Perfusion Imaging Lalith Talagala, Ph. D Perfusion Imaging Lalith Talagala, Ph.D. NIH MRI Research Facility National Institute for Neurological Disorders and Stroke National Institutes of Health, Bethesda, MD First, I want to thank the organizers for inviting me to give this talk. And I appreciate the opp to talk to you today. In this talk I plan to give an overview of the two perfusion MRI techniques available for clinical studies.

Perfusion Imaging: Outline Introduction Dynamic Susceptibility Contrast (DSC) Method Quantification Examples Arterial Spin Labeling (ASL) Labeling Techniques So the talk outline quite simply the following: We will discuss each one of two techniques :DSC and ASL Talk about how they work, how signal changes are converted to perfusion estimates Along the way I will show some example images from each method

Definitions Perfusion – capillary blood flow delivered to tissue MRI methods can assess blood flow – ml blood / min / 100 g of tissue blood volume – ml blood /100 g of tissue mean transit time – seconds Normal values for brain To start off lets review the definitions: Perfusion which refers to capillary blood flow deliv ered to tissue. We are interested in measuring this quantity on a voxel by voxel basis. In addition to flow, MRI can measure blood volume and mean transi time of i.e. average time taken by blood to travel through the capillary bed. We will concentrate on measurements in the brain… so these parameter will be referred to as cerebral blood flow/ volum CBF,CBV. Gray Matter White Matter CBF (ml /min/100 g) 60 - 80 20 - 30 CBV (ml / 100 g) 4 - 6 2 - 4 MTT (s) 4 - 5 5 - 6

Perfusion MRI Dynamic Susceptibility Contrast (DSC) Requires contrast injection Large signal changes, Fast Single application (clinical) Arterial Spin Labeling (ASL) No external contrast required Small signal change, Slow Multiple measurements (clinical and research) S there are now two major approaches for Clinical perfusion MRI., DSC and ASL Here I have listed their major differences. DSC needs contrast, …. ASL no contrast ..

Dynamic Susceptibility Contrast (DSC) Monitor passage of Gadolinium contrast through tissue using rapid T2*/T2 weighted MRI Gradient Echo EPI TR ~1.5- 2 s TE = 30 -50 ms ~1.5 min Imaging Lets look at the DSC technique: This method is based on monitoring the passage of a paramagnetic contrast agent through the tissue using T2/T2* weighted MRI.- Here we detect the change in water MRI signal due to contrast So we inject contrast as a bolus and then acquire images through the brain using a fast imaging tech such as echo planar imaging Gd 0.1 - 0.2 mmol/kg Bolus Injection

DSC - Mechanism Gd Chelates: Paramagnetic, Intravascular W/ Gd W/O Gd Tissue/ Blood Dc Field Inhomogeneity R2* (=1/T2*) GE/SE MRI Signal So what happens when Gd goes through the tissue:? First it is paramag, and, under normal conditions, stays with in the blood vessels. ..

DSC – Passage of Gd through tissue Here is a series of images acquired following bolus injection of contrast. This particular data set is from a patient with a tumor on the right side. You are looking at the same slice acquired at different times. We see high baseline signal before the contrast arrives in the slice, the signal is decreased when the contrast washes in and the signal is restored to when the contrast washes out of the slice 1.5 T, 0.1 mmol/kg, GE- EPI, TE = 50 ms, TR = 2 s

DSC – Signal vs Time GM pixel Blood pixel The signal change due to washin and wash out constrat is better seen by looking at the signal timecourse of indiviual pixels. Here we see the timecourses of a pixels corresponding to a blood vessel and a gray matter We can appreciate a large of signal loss during the first pass of the contrast agent and a smaller signal loss later due to recirculation of the agent. Signal loss is greater in blood pixels than in tissue pixels. Baseline First pass Recirculation

DSC: Signal loss to Concentration k – proportionality constant C – Gd concentration Sc – Signal with Gd S0 – Baseline signal without Gd So we can see that the signal change depends on concentration, but how are they related? Early studies have shown that the change in T2 relax rate is linearly proportional to concentration…so we use that. We can also write that signal in the presence of Gd in terms of the baseline signal and delta R2* Combining the two allows us to estimate the Gd concentration via the log of the ratio of signal with and without contrast. Note that k is not known for each subject.

DSC: Concentration vs time So we can pixel by pixel convert signal timecourses to conc timecourses. We can see the blood pixel show higher concentration than the GM pixel.

Arterial input function (AIF) DSC – CBV, CBF, MTT ? F (ml/min) Cart (t) Ctis (t) Tracer Kinetic Theory Arterial input function (AIF) Tissue Response So here is what we have so far… We know the Gd concentration timecouse in the arteries—this refered to as the arterial input function. Then we know the corrs tissue conc timecourse i.e the tissue response. We would like to use this information to estimate the flow, volume and transit time of blood through the voxel. We can make use of the well developed tracer kinetic theory to estimate them. From the central volume theorem, these parameters are related as shown here.

DSC- Calculation of CBV Cart (t) Cvein (t) Ctis (t) F Estimatition of blood volume is straight forward. CBV is proportional to the ratio of the areas of the tissue timecourse and the blood vessel as I have indicated here. To get accurate ratio need to correct for recirculation effect and account for the differences in hematocrit in large arteries and capillaries.

DSC- Calculation of CBF and MTT Cart (t) Cvein (t) Ctis (t) F CBF Rscl(t) So in terms of concentrations we have the relationship --- the tissue timecourse is the convolution between the arterial input and the residue funnction scaled by CBF To find the residue function , we need to decon the arterial input from the tissue curve. -- And you get a scaled version of the residue function. MTT is the normalized area of scaled residue function CBF is the scaling factor or initial height of the scaled residue. Deconvolution of the arterial curve from the tissue curve allows us to determine CBF and MTT.

DSC – CBV, CBF, MTT maps CBV CBF MTT If we do this calculation on a pixel by pixel basis on the data I showed before we can get images like this. Here we elevated CBV and CBF and shorter MTT in the right hemisphere containing the tumor compared. to the relatively normal left side has lower CBV/CBF and longer MTT. These maps clearly shows the extended abnormal area in this patient.

DSC – CBV, CBF, MTT maps CBV CBF MTT Here is data from multiple slices. Combination of CBF and MTT maps give good idea of the extent of abnormal area of in this patient MTT maps in some cases show clearly the boundary between normal and abnormal tissue.

DSC – CBF maps 1.5 T, 0.1 mmol/kg, GE- EPI, TE = 50 ms, TR = 2 s Here some calculated CBF maps from a different patient. In this we can see good GM/WM contrast on both hemispheres. You can also see high signal in the blood vessels which can make it difficult to see abnormalities near by. 1.5 T, 0.1 mmol/kg, GE- EPI, TE = 50 ms, TR = 2 s

DSC: Quantification Issues Accuracy of DR2* Û C relationship Arteries (quadratic) and tissue (linear) Arterial input function (AIF) determination Partial volume , vessel orientation effects Truncation of the peak Dispersion between measurement site and tissue (local AIF) Deconvolution errors Sensitivity to noise Sensitivity to bolus arrival times Absolute CBF/CBV require use of scaling factors determined separately Although we do have a good foundation for quantification of DSC data, in practice number of difficulties.

Arterial Spin Labeling (ASL) Measure the change in MRI signal due to magnetic labeling (tagging) of inflowing blood Tag Control Label => Perfusion Maps Now lets look at the ASL technique. ASL tech is based on measuring the signal change due to magnetic labeling of inflowing blood….labeling/tagging generally refers to inversion of blood magnetization Subtraction of images acquired without and with labeling allows us to see the signal change due to flow The ASL expt consists of time period for labeling, delay for labeled blood to flow into the tissuse and an imaging period. Imaging can be done with any rapid imaging method , use of EPI is common. Here, unlike in DSC, we can use min TE bec we want to changes in longituditunal magnetization . GE/SE EPI TR ~ 2 - 5 s TE = minimum ~ 4-5 minutes LABELING IMAGING 0.5 – 2.5 s 0.5 - 2 s 0.75 s

ASL: Control/Label/Difference Difference (DM) images - S Control Images in Mydocs/data/ASLdiffExample 20101028-10671/008NIfTI/ 1 pair (10 sec) 24 pairs (4 min) DM: GM – 0.9 %, WM – 0.15 % Label

ASL: Labeling Strategies Pulsed ASL (PASL) Wide labeling slab Created by a short pulse (milliseconds) Input function – decaying exponential (T1 of blood) Continuous ASL (CASL) Narrow labeling plane Long duration (seconds) Input function – constant Labeling Slab PASL Labeling Plane CASL ASL can be performed in two ways continuous and pulsed and they differ in how the inflowing blood is tagged. In cont ASL , a spatially narrow inversion plane is defined with approp RF and Gradients and it is applied for a long time ..several seconds. And in pulsed ASL, we invert blood (and tissue) with in a wide region proximal to the region being imaged. The inversion is accomplish in a v short time.

ASL- Quantification of CBF One compartment model: Labeled blood stays in the vasculature PASL CASL R1a – relaxation rate of arterial blood a0 – labeling efficiency t – labeling time w –post labeling delay l – brain/blood partition coefficient of water

ASL: Pulsed Labeling (QUIPSS II) Proximal Inversion Proximal Saturation Image RF/Signal … Gradient Gi … Label Gi¹0 Label Voxel View

ASL: Pulsed Labeling (QUIPSS II) Gi¹0 Control Gi = 0 Voxel View Label Control

ASL: Pulsed Labeling (Q2TIPS) Control Difference Advantages: High tagging efficiency Low SAR Ease of implementation Disadvantage: Limited coverage

ASL: Continuous Labeling (Flow-driven Adiabatic Fast Passage) Image Label RF/Signal … Control Gradient Gl … Label Gl¹0 RF=0 Control Voxel View Label Control

Continuous ASL: Neck Labeling Coil Plane 8 Ch Rx Advantages: Whole brain coverage High labeling efficiency Lower SAR Neck Labeling Coil Although these studies were encouraging, SNR of perfusion images were not sufficient for any higher resolution images. Availability of multi channel coils in the last few years helped to this problem. Here we see the 8 or the 16 channel coils that we normally use for our studies. Disadvantage: Requires special hardware

CASL with a Neck Labeling Coil: Multi-shot 3D-FSE Spiral Shows perfusion data with 2 coil method on the left is the difference signal (control-label) ..obtained using a 3d sequence spiral FSE with background suppression..resolution …. 6 min …Feature to note is the whole brain coverage..i.e good perfusion signal throughout the brain. Indicates that tha labeling coil is well balanced. Quantified by acquiring R1 maps…middle and CBF on the right. % DS R1 map CBF 3T, Head Coil, 3D-FSE, 3.7 x 3.7 x 5 mm3 8 shots, TR 5.9 s, Label dur 4.1 s, PL delay 1.64 s, Backgr supp 6 min 22 sec Talagala et al, MRM 52: 131-140 (2004)

Continuous ASL: Neck Labeling Coil 3T, 8 Ch Rx, 2D EPI, 3 x 3 x 3 mm3, TE/TR 13 ms/5 s, 4.5 minutes

CASL with a Neck Labeling Coil: Hemangioblastomas 3T, 8 Ch Rx, 2D EPI, 1.5 x 1.5 x 3 mm3 TE/TR 16 ms/5 s, LD/PLD 3 s/1.6 s 10 minutes

CASL Perfusion MRI at 7 T Volume Tx Coil Surface Labeling Coil Head Rx Array Coil Tx Volume / 8 Ch Rx Array Neck Labeling Coil

7T CASL with a Neck Labeling Coil Control (RF off) - Label (RF +ve offset) Control (RF off) - Label (RF –ve offset) 7T, 8 Ch Rx, 2D EPI, 2 x 2 x 3 mm3 TE/TR 13 ms/5 s, ASSET X2, LD = 3 s, PLD 1.5 s 8 minutes Talagala et al ISMRM 2008

CASL Perfusion MRI at 7 T %DS T1 CBF Gray Matter White Matter ml/ (100g. min) ms %DS T1 CBF Gray Matter White Matter Mean T1 (ms) 1940 1363 DS/S (%) 1.43 +/- 0.25 0.3 +/- 0.04 CBF (ml/min.100g) 76 +/- 11 27 +/- 2.4 7T, 8Ch Rx, 2.1 x 2.1 x 3 mm3, 9 minutes, n=5

ASL: Pseudo Continuous labeling 0 f 2f 3f nf Image … Label RF/Signal … f = g Gz t z Control … Gradient Advantages: Whole brain coverage High labeling efficiency Use standard hardware Disadvantages: Higher SAR Labeling sensitive to off-resonance effects 2cm

Pseudo Continuous ASL: 3T data 3.6 x 3.6 x 5 mm3 Gradient-echo EPI TE/TR = 20.8/5000 ms t/w = 2500/1700 ms Scan time 5:00

Pseudo Continuous ASL: 7T data 2.3 x 2.3 x 3 mm3 Gradient-echo EPI TE/TR = 20.8/5100 ms t/w = 3000/1200 ms SENSE 3x Scan time 4:15 Luh et al, MRM 69:402 (2013)

CASL fMRI with a Neck Labeling Coil 3T, Head Coil Finger movement (0.5 Hz), {48 s Task / 48 Rest} X 6, 10 min GE EPI, 3.75 x 3.75 x 5 mm3 12 s per Cont/Label pair SPM, Spatial normalization smoothing (8 mm), N= 15 Perfusion based fMRI can be performed using this methood by using 2D EPI multi slice imaging. Here we see group average data showing CBF changes in response to a finger tapping task. CBF increases are seen the cortical and subcortical regions and the cerebellum. We were able to identify decreases in CBF in the ipsilateral hemisphere. Garraux et al, NeuroImage 25:122-132 (2005)

3T CASL Perfusion fMRI with 16 Rx CBF 75 ± 11 ml/(min.100g) DCBF 78 ± 7% 3 x 3 x 3 mm3 CBF 92 ± 16 ml/(min.100g) DCBF 102 ± 10% 1.5 x 1.5 x 3 mm3 Improved SNR allows Perfusion based fMRI studies also can be conducted at higher resolution Here are examples at 3X3 and 1.5X 1.5 inplane resolution shows activation localized to gray matter. The resting CBF and %change in CBF is about 20% higher on the average at high-resolution, which is most possibly due to reduced partial volume effect. Finger movement (2 Hz), {40 s Rest / 40 Task} X 8, N = 6 GE EPI, TE 26 ms, 10 s per Control/Label pair, 10 min 40 sec

Functional Connectivity with ASL Perfusion In addition to high res studies, increased sensitivity from multi channel coils make studies that are otherwise difficult more robust and reproducible. CBF based resting state connectivity mapping shown here is one example. On top you see the spectra of resting state time courses from the motor ROI. Spectra show expected low frequency BOLD oscillations. Because in the CBF timcourse, tagged and untagged images, CBF resting state oscillations are modulated at nyquist frequency and therefore appear at the high frequency region of the spectrum. This allows CBF fluctuations to be extracted without BOLD contamination. CBF images can then be subjected to correlation analysis to generate functional connectivity maps as shown here. Here we see bilateral CBF correlation in L/R motor areas across multiple slices. We found that CBF fluctuation about ~24% of the baseline CBF in the resting state. DCBF = 29 ± 19% DBOLD = 0.26 ± 0.14% (N=13) 3T, GE EPI, 16 Ch Rx, 3.75 X 3.75 X 3 mm3 TE/TR 12.5/3200 ms, 10 min 40 sec Chuang et al., NeuroImage 40, 1595 (2008)

Functional Connectivity: BOLD, Perfusion, CMRO2 Wu et al., NeuroImage 45, 694 (2009)

Perfusion MRI: Summary Dynamic Susceptibility Contrast (DSC) Requires contrast administration Fast acquisition (< 2 min), whole brain coverage Readily performed in clinical scanners Online/Offline processing software available Absolute quantification is difficult Arterial Spin Labeling (ASL) No contrast required 4-5 min acquisition, whole brain coverage Absolute quantification is possible Robust sequences becoming available Useful for clinical and research work S there are now two major approaches for Clinical perfusion MRI., DSC and ASL Here I have listed their major differences. DSC needs contrast, …. ASL no contrast ..