position of the Imaged, Label and Control Planes B1 Power Variation to Determine Optimum Inversion Efficiency Inversion Efficiency Vs Labeling Efficiency B1 at Different Flow Velocity Rates Dependence of Inversion Efficiency α on Adiabaticity Factor β Perfusion Difference Images Quantified rcbf Maps Single Voxel Time Courses for a GM and WM Pixel After Magnetization Transfer Correction...23 ix

10 LIST OF ABBREVIATIONS MRI CASL ASL MT DAI CBF PET SPECT SNR AFP PASL RF EPI SAR ID TR TE FOV Magnetic Resonance Imaging Continuous Arterial Spin Labeling Arterial Spin Labeling Magnetization Transfer Double Adiabatic Inversion Cerebral Blood Flow Positron Emission Tomography Single Photon Emission Computer Tomography Signal to Noise Ratio Adiabatic Fast Passage Pulsed Arterial Spin Labeling Radio Frequency Echo Planar Imaging Specific Absorption Rate Internal Diameter Repitition Time Echo Time Field of View SPM5 Statistical Parametric Mapping Software Version 5 x

11 ROI GM WM rcbf fmri TSD BOLD PARSE Region of Interest Gray Matter White Matter Regional Cerebral Blood Flow Functional Magnetic Resonance Imaging Temporal Standard Deviation Blood Oxygenation Level Dependent Parameter Assessment by Retrieval from Signal Encoding xi

12 CHAPTER 1 INTRODUCTION The measurement of tissue blood perfusion using MRI techniques has evolved rapidly in the past decade. One of the techniques that developed, Continuous Arterial Spin Labeling (CASL), provides completely non-invasive measurement of cerebral blood flow by using arterial blood water as an endogenous tracer. These methods can now be used to quantitatively assess tissue perfusion in neuropathological states 1,2 and for functional activation 3 in humans. The original ASL method that was implemented by Williams, et al, 4,5 used a continuous flow driven adiabatic inversion scheme. In this method, an RF pulse was continuously applied at a proximal slice for 2-3 seconds in the presence of a field gradient, to continuously invert the proton spins of the inflowing blood. The image obtained from labeling the proximal slice is called the label image. A separate control image is acquired by applying the same inversion to a symmetric distal slice. Subtracting the label image from the control image gives the perfusion weighted image. Magnetization transfer (MT) effects which caused the saturation of macromolecular spins 6,7, were then accounted for by the double adiabatic inversion (DAI) 8, and two coil methods 9,10 for multi-slice CASL studies. Absolute quantification of perfusion requires the measurement or assumption of several parameters such as T1, labeling efficiency (α), the blood brain partition coefficient (λ), and the transit time (δ). Quantification in the original model was based on a modification of the Bloch equations that accounted for flow effects and assumed water to be a freely diffusible tracer. Buxton, et al 11 proposed a general kinetic model, which 1

13 assumed a multi-compartment diffusion exchange of the labeled proton spins. Later models took into account effects of post-labeling delay 12, labeling time (τ) 13, T2* decay effects 14, and capillary wall permeability 15. Cerebral blood flow quantification is confounded by natural physiological variations, sensitivity to input parameters and the choice of quantification model, which has resulted in a large variation (up to 42%) in the calculated rcbf values of gray matter among the different models 16. Perfusion can be quantified reliably in small animal models like rats using radioactive microspheres 17. Perfusion estimates in humans using the different MRI techniques showed that the gray matter rcbf values varied between 59 to 99 ml/100g/min 18. Recently, quantitative studies in anesthetized rhesus monkeys estimated basal CBF in the gray matter to be 104 ml/100g/min 19. However, accurate measurement of basal CBF in awake, behaving primates is required for the quantitative interrogation of blood flow changes related to function. The aim of this study is to try to establish baseline perfusion estimates in awake, behaving macaque monkeys, and to explore the feasibility of functional studies by measuring blood flow changes in awake, behaving non-human primates. These studies will help in validating baseline CBF values in non-human primates as compared to those in humans. 2

14 CHAPTER 2 LITERATURE REVIEW Cerebral blood perfusion can be estimated with MRI, PET or SPECT methods, but each of these methods have their own shortcomings, with varying degrees of absolute quantification and spatial resolution. These methods typically involve injection of an exogenous tracer used as a contrast agent into the blood stream. While a gold standard for perfusion measurement is still elusive, an attractive option that has been recently developed using MRI is called the Arterial Spin Labeling (ASL) technique. This is a completely non-invasive technique, which uses arterial blood water as an endogenous contrast agent. Measurement of perfusion using ASL requires tagging of the arterial blood water proton spins 4. The tagging scheme may involve saturation 4 or inversion 5 of the proton spins. The latter has largely replaced the former, since it offers better signal to noise ratio (SNR) in the difference image. Inversion of flowing spins is achieved using flow induced adiabatic inversion using the principle of adiabatic fast passage (AFP) 20, wherein the inflowing arterial blood water can be tagged effectively without affecting the static tissue signal. Quantitative perfusion maps can then be generated by the knowledge of the perfusion difference signal 11 and a few other parameters. ASL techniques are further sub-divided into two distinct groups, namely Continuous Arterial Spin Labeling (CASL) 5 techniques and Pulsed Arterial Spin Labeling (PASL) 21 techniques. This chapter reviews the theory and literature for flow induced adiabatic inversion and CASL techniques. 3

15 Flow Induced Adiabatic Inversion Adiabatic inversion pulses work on the principle of adiabatic fast passage 20. AFP implies that the magnetization M will follow the direction of effective magnetic field B eff that the direction of the effective magnetic field does not change significantly during one period of precession of the magnetization about the effective field 20. An RF pulse can produce flow-induced adiabatic inversion 20 if a magnetic field gradient is applied concurrently with the RF pulse. This adiabatic inversion pulse affects flowing spins only, while the static signal remains unaffected by the off-resonance RF pulse 5. The frequency offset of the spins relative to the RF carrier frequency is 20 : (1) where = off-resonance frequency offset = gyromagnetic ratio G = magnetic field gradient applied along the direction of flow = resonance RF frequency = remote location of spin flowing towards and then away from it = tagging plane location This frequency offset corresponds to a z-component of the effective magnetic field eff defined as 20 : (2) where we assume that the RF pulse B 1 (t)=ae -iω rf t is applied along the x axis in a B1 rotating reference frame with a frequency of. Under the adiabatic condition, the inflowing spins are inverted according to Eq. (3) 5 : 4

16 (3) where T 1b is the longitudinal relaxation time of the arterial blood. If any side of the above inequality is violated the adiabatic condition is not satisfied and inversion does not occur. The stationary spins at the tagging location do not satisfy the adiabatic condition and typically experience a very large flip angle, whereas the stationary spins in the imaged slice are virtually unaffected because the tagging pulse is off-resonance and has a narrow bandwidth 5. However, MT effects are introduced at the imaged slice that must be adequately compensated for during perfusion quantification 22. Continuous Arterial Spin Labeling Techniques Basic Theory The first ASL technique was proposed by Detre, et al. 4 and was called Continuous Arterial Spin Labeling (CASL). CASL uses a train of RF pulses to saturate (label) the blood water spins flowing through the neck. Under the assumption that water is a freely diffusible tracer, complete exchange of the inflowing blood water and tissue water takes place. This reduces the net magnetization in the imaged slice. The labeled image is then subtracted from a control condition (no labeling), which gives a perfusion weighted difference image (ΔM). The Bloch equations for longitudinal magnetization can be modified to include flow in the steady state condition 4 : (4) In the above equation, M z (t) = longitudinal magnetization of brain tissue per unit mass 5

17 = magnetization of the fully relaxed tissue per unit mass T 1b = longitudinal relaxation time of brain tissue water f = blood flow (perfusion) measured in ml/100 g/min λ = blood brain partition coefficient of water M a = magnetization of water in the inflowing arterial blood per unit volume of blood M v = equivalent term of the outflowing venous blood This leads to the flow quantification equation 4 (5) = steady state magnetization per unit mass of brain tissue = constant that describes the exponential decay to the steady state magnetization. It is defined by the following equation 5 : (6) The above equation is the basic equation of ASL, which shows how the apparent longitudinal relaxation time is affected by the flow of blood. Thus by measuring T 1app and the ratio of magnetization with and without spin saturation, a value for flow can be calculated using Eq. (6). The initially proposed method was improved by Williams, et al. 5 by introducing a technique that inverted the inflowing spins instead of saturating them. They used the principle of Adiabatic Fast Passage (AFP) 20 to invert the spins [see Eqs. (1-3)]. As the blood flows into the brain, RF pulses are applied for a sufficient duration (2-4 seconds) to a labeling plane in the neck of the subject. The frequency of the RF irradiation sweeps from far below to far above the resonance frequency and adiabatic inversion is achieved. The long duration of the RF pulse helps reach a flow-related steady state and all the 6

18 inflowing spins are inverted. The advantage of labeling arterial spins by inversion is a two fold increase in the difference between the labeled and unlabeled states, and flow is then given by 5 : (7) where is the magnetization of tissue per unit mass without arterial spin inversion (control image) and is the magnetization per unit mass with arterial spin inversion (label image). Transit Time and Magnetization Transfer (MT) Effects A couple of problems soon became apparent in the implementation of continuous spin labeling as a method to measure tissue perfusion accurately. The time taken for spins to travel between the labeling plane and the imaging slice, known as the transit time, is nonzero, and therefore T 1 relaxation occurs during this period 22. Since different regions of the brain have different transit times, this effect must be taken into account for accurate quantification of perfusion. The transit time problem has been noted in experiments by Walsh et al. 17 comparing CASL with the radioactive microsphere method of CBF quantification. An underestimation of flow was observed with the spin labeling technique when the flow rates were low. Additionally, underestimation of flow was also observed when flow rates are high, due to incomplete exchange. The application of a long (i.e., several seconds) off-resonance RF pulse causes a decrease in the water signal resulting from magnetization transfer (MT) effects 7. Although no direct saturation of the observed water magnetization in the imaging slice occurs because of the narrow line width of the free water peak, saturation of 7

19 macromolecular spins does occur, resulting in attenuation of the free water signal through magnetization transfer 7. This reduces the perfusion-dependent signal difference between the control and spin-labeled images. Zhang et al. 22 made the first attempt to account for these two effects. They accounted for the transit time effects by modifying the degree of inversion, α (equal to 0.5 for saturation and 1 for perfect inversion), by an amount related to T 1 relaxation. The MT effects were also accounted for in the Bloch equation analysis 22. (8) (9) where is the longitudinal relaxation time of arterial blood water and Δ is the transit time. To produce quantitative perfusion maps, knowledge of the transit time on a pixelby-pixel basis was necessary. This was attempted by Ye et al. 23 where the difference between spin-labeled and control images was measured as a function of the duration of the labeling period using single-shot EPI techniques, but the SNR was found to be insufficient to accurately estimate the transit time with high spatial resolution. An alternative approach was then taken by Alsop and Detre 24 in which a time delay was inserted into the sequence between the end of the labeling period and the image acquisition. Although this reduced the signal difference between the control and labeled images and complicated the quantification procedure, it was shown that if this delay is greater than the arterial transit times across the image, then the resulting CBF maps would be almost completely insensitive to variations in transit time. This technique has been shown to produce fairly good quality perfusion images, even in patients with 8

20 cerebrovascular disease, where there is usually large range of transit times 25. However, the length of the post-labeling delay, and thus the tolerance to long transit times is limited by T 1 relaxation and SNR 24. Another model proposed by Floyd, TF et al 26 used a fixed T 1a rather than measuring tissue T1. The model assumed that the labeled spins remains primarily in the vasculature and microvasculature rather than exchanging completely with tissue water, given by 26 : (10) Where = difference between control and label image intensities, is the average intensity of the control image in a defined ROI in the ventricular region, is the longitudinal relaxation time of CSF, and are the longitudinal and transverse relaxation times of arterial blood, is the post-labeling delay, is the labeling efficiency, is the water fraction of arterial blood and is the density of brain tissue. A two-coil setup can be used to avoid the saturation of macromolecular spins 19. In this setup, a small surface coil is placed on the neck of the subject immediately adjacent to the artery supplying blood to the brain. The physical range of the B 1 field produced by the surface coil is limited to a localized region. Arterial water spins are inverted using the surface coil, but no MT effects are observed in the brain, since the B 1 field does not extend sufficiently far. A separate (actively decoupled) coil is used to apply the imaging pulses and receive signal. However, specialized hardware is needed to implement this method, and flow quantification still is complicated by the need to account for a large range of arterial transit times. 9

21 Intravascular signal Another potential source of systematic error in CASL techniques is in the presence of signal contributions from vasculature in the subtraction (control minus labeled images) 4. All theoretical models describing the interrelationship between flow and signal difference are based on the assumption that the signal is exclusively from tissue. Thus an over-estimation of perfusion would result if there is a significant contribution from the vasculature. Most spin labeling studies include a flow-crushing gradient in the imaging sequence, which are intended to completely eliminate the signal emanating from intravascular spins 17. The transit time insensitive sequence proposed by Alsop and Detre 24, mentioned previously, allows most of the labeled blood to either exchange with the tissue or wash through the vasculature during the post labeling delay, and therefore suffers less from intravascular signal errors. Since transit time effects cause an underestimation of CBF and intravascular signal causes an over-estimation, the two effects can cancel out one another. However, this will be true only under specific circumstances, and both sources of systematic error need to be addressed. A combination of delayed acquisition and flowcrushing gradients usually work best to account for both the effects 23. Other Spin Labeling Quantification Issues Implementation of the CASL sequence on scanners. The initial CASL studies were done on small animals and transit times and magnetization transfer effects were minimal. The loss of the label is also intensified by T1 relaxation effects. Another issue to be taken care of is RF power deposition. Duty cycle restrictions of the RF amplifiers and the specific 10

22 absorption rate (SAR) of the sequence often limit the application of a continuous RF pulse over the required duration. Instead, a pulse train is applied consisting of repeated rectangular pulses with a duty cycle of approximately 75% to 90% 27. Degree of inversion, α. An accurate measurement of the labeling efficiency is required for continuous ASL 28. The degree of inversion is considerably more velocity dependent for the CASL technique than for pulsed ASL techniques 12, since the fulfillment of the adiabatic condition in the case of the continuous adiabatic fast passage pulse is intrinsically related to velocity of spin passage through the labeling gradient, individual calibration of the value of α may therefore be necessary for the CASL technique. CBF changes as a Tool for Functional Imaging Studies Functional studies are traditionally based on Blood Oxygenation Level Dependent (BOLD) contrast 29, which is the result of a series of physiological responses to neuronal activation. ASL based fmri offers measurement of blood flow changes that directly result from neuronal activation. ASL based fmri provides absolute quantification in terms of ml 100gm -1 min -1, whereas BOLD fmri gives relative estimates of changes in activation states. Additionally, ASL fmri can measure baseline CBF, thus making the results more quantitative and inter-subject comparisons more reliable. Functional activation data comparing BOLD and ASL contrast during task activation have indicated that ASL is superior to BOLD, for task periods greater than 1-2 minutes 30. These advantages warrant the use of ASL based CBF changes as the underlying mechanism for Functional imaging studies. 11

23 CHAPTER 3 MATERIALS AND METHODS All experiments were conducted on a 4.7T Varian vertical bore primate imaging magnet (Version 6.1, Palo Alto, CA). The perfusion imaging sequence that was implemented is shown in Fig. 1. A vendor supplied Echo Planar Imaging (EPI) sequence was modified to develop the CASL sequence. Due to duty cycle restrictions on the RF amplifier and possible SAR concerns, an RF pulse train was used to implement flow induced adiabatic inversion 27, instead of a single long pulse for the entire duration of inversion. A correction was then applied during calculation of the labeling efficiency, to account for the cycled labeling 31. Fig. 1. Perfusion imaging sequence. A standard EPI pulse sequence was modified to incorporate perfusion measurements. The RF pulse train consisting of 50ms hyperbolic secant pulses was applied along with a constant gradient resulting in flow induced adiabatic inversion. 12

24 The RF pulse train consisted of a series of 40 hyperbolic secant pulses applied along with a constant gradient in the Gz direction. Each pulse was applied for 50ms followed by a 4ms delay. The resultant label time was 2s with a RF duty cycle of 92.5%. The RF pulse train was followed by flow crushers, and then by a post-labeling delay. This was followed by an EPI acquisition. The acquisitions of label and control images were interleaved, with a control image following every label image. The proximal and distal labeling slices were chosen at equal distances from the imaged plane. Phantom Studies Inversion efficiency (α) is an important parameter in perfusion quantification. Experiments were performed on a flow phantom in order to accurately measure α. Experiment Design for Flow Phantom Study The flow phantom consisted of an 8.85m long, 0.95cm ID tube containing flowing water. The tube was surrounded by a static water filled tube near the imaged section (Fig.2). Two meters of flexible tubing were coiled inside the magnet to ensure that the flowing water was polarized before flowing into the phantom. Flow through the phantom was generated using a gravity driven flow apparatus as shown in Fig. 2(a). The top reservoir and recirculating pump were used to ensure continuous flow. The flow rate in the tube was regulated using a valve. The volume flow rate was measured using a measuring cylinder and a stop watch. No transit delay was used for this set of experiments to ensure that the labeled images were immediately followed by the EPI acquisition and the labeled spins were imaged. B 1 power was varied from 0 to 3.16W. The label and control gradients were 13

25 fixed 0.5 G/cm. Measurements were acquired for mean velocities of 8.65, 16.60, and 63.4 cm/s. Axial images were taken 1 cm downstream from the labeling plane. Control pulses were applied 1cm further downstream from the imaging plane. Each EPI image had a 64 x 64 data matrix and FOV of 12.8 x12.8 cm, slice thickness 5mm, and TR = 4s and TE = 30ms. 50 label-control image pairs were obtained. (b) (a) Fig. 2. Flow Phantom Setup. A gravity driven flow system is shown in (a). The static water filled tube (b) simulates the static tissue around the vasculature with blood flowing through it. The tube containing flowing water passes through the center of (b). The above images are schematic representations and are not drawn to scale. 14

26 Calculating the Inversion Efficiency (α) of the CASL Sequence Under steady state conditions (with minimal transit delay and no magnetization transfer effects), the inversion efficiency α can be measured within a flow ROI using 31 : (11) where α = inversion efficiency of the adiabatic fast passage (AFP), A = T 1 relaxation during transit from the inversion region to the measurement plane B = fraction of signal remaining after losses related to cycling the labeling (i.e. using a labeling pulse train instead of continuous RF) 31. Inversion Efficiency (<α>). For protons in steady flow with continuous RF labeling, the inversion efficiency can be approximated by Zhernovoi s model 31,32 where β is the adiabaticity factor given by 31 : (12) (13) In the calculation of <α>, plug flow is assumed. Depending on whether the flow is laminar or turbulent, Eq. (12) and Eq. (13) would assume more complicated forms. T1 relaxation during transit <A>.T 1 relaxation during transit can be approximated by 31 : Eq. 14 holds true for plug flows. Again this equation is an approximation and more complicated forms of the equation exist for laminar and turbulent flows 31. (14) 15

27 Effects of Duty Cycle (<B>). Often the labeling RF and the flow are not continuous in clinical experiments. Spin locking is disrupted when the labeling RF is turned off and spins are dephased by gradient ramps and field inhomogeneities between RF labeling pulses 31. The cycled labeling causes an attenuation of the measured inversion efficiency (<B>) that varies non-linearly with the duty cycle 33. <B> was assumed to be 0.95 in this experiment. Non-Human Primate Studies The Non-Human Primate studies were performed using the protocol enunciated by Gamlin, PD et al 34. A single 5mm thick axial slice was used for perfusion measurement. A proximal slice 2cm from the imaged plane was used for labeling and a symmetrically distal slice was chosen 2cm above the imaged slice (Fig. 3). A TR/TE of 4s/ 22ms was used. Matrix size was 64x64, FOV of 12.8x12.8 cms. 50 averages were taken, with interleaved control and labeled images. Total imaging time was 6min and 37s. An optimum labeling power of 2 W and an axial gradient of 0.45G/cm were used for labeling. A labeling time of 2 s with a 92.5% duty cycle was used before the application of a pulse labeling delay of 500 ms. Fig. 3. Scout images showing the position of the imaged, label and control planes 16

28 Data Processing All the image analysis was done in MATLAB (Version 7.2.0, Mathworks, Natick, MA). The raw images were reconstructed and converted to Nifti format, and exported to SPM5 (Wellcome Department of Imaging Neuroscience, University College, London) to correct for motion. The images were then separated into 50 pairs of label and control images. Each label image was subtracted from its corresponding control image. The resulting set of difference images was averaged and used for further perfusion quantification. CBF quantification in ml/100g/min were calculated using Eq. (10), proposed by Floyd TF, et al (2003). In the equation, is calculated from a manually drawn ROI in the ventricular region in the control image, TR=4s, TE=22ms, T1csf of 4.6s 35, w of 0.5s, of 1.5s 35, and T 2a = 0.7s 35, =0.76ml/g 16 and of 1.05 g/ml 16 17

29 CHAPTER 4 RESULTS Labeling Efficiency The B1 power was varied to verify its effect on labeling efficiency. Figure 4 illustrates how the inversion efficiency increases as the B1 power increases. As described by Maccotta et al, , the inversion efficiency becomes insensitive to RF power after a certain threshold defined by Eq. (3). Our experiments showed B1 insensitivity after 1.5W. An optimum power of 2W which resulted in a labeling efficiency of 88% was chosen for the monkey experiments. Fig, 4. B1 Power is varied to determine optimum inversion efficiency. (a) Schematic image showing the position of inflowing and outflowing blood (b) equilibrium image (c), (d), (e), (f), (g) and (h) are difference images in which the applied B1 power is increased from 10mW to 3.16W 18

30 Fig. 5. Inversion Efficiency vs labeling field B1 at different flow velocity rates Fig. 6. Dependence of inversion efficiency α on adiabaticity factor β 19

31 Figure 5 illustrates the variation of labeling efficiency at different flow rates. Four different flow velocity rates (8.64, 16.6, and cm/s) were used. A delayed increase in inversion efficiency is observed for higher flow rates. Fig. 6 shows the dependence of the adiabaticity factor β, on the calculation of inversion efficiency α. labeling efficiency reaches an optimum value for a β value of 2.5 or more. rcbf Quantification Results obtained from the non-human primate (n=1) are shown in Fig. 7. Fig. 7(b) shows the signed perfusion weighted difference image. Fig. 7(a) shows a T1 weighted Fig. 7. Perfusion difference images. (a) Anatomical T1-weighted image, (b) zero padded FT interpolated difference image, (c) is a fractional difference image and (d) is a percentage difference map 20

32 anatomical image of the same slice for comparison purposes. Fig. 7(c) is a fractional difference image, and Fig. 7(d) is a percentage difference map. The percentage difference varied from 1-3%. Thus, optimal parameters of RF power of 2W, a pulse labeling delay of 500ms, and a label time of 2s with a labeling efficiency of 88% and a 92.5% duty cycle were used for perfusion quantification. MT Bias Correction: A negative signal offset was observed in the difference images even after compensating for the labeling plane magnetization with a symmetric control plane magnetization. A dead monkey experiment with the same parameters revealed a negative signal offset, which can be attributed to an MT bias. Correcting the in vivo monkey data with the MT bias gave satisfactory results. Quantification: Perfusion quantification results are shown in Fig. 8(b) and (c), which depict the quantification results after the MT bias correction is applied. Two 8 voxel ROI s are drawn for gray matter and white matter [see Fig. 8(b)], and the analyses are shown in Table 1. The mean gray matter CBF within the GM ROI is 60.29±5.42 ml/100g/min. The mean white matter CBF within the WM ROI is 26.33±4.62 ml/100g/min. The mean ratio of GM/WM was 2.35±0.52. The peak GM values were ml/100g/min. A single GM and WM voxel time course was plotted after the application of the MT bias correction as shown in Fig. 9. The mean GM difference signal corresponded to 2.52% of the control signal S csf. The mean WM difference signal corresponded to 1.11% of the control signal. 21

33 Fig. 8. Quantified rcbf maps. A T1 weighted anatomical image (a) is used to compare quantification results in the (b) rcbf map. 8 voxel ROI s depicting GM and WM are shown in (b). (c) Zero padded FT interpolation of (b) ROI Pixel Number CBF (ml/100g/min) Ratio Gray Matter (GM) White Matter (WM) GM/WM Average 60.29± ± ±0.52 Table 1. GM and WM voxel values in the ROI shown in Fig. 8(b) 22

34 Fig. 9. Single voxel time courses for a GM and WM pixel after MT bias correction. Number of time Gray Matter White Matter Points /block of data (N) Mean± SD TSD as a percent of Mean Mean± SD TSD as a percent of Mean N= ± % 1.11 ± % N= ± % 1.17 ± % N= ± % 1.09 ± % N= ± % 0.99 ± % N= ± % 1.10 ± % Table 2. Temporal Standard Deviation varies with the number of time points per block of data (N) In table 2, the number of time points (N) per block of data is varied to see its effect on temporal standard deviation (TSD) as a percentage of the mean, to assess the 23

35 random fluctuations of basal CBF and its implications for ASL based functional studies. For the single voxel time course shown in figure 9, the GM TSD is 61% of the mean, whereas the WM TSD is 96% of the mean value. The TSD reduces progressively as more time points are summed together in a block. When N=9, the GM TSD is 11% and the WM TSD is 26%. 24

36 CHAPTER 5 DISCUSSION In this study, the Continuous Arterial Spin Labeling (CASL) sequence was implemented and perfusion quantification images were obtained on awake, behaving non-human primates. Further, GM and WM CBF values were obtained. This study established optimal parameters for RF power, labeling efficiency, and labeling time for the calculation of absolute CBF values. Potential Errors in Quantified CBF values CBF quantification in this study is limited by the following potential errors: Magnetization transfer effects 36, transit time effects 37, and inherent quantification model insufficiencies 16 and partial volume effects due to large voxels. The study results in magnetization transfer effects that are not entirely corrected by the usual control and label scheme. The paired label and control inversion slices equidistant above and below the imaging slice are assumed to have the same MT contributions 22. However, an asymmetric frequency response in the labeling pulse could cause MT effects in the imaged slice. Such an asymmetry is believed to be responsible for the observed shift in the difference image values. A novel method was used here to estimate and correct for the MT bias. However, this method has a drawback, wherein such correction factors would have to be calculated for each slice, if a multi-slice CASL experiment is desired. A single value for arterial transit time was assumed, since CBF is weakly dependent on arterial transit time within physiological limits 24 and because of the 25

37 difficulty in obtaining a transit time map 24. Arterial transit time in humans is estimated to be up to 1.5s, for which a 900 ms pulse labeling delay was used by Alsop, et al Owing to the fact that a single slice CASL experiment was performed and that monkeys have much shorter transit times 19, a single transit time value of 500 ms was chosen. The quantification model used here may also introduce some errors. Steger, et al 16 compared a number of quantification models including the Floyd model 26 that was used here. This model typically underestimated flow in comparison to other CBF quantification models 16. The lack of a gold standard for perfusion quantification limits comparison of the accuracy of the quantification technique. The voxel size in our studies is 2mm x 2mm x 5mm. Such large voxels may have GM as well as WM contained in them, and hence the value calculated is the average perfusion of the mixture of GM and WM contained in them. This may lead to underestimation of GM values. Another limitation of this study is the assumption of parameter values in the quantification procedure. The assumption of T1a significantly affects the calculation of the measured CBF values 16. The values assumed in our calculations were taken from the study by Pfeuffer and Logothetis, et al, In this study, CASL experiments were performed in only on one subject and only single slice CASL was studied. A further improvement would be multi-slice CASL by implementing amplitude modulated RF power. rcbf Comparison PET studies reported quantitative CBF values (whole brain) of ml/100g/min in propofol-anesthetized monkeys 38. GM values of ml/100g/min and 26

38 WM CBF values of 34 ml/100g/min were reported in ketamine-anesthetized monkeys 39. GM values of 104 ml/100g/min and WM values of 45 ml/100g/min in CASL experiments on isofluorane anesthetized monkeys were reported by Zhang et al, To our knowledge, no other CBF estimates using MRI on awake, behaving non-human primates have been published. The CBF values calculated here are in good agreement with human CASL perfusion estimates which range from ml/100g/min 18. The CBF GM/WM ratio calculated was 2.35, which is in excellent agreement with values obtained in literature. Ranges of GM/WM ratio reported using MRI are 1.97 by Talagala et al, ,2.3 by Zhang, et al , 2.7 by Ye, et al, , and 2.0 by Ye et al, 2000 using PET 41. Future Directions Separate Labeling Coil The implementation of a separate labeling coil has many advantages. With increased SNR and complete elimination of MT effects, fewer averages would be required, and the temporal resolution would be improved for functional studies 19. Feasibility for Functional Studies ASL based functional studies present a number of advantages over the traditional BOLD fmri. ASL enables absolute quantification of CBF changes directly caused by neuronal activation and makes inter subject comparison more feasible. The interleaved control and label scheme reduces baseline drifts 30 which is a common problem in BOLD fmri. Furthermore, ASL fmri is shown to be superior to BOLD contrast for task periods 27

39 of greater that 1-2 minutes 30,42. Hence a block design fmri experiment would be most feasible using ASL. If an experiment were designed to measure functional activity by measuring hemodynamic changes in blood flow using CASL, a reasonable temporal standard deviation may be achieved to make block design based functional studies possible. In the present study, the TSD as a percentage of the mean, for the time points/ block of N=1 is too high to gather meaningful conclusions from it [see Table 2]. The 50 time points represent an acquisition time of about 6 minutes, giving a task period of about 7.2 seconds. Increasing the number of time points/block to atleast 9 [see Table 2], would increase the task period to about 1 minute, making it suitable for ASL fmri. This would give a TSD of GM of about 11% which can give more meaningful results. Increasing the number of time points/block would further decrease TSD. PARSE Functional studies using ASL would be more attractive with better and faster sequences such as PARSE 43, which can vastly improve temporal resolution. 28

40 CHAPTER 6 CONCLUSION In this study, the Continuous Arterial Spin Labeling (CASL) sequence was implemented and perfusion was quantified for awake, behaving non-human primates. Further, GM and WM CBF were obtained. This study established optimal parameters for RF power, labeling efficiency, labeling time and pulse labeling delays to obtain perfusion estimates in awake, behaving non-human primates. Though CBF values for awake, behaving non-human primates have not been reported before, the estimates are in good agreement with published values for anesthetized monkeys. They are also in good agreement with published perfusion estimates in human CASL studies. Furthermore, the data obtained suggests that suitable block design based functional studies on non human primates may be feasible using CASL. 29

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44 APPENDIX IACUC APPROVAL 33