Principles of MRI Physics and Engineering

1. Principles of MRI Physics and Engineering Lecture 4: Advanced MRI for Functional Imaging Allen W. Song Brain Imaging and Analysis Center Duke University

2. Slice Through Rotate Through See Through Wedge Through

3. Improving Spatial Resolution, • Coverage and Specificity

4. The Use of Ultra-High Magnetic Field (7T, 9.4T, etc.) M  Bo

5. Micro-MRI of a Mouse Brain Figure 12.2. An ultra-high resolution MR microscopy image. Shown is an image of a male C57BL/6J mouse brain, collected at 43m isotropic resolution. To appreciate the spatial resolution of this image, consider that approximately one million of its voxels would fit within a single 4mm*4mm*5mm human fMRI voxel. Image courtesy of Dr. G. Allan Johnson at the Center for In-vivo Microscopy at Duke University.

6. The Use of Paramagnetic Contrast Agent (Iron Oxide, Gadolinium, etc.) Susceptibility Effect

7. Single-Cell MRI Iron-particle labeled T lymphocyte cells (each cell ~ 5 mm, contrast agent amplifies 50 times in size) standard fluorescence image Dodd and colleagues (1999)

8. The Use of Parallel Imaging (4-Channel, 8-Channel, etc.) M  # of averages

9. Parallel MRI to Increase Spatial Resolution and Coverage Signal Combiner Final Image Figure 12.3. Use of multiple-channel acquisition to improve spatial resolution. By using more than one reception coil simultaneously (A), spatial resolution can be improved considerably without increasing imaging time. If four coils are used, for example, each coil can collect one quadrant of the image, resulting in an increase in spatial resolution by a factor of 2 (B). This improvement will occur whether the coils collect data from complementary spatial regions, as shown, or overlapping regions. [This image will need to be created by the art studio.]

10. Increasing Spatial Resolution

11. Acquisition: Probe SENSE Folded images in each receiver channel Reconstruction: Folded datasets + Coil sensitivity maps Increasing Spatial Resolution

12. SENSE imaging of a quality phantom with increasing reduction factor R indicated on the left. Phase encoding in vertical direction. Left: conventional sum-of-squares images. Middle: SENSE reconstruction from the same data. Right: maps of the relative noise level as predicted by SENSE theory, coloured according to the grey-scale on the far right (arbitrary units).

13. Increasing Spatial Coverage

14. Increasing SNR Four-channel Acquisition One-channel Acquisition Figure 12.4. High-resolution images acquired using four parallel channels. The conventional 256 * 256 image at left can be improved considerably by using four parallel channels to acquire the same data at 512 * 512 resolution. [Layout of this figure should be improved by the studio.]

15. Pulse Sequence Development for Improved Spatial Coverage at High Magnetic Field Susceptibility-Compensated Imaging: It is used for uniform spatial coverage at desired image location Susceptibility-Weighted Imaging: Similar to the earlier contrast agent example, it is used to enhance visualization of certain tissues, e.g. veins.

16. A B Signal Recovery using Susceptibility-Compensated Single-Shot EPI Figure 12.11. Effects of susceptibility compensation. Shown in (A) are four representative axial slices acquired using gradient echo echo-planar imaging. Visible is the typical pattern of susceptibility-induced signal losses in frontal and inferior temporal regions. In (B) the same slices are shown, acquired using a single-shot susceptibility compensation sequence. The signal in the regions of loss is much improved, and anatomical details are clearly visible. Song, AW, MRM 46, 407-411, 2001.

17. Susceptibility-Weighted Imaging Figure 12.12. A venogram image. Venograms use information about local susceptibility effects to map out the venous system. Imaging parameters courtesy of Dr. E. Mark Haacke, Wayne State University. [We will replace this image with a better venogram.]

18. High spatial resolution and SNR do not necessarily guarantee accurate functional localization, as the signal origin of the functional signal may not be directly linked to the neuronal activities. Thus, the important resolution for functional imaging is the “functional resolution”, that is, the ability to resolve the neuronal populations in action.

19. D A B C 9 E 10 9 10 10 10 10 + + 50 mv m 50 V 11 200 msec 200 ms 11 11 Slice 2 11 12 12 Correlation of fMRI with Intra-Cranial ERP Recordings Figure 12.5. Direct comparison of electrophysiological and intracranial data. Patients undergoing pre-surgical testing for epileptic foci may have more than a hundred electrodes placed on their cortical surface (a). It is possible to measure changes in electrical potentials within these electrodes associated with neuronal activity using electrodes, providing the most direct measure of neuronal activity possible in human subjects (e.g., Allison et al., 1999). For example, electrodes within the fusiform gyrus, as highlighted in yellow in (B), show selective electrical potentials about 200ms following the visual presentation of a photograph of a face, but not following other types of stimuli (C). FMRI studies of face processing have found activity in the similar region (D; from Puce et al., 1995), providing good evidence for the localization power of fMRI.

20. Signal Compartmentalization Based on Mobility b = 0 b = 54 t = 8.0 t = 3.6 b = 108 ADC 4.0 mm2/s 2.2 0.4 mm2/s Figure 12.6. BOLD maps at different b-factorsand the resulting ADC map. The subject passively viewed flashing visual stimuli while BOLD contrast was measured at different diffusion weighting values. Shown at upper left is a map of BOLD activity with no diffusion weighting (b = 0); this is equivalent to normal fMRI studies. As diffusion weighting increases, the region of significant activity reduces in extent, due to the elimination of signal from large blood vessels. The data from the three different diffusion-weighting values can be combined into a single map of ADC contrast, with red indicating voxels with high spin mobility due to the presence of large vessels and blue indicating voxels with low spin mobility that may reflect largely capillaries. (See also Figure 12.14).

21. (a) ADC (mm2/s) (b) ADC (mm2/s)

22. Initial Dip at Ultra-High Field To Improve Spatial Specificity 3~6 s Onset 1~2 s

23. High Spatial Specificity using Initial-Dip BOLD Signal in Cat Visual Cortex -0.4% Figure 12.10. Using the initial dip of the BOLD response to improve its spatial specificity. A wide swath of occipital cortex shows a strong positive response (A) to the presentation of a 10s duration visual stimulus (gray bar in B). However, the strong positive response is preceded by a smaller negative response known as the initial dip. Examination of the voxels showing a significant negative response (C) reveals a set of irregularly distributed clusters that showed orthogonal orientation preferences in additional testing. For comparison, (D) shows the activity to the positive response thresholded to the same number of voxels as active to the negative response. The pattern of activity shows fewer and larger clusters that likely represent large vessel effects. [Images from Kim et al., 2000].

24. Composite-Angle Map and Orientation “Pinwheels"

25. Artery Flow Signal Labeling Plane Small Vessels BOLD Signal Vein Optimized Perfusion Contrast For Spatial Specificity

26. Perfusion vs. BOLD fMRI Figure 12.13. Comparison of perfusion and BOLD contrasts. Shown in the top row are an anatomical reference image (A), a T1 image with ROIs in motor cortical areas (B), and a T2 image (C). The resting state perfusion map is shown in (D). During a simple motor task, there were significant increases in perfusion within the regions of interest, as shown in (E). The perfusion-related increases generally were similar to those obtained using BOLD contrast (F), but were more spatially specific. Image from Luh et al., 2000. [This black and white image is from the cited paper. Dr. Luh has agreed to provide a color replacement.]

28. Vascular Sensitivity of fMRI • Traditional BOLD contrast is sensitive to the blood oxygenation level changes in veins and capillaries • Flow changes, detected by ASL CBF imaging, are generally sensitive to arterial and capillary networks • Small vessel (e.g. arterioles, capillaries, venules) activation, likely the overlap between the BOLD and CBF contrasts, is better coupled to the neuronal activity. Artery Flow Signal Small Vessels BOLD Signal Vein

29. b = 229s/mm2 b = 114s/mm2 . . . . . . b = 0s/mm2 BOLD Contrast . . . . . . ADC Contrast Time B O L D A D C Simultaneous ADC and BOLD Imaging A Exponential Fitting Routine B Figure 12.14. Simultaneous acquisition of BOLD and ADC contrasts. By varying the amount of diffusion weighting over time, both BOLD and ADC contrasts can be obtained within a single fMRI run. A simple technique for doing so is shown in (A). Each image is acquired with a b-factor of 0, 144, or 229. The data from the b-factor of 0 are simply normal BOLD images, and a BOLD map can be generated from them (B, blue color map). Furthermore, the change in activity across the three b-factors can be used to generate an ADC map (B, red color map).

30. 2500 2000 1500 1000 500 0

31. Z > 3 Z > 5 Z > 7 ADC BOLD Increasing Baseline b Factor to Reduce Upstream Contributions in ADC Contrast (b=2 s/mm2, FM = 0.07 rad s/mm) Z > 3 Visual Stimulation: Rotating and Flashing Checkerboard, 12o Angle Song et al., NeuroImage, 20, 955-961, 2003.

32. Imaging Nerve Fiber Connection

33. Mean Diffusivity (ADC): Fraction Anisotropy (FA): Dzz Dyy Dxx Diffusion Tensor Imaging The diffusion pattern of the water molecules in each voxel can be thought as an ellipsoid. fMRI Relevance: The preferred diffusion direction can be used to track communication pathways among neuronal populations

34. Voxel-Based Diffusion Tensor Characterization Figure 12.7. Ellipsoids showing the relative diffusion along each axis for white matter voxels. Diffusion tensor imaging allows measurement of the relative motion of water molecules within the voxel. Each ellipsoid shows the rate of motion along each axis, with spheres showing isotropic diffusion and ellipses showing diffusion along a preferred axis. Within white matter (axons), molecules tend to move in straight lines, as indicated in these images by the alignment of the ellipsoids. Image courtesy of Dr. Guido Gerig, University of North Carolina at Chapel Hill. [These images are pretty good, but we may replace them with even better ones.]

35. Fiber Tracking by Connecting the Long Axis of Diffusion Tensor Figure 12.8. Fiber tracking using diffusion tensor imaging. Diffusion tensor imaging can be used to reconstruct axonal tracts connecting different brain regions. Shown here are selected axon tracts that begin or terminate in either the prefrontal cortex or the visual cortex.

36. V5 V5 V1 V2 V2 Functional Activation Guided Fiber Tracking (2D) Figure 12.9. Combined use of diffusion tensor and fMRI. Fiber tracking using diffusion tensor imaging can provide important information about the functional properties of different brain regions. In yellow is shown the diffusion tensor vector field; straight lines across multiple vectors indicate fiber tracts. Tracts between visual areas are shown in green. Note that although BOLD contrast shows activity in an extended set of brain regions (blue), ADC contrast shows activity in a smaller set of regions that more closely correspond to the connecting tracts. These activated areas likely correspond to visual processing areas V1, V2, and V5, representing primary, secondary, and motion-sensitive cortices.

37. Functional Activation Guided Fiber Tracking (3D)

38. II. Improving Temporal Resolution

39. Full k- vs. Partial k-Space Acquisition Keyhole Mirror K A B Figure 12.16. Reducing imaging acquisition time through partial k-space imaging. Shown are two common methods for reducing k-space coverage to gain temporal resolution, while maintaining the same imaging matrix and spatial resolution. In (A) is shown a keyhole pattern where the center of k-space (shown in orange) is acquired for each image, while the periphery of k-space (in blue) is filled with previously acquired data. The use of previously acquired data avoids the blurring effect induced by zero-filling methods. In (B) is shown a conjugate mirroring technique. Data is acquired from one half of k-space (in orange) and is mirrored into the other half (in blue). Note that the conjugate mirroring technique acquires data from slightly more than half of k-space to ensure that the center is well-represented.

40. Full k vs. Partial k-Space Acquisition

41. High-Resolution (Spatial and Temporal) fMRI using Partial k-Space Acquisition Figure 12.17. Partial k-space fMRI with cubic millimeter resolution. Use of a partial k-space acquisition strategy can greatly improve the speed of image acquisition. This image was collected at roughly the same speed as normal fMRI images, despite its much higher resolution. Image courtesy of Dr. Andrejz Jasmanowicz and Dr. James S. Hyde, Medical College of Wisconsin. [Dr. Jasmanowicz has agreed to provide the latest figure from their laboratory using this technique.]

42. Center vs. Outer k-Space Acquisition

43. Outer-k-Space fMRI Acquisition A B C D E Figure 12.18. Rapid fMRI using outer k-space imaging. Outer k-space imaging records data from the periphery of k-space on each image (A, orange), while filling the center of k-space with previously acquired data (A, blue). The value of this technique for fMRI is that the peripheral points contribute more to high spatial frequency effects, such as small BOLD activations. A standard EPI image from a time series of volumes is shown in (B), and the BOLD significance map from that time series is shown in (C). For comparison, an EPI image acquired using outer k-space filling is shown in (D), with its significance map shown in (E). Note the substantial difference between the two types of EPI images, with the image in (D) completely lacking low spatial frequencies, but also note the similarity in the activation maps. Data courtesy Dr. Gary Glover, Stanford University.

44. t t / √2 Figure 12.19. Simultaneous use of X and Y gradients to speed image acquisition. By using both x and y gradients at the same time to traverse a diagonal path through k-space, the time that it takes to fill up the entire k-space can be reduced by a factor of √2, effectively increasing the temporal resolution without sacrificing spatial resolution. Diagonal EPI Acquisition For Higher Temporal Resolution

45. Spiral-in and Spiral-out Acquisition Spiral Out TE Spiral In Figure 12.20. Spiral imaging sequences. Spiral sequences trace a spiral path through k-space, allowing more efficient use of the gradients and more rapid data acquisition. In the conventional spiral-out sequence (top), the gradients are turned on at time TE, but in the newer spiral-in technique, the gradients are turned on earlier so that the center of k-space is reached at time TE. The spiral-in sequence can thus acquire data more rapidly than the spiral-out sequence. [Figure to be created by the studio, perhaps by better layout of these elements.]

46. Spiral-in and Spiral-out Images at 4T and Their Combinations After Rotational Correction T1 Weighted Spiral-in Spiral-out Spiral-out + z shim 4 2 3 477 1 441 142 648 653 202 Direct Sum Weighted Sum MIP Sqrt of Squared Sum 664 482 539 6 8 7 5 917 653 550 685 855 Spiral-in can reach 24 slices / s at 642 matrix

47. High temporal resolution for image acquisition does not necessarily lead to high temporal resolution of the functional signal, as functional signal is modulated by the hemodynamics which is influenced by the timing hierarchy of blood delivery. Is it possible then, by using fMRI, to resolve neuronal activities on the order of ms?

48. Probing Neuronal Interactions Using Refractory Effect First Event Second Event Neuronal Refractoriness On the order of milliseconds Hemodynamic Refractoriness On the order of seconds

49. Probing Neural Events On the Order of Milliseconds Figure 12.21. Using neural interactions to improve the temporal resolution of fMRI. Ogawa and colleagues investigated electrophysiological and BOLD responses in somatosensory cortex to bilateral stimulation of the rat forepaws, first right then left. Shown in (A) at left are electrophysiological responses in right somatosensory cortex. Normally, a somatosensory evoked potential (N1) is present following stimulation of the left forepaw (e.g., in 12.5 and 75ms ISI conditions). But, when the right stimulation precedes the left by about 40ms, then there is suppression of the electrical activity. Importantly, that suppression is also present in the BOLD effect (B), suggesting that the use of fMRI suppression designs may be able to resolve very small differences in event timing, on the order of tens of milliseconds. Figure adapted from Ogawa et al., 2000.

50. Direct Imaging of Neuronal Activities