Impact of gradient number and voxel size on DTI tractography for resective brain surgery

Objective: To explore quantitatively and qualitatively how the number of gradient directions (NGD) and spatial resolution (SR) affect DTI-tractography in patients planned for brain tumor surgery, using routine clinical MR-imaging protocols. Methods: Of 67 patients with intracerebral lesions who had two different DTI-scans, three DTI-series were reconstructed to compare the effects of NGD and SR. Tractographies for four clinically relevant tracts (corticospinal tract, superior longitudinal fasciculus, optic radiation and inferior fronto-occipital fasciculus) were constructed with a probabilistic tracking algorithm and automated ROI-placement and compared for three quantitative measurements: tract volume, median fiber density and mean FA, using linear mixed-effects models. The mean tractography volume and intersubject reliability were visually compared across scanning protocols, to assess the clinical relevance of the quantitative differences. Results: Both NGD and SR significantly influenced tract volume, median fiber density and mean FA, but not to the same extent. Especially higher NGD increased tract volume and median fiber density. More importantly, these effects further increased when tracts were affected by pathology. The effects were tract-specific, but not dependent on threshold. The superior longitudinal fasciculus and inferior fronto-occipital fasciculus showed the most significant differences. Qualitative assessment showed larger tract volumes given a fixed confidence level, and better intersubject reliability for the higher NGD-protocol. SR in the range we considered seemed less relevant than NGD. Conclusion: The current study indicates that, under time constraints of clinical imaging, a higher number of diffusion gradients is more important than spatial resolution for superior DTI probabilistic tractography in patients undergoing brain tumor surgery. we explore the impact of NGD and spatial resolution on quantitative tractography measures in patients with intracerebral lesions involving four surgically relevant fiber tracts using a probabilistic tracking algorithm. A linear mixed effects analysis was performed of the relationship between three tractography outcome measures (volume, median fiber density and mean FA) and scanning-parameters NGD and resolution (reflected by “Series”) under healthy and pathological conditions (reflected by “Disease-effect”). Subject, Threshold and Tract were entered as random effects for controlling possible confounding relations. Both “Series” and “Disease-effect” affected all three outcome measures (log.vol, log.median.f, meanFA) significantly, as did all three random effects. Next, we extended this basic model as described in the Methods section, to find better predictive models (Table 2). First, an interaction effect of Series and Disease was added. A small effect was found for all three outcome measures (log.vol: χ 2 47.5, p<0.0001, log.median.f: χ 2 34.6 p<0.0001, meanFA: χ 2 226.6 p<0.0001). Next, we further improved the model by specification of disease effect by distinghuising “healthy” from “mass effect” from “edema/infiltration”. This also affected all three outcome measures (log.vol: χ 2 223.5, p<0.0001, log.median.f: χ 2 118.3 p<0.0001, meanFA: χ 2 158.3 p<0.0001) and improved the model. Finally, the effects of histology and tumor volume were explored for the oncological subgroup (consisting of gliomas and metastases). Expanding the model by covariates histology and tumor volume had no significant effect on neither of the three outcome measures (Table 2). In neurosurgical practice the optimal tractography would meet the following requirements: (1) estimated confidence levels assigned to the generated tracts, (2) high intersubject reliability, (3) relative insensitivity to changes in threshold settings, and (4) the ability to propagate fibers in areas of relative uncertainty of fiber direction, e.g. in case of pathology. Our data suggest, quantitatively as well as qualitatively, that a higher NGD scanning protocol helps for the last three, and that the number of diffusion gradients is more important than spatial resolution for DTI probabilistic tractography, especially in patients with structural intracerebral lesions. “effect” describes the added component/variable to the basic model. AIC = Akaike’s Information Criterion (smaller values indicate superior models), log.Lik = logLikelihood, χ2 relates to the difference of the two models compared, df = (Satterthwaite approximation of) degrees of freedom, and p = p-value (with Satterthwaite approximation for degrees of freedom). P-values <0.05 are considered significant. =