Optimization of acquisition parameters for diffusion MRI using chemical tracing
Giorgia Grisot1,2, Julia Lehman3, Suzanne N Haber3, and Anastasia Yendiki2

1Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States, 2Athinoula A. Martinos Center for Biomedical Imaging, MGH, Charlestown, MA, United States, 3University of Rochester School of Medicine, Rochester, NY, United States

Synopsis

Determining the optimal diffusion MRI (dMRI) acquisition scheme for reconstructing a brain network of interest with tractography is an open problem, and the lack of ground truth on brain connections makes it challenging to resolve. We use chemical tracing and ex vivo dMRI in macaques to optimize dMRI acquisition with respect to tractography accuracy. We present preliminary results illustrating that 1. There is an upper bound to the angular resolution of dMRI, beyond which tractography accuracy does not improve, and 2. That this finding likely generalizes to in vivo human dMRI.

Purpose

Determining the optimal diffusion MRI (dMRI) acquisition scheme for reconstructing a brain network of interest for a specific study is an open problem. Given the time limitations of in-vivo scans, it is important to determine which parameters (b-values, number of gradient directions and number of shells) have the greatest effect on the accuracy of white matter (WM) pathways reconstructed by tractography. However, the lack of ground truth on brain circuitry poses a challenge for quantifying the accuracy of dMRI tractography. Here, we propose to use chemical tracing in macaques for optimizing dMRI acquisitions with respect to tractography accuracy. Previous studies that performed dMRI validation using chemical tracing did not compare acquisition parameters, using instead a single dMRI acquisition. These studies either used dMRI with low angular resolution1,23 or used tracer and dMRI data from different animals1,4,5. Here, we collect ex-vivo dMRI data with 22 shells, spanning b-values from 1600 to 40k, on a macaque brain that has received a tracer injection in the frontal cortex. This allows us to investigate how the addition of each shell affects tractography accuracy. We find that there are diminishing returns beyond a b-value of 25.6k for reconstructing projections from this injection site. We then show a preliminary evaluation of in-vivo human dMRI tractography from the homologous cortical region with different shells.

Methods

Macaque data: An adult macaque was injected with Lucifer yellow in BA10. Following injection and perfusion, the brain was imaged in a small-bore 4.7T MRI system with maximum gradient=480mT/m. A dMRI data set was collected using a 2-shot EPI sequence with δ=15ms, Δ=19ms, 514 directions, 0.7mm resolution and b­max=40000s/mm2. The brain was then sectioned and stained. Axon bundles were manually traced on the histology data using Neurolucida and spatially aligned to the dMRI data using our in-house pipeline (Fig.2). We extracted subsets of volumes from the macaque dMRI corresponding to different shells. Details for each combination are shown in Table 1. We fitted the ball-and-stick model6,7 to each dataset and performed probabilistic tractography in FSL, using the injection site as seed. Chemical tracing revealed three bundles of frontal pole fibers (Fig.1): uncinate fasciculus-UF, corpus callosum-CC and internal capsule-IC. A connection present in both dMRI tractography and tracer data was deemed true positive (TP); one present only in tractography (e.g., the fornix) was deemed a false positive (FP).

Human data: We used in-vivo human dMRI data from the MGH/UCLA Human Connectome Project (3T Siemens Skyra, maximum gradient=300mT/m, 512 directions, bmax=10000, 1.5mm resolution). We extracted different subsets of volumes, listed in Table 1. Seeds for tractography were placed in the region homologous to the macaque injection site. Taking advantage of inter-species homologies in frontal cortex4, we deemed streamlines going through the UF, CC, IC as TPs and all others (e.g., the fornix) as FPs.

Results

We evaluated tractography accuracy on each dataset by computing the TP and FP rate as the fraction of connections reconstructed by tractography that are, respectively, present and absent in the ground truth. We repeat this for varying levels of thresholding on the probabilistic tractography map to plot receiver-operating characteristic (ROC) curves. Fig.3 and 4 show ROC curves for each acquisition scheme of the monkey and human datasets respectively.

Discussion

Our findings show that there are diminishing returns, in terms of tractography accuracy, above a certain maximum b-value. As seen in Fig. 3, accuracy in the ex-vivo macaque brain does not improve substantially by adding shells beyond a maximum b-value of 25.6k and 256 directions, and, in some case, becomes worse. Note that this would be equivalent to a b-value of 6K-7K in vivo, as diffusivity in the fixed macaque brain is at 25% of its in vivo values8. As seen in Fig. 4, accuracy in the in vivo human data for reconstructing the homologous projections did not improve substantially when adding the b=10K shell.

Conclusion

We used objective, quantitative metrics to compare dMRI acquisition schemes in terms of the accuracy of tractography originating in BA10. Our results indicate that there is an upper bound to the angular resolution of dMRI, beyond which accuracy does not improve. Encouragingly, the optimum is within the range feasible in vivo with current state-of-the-art ultra-high diffusion gradients and accelerated sequences. These results are specific to the accuracy of frontal pole projection, and the analyses methods used here. Our future work will analyze pathways from additional injection sites to determine whether optimal acquisition parameters are global or depend on the brain network of interest. We will also investigate optimal acquisition schemes for other diffusion model-fitting and tractography methods.

Acknowledgements

This research was carried out in whole or in part at the Athinoula A. Martinos Center for Biomedical Imaging at the Massachusetts General Hospital, using resources provided by the Center for Functional Neuroimaging Technologies, P41EB015896, a P41 Biotechnology Resource Grant supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB), National Institutes of Health and the NIH Blueprint for Neuroscience Research (T90DA022759/R90DA023427) This work also involved the use of instrumentation supported by the NIH Shared Instrumentation Grant Program and/or High-End Instrumentation Grant Program; specifically, grant number(s) S10RR016811, S10RR023401, S10RR019307, S10RR019254, S10RR023043.

References

1 Thomas, C. et al. Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited. Proc. Natl. Acad. Sci. 111, 16574–16579 (2014).

2 Gao, Y. et al. Validation of DTI tractography-based measures of primary motor area connectivity in the squirrel monkey brain. PLoS One 8, e75065 (2013).

3 Dauguet, J. et al. Comparison of fiber tracts derived from in-vivo DTI tractography with 3D histological neural tract tracer reconstruction on a macaque brain. Neuroimage 37, 530–538 (2007).

4 Jbabdi, S., Lehman, J. F., Haber, S. N. & Behrens, T. E. Human and monkey ventral prefrontal fibers use the same organizational principles to reach their targets: tracing versus tractography. J. Neurosci. 33, 3190–201 (2013).

5 Schmahmann, J. D. et al. Association fibre pathways of the brain: Parallel observations from diffusion spectrum imaging and autoradiography. Brain 130, 630–653 (2007).

6 Van Essen, D. C. et al. Diffusion MRI. Diffusion MRI (Elsevier, 2014). doi:10.1016/B978-0-12-396460-1.00016-0

7 Jbabdi, S., Sotiropoulos, S. N., Savio, A. M., Graña, M. & Behrens, T. E. J. Model-based analysis of multishell diffusion MR data for tractography: How to get over fitting problems. Magn. Reson. Med. 68, 1846–1855 (2012).

8 D’Arceuil, H. E., Westmoreland, S. & de Crespigny, A. J. An approach to high resolution diffusion tensor imaging in fixed primate brain. Neuroimage 35, 553–565 (2007).

Figures

Fig.1: Chemical tracing provides ground truth for tractography. A: two histological slides from a macaque brain with an injection in area BA10, and light microscopy magnification (right) of the internal capsule-IC, uncinate fasciculus-UF, the corpus callosum-CC and fornix. The tracer can be seen in the first three , but not in the fornix. dMRI in monkey (B) and humans (C) shows connections through CC, IC and UF, but also through the fornix, which is a false positive as chemical tracing shows.


Fig.2: Schematic of our in-house registration pipeline, which uses blockface images as an intermediate step to compensate for histology processing distortions. After background segmentation, we use a combination of 2D and 3D robust affine and diffeomorphic registration to spatially align dMRI and histology. We then apply the combined transformation to the tracer outlines, which are in histology space, and achieve spatial correspondence between histology, dMRI and tracing.


Table 1: Tables summarizing the different dMRI acquisition schemes investigated on the monkey data (left) and the human data (right). The 48 hours ex-vivo acquisition of the macaque data allowed us to probe a wider range of b-values compared to the in-vivo human acquisition.


Fig. 3: Comparison of ROC curves (left) and area under the curve (AUC) estimates (right) from different acquisition schemes of the monkey brain. Each datapoint in the ROC curve represent the TP and FP rate probabilistic map of the reconstructed tract at varying levels of thresholding, ranging from 0 to 2000 at increments of 100.


Fig. 4: Comparison of ROC curves (left) and area under the curve (AUC) estimates (right) from different acquisition schemes of the human brain. Each datapoint in the ROC curve represent the TP and FP rate probabilistic map of the reconstructed tract at varying levels of thresholding, ranging from 0 to 2000 at increments of 100.




Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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