On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge

Abstract

Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. Most predictions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.

Mean kurtosis‐Curve (MK‐Curve) correction improves the test–retest reproducibility of diffusion kurtosis imaging at 3 T

“…Values may locally reach more than 40, and the majority lie in a suitable range. The SNR was on average 18 (GM) to 23 (WM), which is typical for a clinical study, and in line with commonly available research datasets (e.g., the SNR of the MASSIVE 38 ), is around 16 39 . While differences between DKI estimates with and without the MK‐Curve method are expected to decrease with increasing SNR, in their original work, Zhang et al showed that the MK‐Curve method also improved DKI estimates with high quality data, such as those from the Human Connectome Project 15 .…”

“…The SNR was on average 18 (GM) to 23 (WM), which is typical for a clinical study, and in line with commonly available research datasets (e.g., the SNR of the MASSIVE 38 ), is around 16. 39 While differences between DKI estimates with and without the MK-Curve method are expected to decrease with increasing SNR, in their original work, Zhang et al showed that the MK-Curve method also improved DKI estimates with high quality data, such as those from the Human Connectome Project. 15 Accordingly, we recommend the use of the MK-Curve method or other recently proposed alternatives, 10,12,14 when fitting DKI to both clinical and research quality data.…”

Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and bundle segmentation workflow

“…Figure 12 shows the MAPE of MD for different sources of variation. Most noticeable, MD is highly different when calculated using two different b-values, as expected [3, 25, 32, 75, 76], followed by differences due to vendors. Differences across RESCAN, SCAN, RES, and DIR are typically <5%.…”

Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and bundle segmentation workflow

“…12 shows the MAPE of MD for different sources of variation. Most noticeable, MD is highly different when calculated using two different b-values, as expected ( Novikov et al, 2018 ; Landman et al, 2007 ; Jones, 2004 ; De Luca et al, 2021 ; DK Jones et al, 1999), followed by differences due to vendors. Differences across RESCAN, SCAN, RES, and DIR are typically < 5%.…”