IntroductionCurrently, there is no consensus on the "optimal" b-table setting to acquire diffusion MRI for "beyond-DTI" analysis. 10 years ago, the trend in the field was "high angular resolution diffusion imaging" (HARDI), which acquires hundreds of sampling at one b-value (typically 3000 or more), but then HARDI was gradually replaced by multishell acquisition, which acquired diffusion MRI using at least two different b-values. This trend was motivated by studies confirming the benefit of combining data from multiple b-values. (2) in addition to the efficiency problem, the sampling density is largely inhomogeneous within each shell. Some directions have a higher sampling density, whereas others have lower. Consequently, the scheme has a lot of "rotation variation". You may plot the sampling distribution to see this sampling homogeneity problem. This inhomogeneity may reduce reproducibility problems if the subject head is positioned at different angles.
The multishell acquisition used in HCP protocol can also provide outstanding data, and many of my studies have used HCP multi-shell data to get robust results. The reason I do not use HCP scheme for my own study is that (1) the scheme is not "efficient" enough, (2) it is not invariant against rotation, and (3) intra-shell sampling is always much greater than inter-shell sampling. To be specific... My recommendation: 23 b-values with b-max=4,000 at 258 directionsMy personal recommendation is an 11-minute "grid-258" sampling with a maximum b-value of 4000, which acquires, not just two or three b-values, but 23 different b-values ranging from b=0 to b=4000 at a total of 258 directions. The low b-value range has fewer sampling compared to HCP multi-shell, making the entire acquisition much more efficient. Moreover, the scheme reaches a higher b-value to 4,000 so that it captures restricted diffusion much better. This scheme addresses the above mentioned 3 issues (1)(2)(3). Using a multi-band sequence (e.g. CMRR) with an MB factor of 4, this 2-mm 258-direction dMRI acquisition can be done in 12 minutes.
There are limitations with the grid sampling scheme:
Another b0 with an opposite phase encoding direction will also be acquired to correct for the phase distortion artifact. The following steps will help you set up the grid-258 sampling scheme on your MRI scanner. The following steps are verified on Siemens Prisma scanners, and a similar protocol can be implemented in other manufacturers. Optiomization strategiesThe following are strategies I recommend to optimize the DWI protocol (1) Reduce scanning time using multiband (MD=3 or 4) (2) shorten TE by partial Fourier PE (3) shorten TE by high readout bandwidth (trade-off with SNR) (4) Phase distortion correction by acquiring full forward PE and one b0 at backward PE The following are the step to install my recommended acquisition. Steps to install the 12-min q-space schemeI would recommend NOT to use Siemens' SMS-DWI for DSI-258 because my collaborator has reported serious peripheral nerve stimulation. If this has been improved, please email me and I will revise this recommendation. The protocol PDF (QSI_258dir.pdf) and exar file at the bottom of this page. Please use the "dMRI_dir258_1" sequence. To correct for phase distortion, please add "dMRI_dir258_2" and acquire only the b0 (take only few seconds). "dMRI_dir258_2" is a copy of the former sequence with an opposite phase encoding direction. If you are using other scanners, please follow the following instruction. STEP1: Download the b-tableIf you are using a Siemens Scanner, you may download the vector table here:101 directions on grid: https://pitt.box.com/v/GRID101-BTABLE 128 directions on grid: https://pitt.box.com/v/GRID128-BTABLE 258 directions on grid: https://pitt.box.com/v/GRID258-BTABLE (recommended) If you are using other Scanners, you may need to convert the b-table to its compatible format: https://pitt.box.com/v/GRID258 1. In-plane resolution: 2.0 mm, slice thickness: 2.0 mm (if your SNR is not good enough, increase them to 2.4 mm.) 2. Matrix size: 104x104 3. Slice number: 72 (can be reduced if ignoring the cerebellum), no gap 4. Multi-band acceleration factor: 3 or 4 5. In the diffusion tab, load the vector table obtained from STEP1 by "Free" mode. 6. b-value1=0 and b-value2=4000 7. "Bipolar" diffusion scheme (for eliminating eddy current). Some may prefer "Monopolar" and correct Eddy current using FSL's eddy. This only works on shell acquisition, and I do not recommend this approach for grid scheme. 8. Minimum TE and TR 9. Pixel bandwidth: ~1700 10. Phase encoding direction: A to P 11. Make a copy of the sequence, invert its phase encoding direction (P to A). Only b0 is needed here for phase distortion correction. 1. Make sure that you can still see the brain contour in the DWI with b=4000. If not, consider lowering the b-value to 3000. *Please feel free to send me your grid258 data for a quality check. I will compare the results with the data I have to make sure that you have achieved the same quality. 1. Rename DICOMs: Click on [Batch Processing][Step B1: Rename DICOM n Subfolders] and specify the folder that contains the DICOM files. DSI Studio will rename the DICOM files and create new folders storing them. This step can be done for multiple subjects in one shot. You may remove the original folders after it is done. 2. Create SRC and NIFTI files: Click on [Batch Processing][Step B2: Create SRC/NIFTI files] and specify the folder that contained the renamed DICOM files. DSI Studio will create SRC files and place them under the "src" folder, one for each scan session. The T1W images or other structure images will be created and placed under the "t1wt2w" folder. Please note that the PA b0 file (e.g., xxxxx_dMRI_dir258_PA_SBRef.nii.gz) will also be created and placed under the same folder. 3. Move SRC files together: Each SRC file store the all DWI for each scan session. I would suggest copy all SRC files to one folder to proceed with the analysis. You may also rename the SRC files to remove date information. The following analysis can follow the other documentations: Batch processing using GUI, Diffusion MRI Reconstruction in DSI Studio, Create a connectometry database The good news is that you can use the scanner's built-in DTI protocol to acquire "multishell" and still enjoy the benefit of "beyond-DTI" methods such as GQI, QSDR, RDI...etc. Here's a working parameter on a SIEMENS 3T Scanner: 1. acquire one 32-direction DTI at b=1500 and another 60-direction DTI at b=3000 (built-in ep2d_mddw protocol) 2. In-plane resolution: 2mm, slice thickness: 2mm 3. Matrix size: 104x104 4. Slice number: 72 (can be reduced if ignoring the cerebellum), no gap 5. Minimum TE and TR, but the TE for the b=1500 DTI should be the same as the TE of the b=3000 DTI. 6. Make a copy of the sequence and invert its phase encoding direction (acquire AP and PA for phase distortion correction) In the analysis, copy the DICOM files from these two DTI acquisitions together. This acquisition will require FSL's eddy to correct for the eddy correct artifact. An important issue about motion correctionFSL's eddy is the best solution for motion correction, and I highly recommend using it if it accepts your data. However, it is worth noting that the correction is using "data redundancy" to replace corrupted data. To be specific, motion correction routine first discards corrupted DWI volume/slices, and then the missing slices were estimated using "nearby DWI" (DWI with a similar diffusion gradient encoding). Motion correction works means that (1) several DWI samples are redundant (2) you can acquire fewer DWI samples in a shorter scanning time to get similar quality. An optimized sampling will have redundancy minimized, and there is no chance for motion correction. The grid sampling scheme I suggested above is close to this optimized condition. FSL's eddy will not improve its quality because there is minimal data redundancy. My past experience is that if there is visible motion in the acquisition, the only choice is to discard the entire scan and redo it. DSI Studio has a routine for DWI quality checks and can help identify problematic data sets. |
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Siemens vector table