Optimal in-vivo dMRI protocols for r eff mapping. (a-b) Optimal Pearson's correlation coefficient (R) and normalized root mean square error (NRMSE) as a function of maximum gradient amplitude (gmax). Markers encode gmax of existing clinical scanners and research scanners (assuming 90 % of the nominal gmax). Colored markers highlight optimal protocols for next-generation clinical scanners. Line styles indicate different SNR baseline levels. While the reference SNR baseline level reflects our experimental conditions, increased SNR baseline levels assume an SNR increase through potential technical or acquisition advances. In addition, we accounted for SNR variation due to protocol parameter differences (see Eq. (13)). For our experimental protocol, baseline SNR levels would correspond to SNR values of 32 (reference), 56 (75 % increased) and 80 (150 % increased). Note that we optimized protocols by minimizing R, whereas NRMSE is an auxiliary metric. (ce) Comparison of estimated r eff with histological gold standard for optimal next-generation clinical scanner protocols across baseline SNR levels (color coding matches the highlighted protocols in (a-b)). SNR values of protocols are annotated above plots. Markers represent histological ROIs in Fig. 1a. The 95 % confidence intervals (shaded areas) were computed across 1000 noise realizations. The dashed lines illustrate theoretical perfect agreement. The legends provide metrics computed over all ROIs, including Pearson's correlation coefficient (R) and the corresponding p-value, the normalized rootmean-square error (NRMSE), and the fitting success rate (S) (see Section 4.7 for metric definitions).

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... clinical scanners could narrow gap to research scanners Next-generation clinical scanners may reveal correlation at increased SNR Fig. 3c-e show simulated r eff for optimal next-generation clinical scanner protocol at each baseline SNR level (corresponding to colored markers in Fig. 3a-b). At SNR ≈ 27, reŕecting the expected SNR of the protocol candidate under our experimental conditions, next-generation clinical scanners would not reveal a signiőcant correlation with ...

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... clinical scanners could narrow gap to research scanners Next-generation clinical scanners may reveal correlation at increased SNR Fig. 3c-e show simulated r eff for optimal next-generation clinical scanner protocol at each baseline SNR level (corresponding to colored markers in Fig. 3a-b). At SNR ≈ 27, reŕecting the expected SNR of the protocol candidate under our experimental conditions, next-generation clinical scanners would not reveal a signiőcant correlation with our histology data (R = 0.29, p = 0.13, see Fig. 3c). This aligns roughly with simulations of our experimental protocol on a state-of-the-art research ...

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... next-generation clinical scanner protocol at each baseline SNR level (corresponding to colored markers in Fig. 3a-b). At SNR ≈ 27, reŕecting the expected SNR of the protocol candidate under our experimental conditions, next-generation clinical scanners would not reveal a signiőcant correlation with our histology data (R = 0.29, p = 0.13, see Fig. 3c). This aligns roughly with simulations of our experimental protocol on a state-of-the-art research scanner in Fig. 2m, although these simulations assume Rician-rather than Gaussian-distributed signals (see Section SI8 for impact of noise distribution on r eff estimaton). However, our simulations suggest that a signiőcant correlation ...

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... experimental protocol on a state-of-the-art research scanner in Fig. 2m, although these simulations assume Rician-rather than Gaussian-distributed signals (see Section SI8 for impact of noise distribution on r eff estimaton). However, our simulations suggest that a signiőcant correlation could be revealed at SNR ≈ 48 (R = 0.49, p = 3.7e −3 , see Fig. 3d) and a stronger correlation at SNR ≈ 68 (R = 0.63, p < 0.05, see Fig. 3e). For experiment-like simulations in (e), the pattern reflects the median across 1000 noise ...

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... although these simulations assume Rician-rather than Gaussian-distributed signals (see Section SI8 for impact of noise distribution on r eff estimaton). However, our simulations suggest that a signiőcant correlation could be revealed at SNR ≈ 48 (R = 0.49, p = 3.7e −3 , see Fig. 3d) and a stronger correlation at SNR ≈ 68 (R = 0.63, p < 0.05, see Fig. 3e). For experiment-like simulations in (e), the pattern reflects the median across 1000 noise ...

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... model-inherent bias is a relevant factor in procotol design While R remains stable after reaching the optimum at some g max (see Fig. 3a), NRMSE decreases thereafter (see Fig. 3b). We attribute this loss of absolute agreement to the increasing inŕuence of the model-inherent bias, making it a relevant factor in protocol design for scanners with very high g max , such as next-generation research ...

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... model-inherent bias is a relevant factor in procotol design While R remains stable after reaching the optimum at some g max (see Fig. 3a), NRMSE decreases thereafter (see Fig. 3b). We attribute this loss of absolute agreement to the increasing inŕuence of the model-inherent bias, making it a relevant factor in protocol design for scanners with very high g max , such as next-generation research ...

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... investigated whether r eff could distinguish between healthy individuals and those with autism spectrum disorder (ASD) as a potential clinical application of the optimal next-generation scanner protocols identiőed in Fig. 3c-e. Based on a reported 28.6 % reduction of axon radii in the splenium under ASD conditions [7], we simulated ASD conditions by adjusting our histological axon radius distributions accordingly, while leaving distributions untouched for healthy controls (see Fig. 4a). In a Monte Carlo simulation, we estimated the statistical power to ...