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Improving diffusion-weighted imaging of post-mortem human brains: SSFP at 7 T.

ABSTRACT: Post-mortem diffusion imaging of whole, human brains has potential to provide data for validation or high-resolution anatomical investigations. Previous work has demonstrated improvements in data acquired with diffusion-weighted steady-state free precession (DW-SSFP) compared with conventional diffusion-weighted spin echo at 3T. This is due to the ability of DW-SSFP to overcome signal-to-noise and diffusion contrast losses brought about by tissue fixation related decreases in T2 and ADC. In this work, data of four post-mortem human brains were acquired at 3T and 7 T, using DW-SSFP with similar effective b-values (b(eff)~5150 s/mm(2)) for inter-field strength comparisons; in addition, DW-SSFP data were acquired at 7 T with higher b(eff) (~8550 s/mm(2)) for intra-field strength comparisons. Results demonstrate that both datasets acquired at 7 T had higher SNR and diffusion contrast than data acquired at 3T, and data acquired at higher b(eff) had improved diffusion contrast than at lower b(eff) at 7 T. These results translate to improved estimates of secondary fiber orientations leading to higher fidelity tractography results compared with data acquired at 3T. Specifically, tractography streamlines of cortical projections originating from the corpus callosum, corticospinal tract, and superior longitudinal fasciculus were more successful at crossing the centrum semiovale and projected closer to the cortex. Results suggest that DW-SSFP at 7 T is a preferential method for acquiring diffusion-weighted data of post-mortem human brain, specifically where the primary region of interest involves crossing white matter tracts.

SUBMITTER: Foxley S

PROVIDER: S-EPMC4229505 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundDiagnostic classification of diffuse gliomas now requires an assessment of molecular features, often including IDH-mutation and 1p19q-codeletion status. Because genetic testing requires an invasive process, an alternative noninvasive approach is attractive, particularly if resection is not recommended. The goal of this study was to evaluate the effects of training strategy and incorporation of biologically relevant images on predicting genetic subtypes with deep learning.MethodsOur dataset consisted of 384 patients with newly diagnosed gliomas who underwent preoperative MRI with standard anatomical and diffusion-weighted imaging, and 147 patients from an external cohort with anatomical imaging. Using tissue samples acquired during surgery, each glioma was classified into IDH-wildtype (IDHwt), IDH-mutant/1p19q-noncodeleted (IDHmut-intact), and IDH-mutant/1p19q-codeleted (IDHmut-codel) subgroups. After optimizing training parameters, top performing convolutional neural network (CNN) classifiers were trained, validated, and tested using combinations of anatomical and diffusion MRI with either a 3-class or tiered structure. Generalization to an external cohort was assessed using anatomical imaging models.ResultsThe best model used a 3-class CNN containing diffusion-weighted imaging as an input, achieving 85.7% (95% CI: [77.1, 100]) overall test accuracy and correctly classifying 95.2%, 88.9%, 60.0% of the IDHwt, IDHmut-intact, and IDHmut-codel tumors. In general, 3-class models outperformed tiered approaches by 13.5%-17.5%, and models that included diffusion-weighted imaging were 5%-8.8% more accurate than those that used only anatomical imaging.ConclusionTraining a classifier to predict both IDH-mutation and 1p19q-codeletion status outperformed a tiered structure that first predicted IDH-mutation, then 1p19q-codeletion. Including apparent diffusion coefficient (ADC), a surrogate marker of cellularity, more accurately captured differences between subgroups.

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Improving diffusion-weighted imaging of post-mortem human brains: SSFP at 7 T.

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