Mapping axon diameters within the central and peripheral nervous system could play an important role in our understanding of nerve pathways, and help diagnose and monitor an array of neurological disorders. Numerous diffusion MRI methods have been proposed for imaging axon diameters, most of which use conventional single diffusion encoding (SDE) spin echo sequences. However, a growing number of studies show that oscillating gradient spin echo (OGSE) sequences can provide additional advantages over conventional SDE sequences. Recent theoretical results suggest that this is especially the case in realistic scenarios, such as when fibres have unknown or dispersed orientation. In the present study, we adopt the ActiveAx approach to experimentally investigate the extent of these advantages by comparing the performances of SDE and trapezoidal OGSE in viable nerve tissue. We optimise SDE and OGSE ActiveAx protocols for a rat peripheral nerve tissue and test their performance using Monte Carlo simulations and a 800 mT/m gradient strength pre-clinical imaging experiment. The imaging experiment uses excised sciatic nerve from a rat's leg placed in a MRI compatible viable isolated tissue (VIT) maintenance chamber, which keeps the tissue in a viable physiological state that preserves the structural complexity of the nerve and enables lengthy scan times. We compare model estimates to histology, which we perform on the nerve post scanning. Optimisation produces a three-shell SDE and OGSE ActiveAx protocol, with the OGSE protocol consisting of one SDE sequence and two low-frequency oscillating gradient waveform sequences. Both simulation and imaging results show that the OGSE ActiveAx estimates of the axon diameter index have a higher accuracy and a higher precision compared to those from SDE. Histology estimates of the axon diameter index in our nerve tissue samples are 4-5.8 μm and these are excellently matched with the OGSE estimates 4.2-6.5 μm, while SDE overestimates at 5.2-8 μm for the same sample. We found OGSE estimates to be more precise with on average a 0.5 μm standard deviation compared to the SDE estimates which have a 2 μm standard deviation. When testing the robustness of the estimates when the number of the diffusion gradient directions reduces, we found that both OGSE and SDE estimates are affected, however OGSE is more robust to these changes than the SDE. Overall, these results suggest, quantitatively and in in vivo conditions, that low-frequency OGSE sequences may provide improved accuracy of axon diameter mapping compared to standard SDE sequences.
Purpose Prostate diffusion‐weighted MRI scans can suffer from geometric distortions, signal pileup, and signal dropout attributed to differences in tissue susceptibility values at the interface between the prostate and rectal air. The aim of this work is to present and validate a novel model based reconstruction method that can correct for these distortions. Methods In regions of severe signal pileup, standard techniques for distortion correction have difficulty recovering the underlying true signal. Furthermore, because of drifts and inaccuracies in the determination of center frequency, echo planar imaging (EPI) scans can be shifted in the phase‐encoding direction. In this work, using a B 0 field map and a set of EPI data acquired with blip‐up and blip‐down phase encoding gradients, we model the distortion correction problem linking the distortion‐free image to the acquired raw corrupted k‐space data and solve it in a manner analogous to the sensitivity encoding method. Both a quantitative and qualitative assessment of the proposed method is performed in vivo in 10 patients. Results Without distortion correction, mean Dice similarity scores between a reference T2W and the uncorrected EPI images were 0.64 and 0.60 for b‐values of 0 and 500 s/mm 2 , respectively. Compared to the Topup (distortion correction method commonly used for neuro imaging), the proposed method achieved Dice scores (0.87 and 0.85 versus 0.82 and 0.80) and better qualitative results in patients where signal pileup was present because of high rectal gas residue. Conclusion Model‐based reconstruction can be used for distortion correction in prostate diffusion MRI.
Purpose: We investigate the feasibility of data-driven, model-free quantitative MRI (qMRI) protocol design on in vivo brain and prostate diffusion-relaxation imaging (DRI).Methods: We select subsets of measurements within lengthy pilot scans, without identifying tissue parameters for which to optimise for. We use the “select and retrieve via direct upsampling” (SARDU-Net) algorithm, made of a selector, identifying measurement subsets, and a predictor, estimating fully-sampled signals from the subsets. We implement both using artificial neural networks, which are trained jointly end-to-end. We deploy the algorithm on brain (32 diffusion-/T1-weightings) and prostate (16 diffusion-/T2-weightings) DRI scans acquired on three healthy volunteers on two separate 3T Philips systems each. We used SARDU-Net to identify sub-protocols of fixed size, assessing reproducibility and testing sub-protocols for their potential to inform multi-contrast analyses via the T1-weighted spherical mean diffusion tensor (T1-SMDT, brain) and hybrid multi-dimensional MRI (HM-MRI, prostate) models, for which sub-protocol selection was not optimised explicitly.Results: In both brain and prostate, SARDU-Net identifies sub-protocols that maximise information content in a reproducible manner across training instantiations using a small number of pilot scans. The sub-protocols support T1-SMDT and HM-MRI multi-contrast modelling for which they were not optimised explicitly, providing signal quality-of-fit in the top 5% against extensive sub-protocol comparisons.Conclusions: Identifying economical but informative qMRI protocols from subsets of rich pilot scans is feasible and potentially useful in acquisition-time-sensitive applications in which there is not a qMRI model of choice. SARDU-Net is demonstrated to be a robust algorithm for data-driven, model-free protocol design.
PurposeWe introduce “Select and retrieve via direct upsampling” network (SARDU-Net), a data-driven deep learning framework for model-free quantitative MRI (qMRI) experiment design. Here we provide a practical demonstration of its utility on in vivo joint diffusion-relaxation imaging (DRI) of the prostate.MethodsSARDU-Net selects subsets of informative qMRI measurements within lengthy pilot scans. The algorithm consists of two deep neural networks (DNNs) that are trained jointly end-to-end: a selector, identifying a subset of input qMRI measurements, and a predictor, estimating fully-sampled signals from such a subset. We studied 3T prostate DRI scans performed on 3 healthy volunteers with 16 unique (b,TE) values (diffusion-/T2-weighting), and used SARDU-Net to identify sub-protocols of 12 and 9 measurements. The reproducibility of the sub-protocol selection was evaluated, and sub-protocols were assessed for their potential of informing multi-contrast analysis, as for example Hybrid Multi-dimensional MRI (HM-MRI).ResultsSARDU-Net identifies informative sub-protocols of specified size from a small number of pilot scans. The procedure is reproducible across training folds and random initialisations. Moreover, SARDU-Net sub-protocols corresponding to up to ~50% scan time reduction support downstream HM-MRI modelling for which they were not optimised explicitly, providing quality of fit in the top 5% of all tested sub-protocols.ConclusionsSARDU-Net gives new opportunity to identify economical but informative qMRI data sets for clinical applications under high time pressure in a fully data-driven way from a few pilot scans. The simple SARDU-Net architecture makes the algorithm easy to train and appealing when extensive sub-protocol searches are intractable.
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