Impact of Gradient Number and Voxel Size on Diffusion Tensor Imaging Tractography for Resective Brain Surgery

OBJECTIVE: To explore quantitatively and qualitatively how the number of gradient directions (NGD) and spatial resolution (SR) affect diffusion tensor imaging (DTI) tractography in patients planned for brain tumor surgery, using routine clinical magnetic resonance imaging protocols. METHODS: Of 67 patients with intracerebral lesions who had 2 different DTI scans, 3 DTI series were reconstructed to compare the effects of NGD and SR. Tractographies for 4 clinically relevant tracts (corticospinal tract, superior longitudinal fasciculus, optic radiation, and inferior fronto-occipital fasciculus) were constructed with a probabilistic tracking algorithm and automated region of interest placement and compared for 3 quantitative measurements: tract volume, median fiber density, and mean fractional anisotropy, 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 fractional anisotropy, but not to the same extent. In particular, higher NGD increased tract volume and median fiber density. More importantly, these effects further increased when tracts were affected by disease. 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. CONCLUSIONS: This 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

A systematic review on the use of quantitative imaging to detect cancer therapy adverse effects in normal-appearing brain tissue

Cancer therapy for both central nervous system (CNS) and non-CNS tumors has been previously associated with transient and long-term cognitive deterioration, commonly referred to as ‘chemo fog’. This therapy-related damage to otherwise normal-appearing brain tissue is reported using post-mortem neuropathological analysis. Although the literature on monitoring therapy effects on structural magnetic resonance imaging (MRI) is well established, such macroscopic structural changes appear relatively late and irreversible. Early quantitative MRI biomarkers of therapy-induced damage would potentially permit taking these treatment side effects into account, paving the way towards a more personalized treatment planning. This systematic review (PROSPERO number 224196) provides an overview of quantitative tomographic imaging methods, potentially identifying the adverse side effects of cancer therapy in normal-appearing brain tissue. Seventy studies were obtained from the MEDLINE and Web of Science databases. Studies reporting changes in normal-appearing brain tissue using MRI, PET, or SPECT quantitative biomarkers, related to radio-, chemo-, immuno-, or hormone therapy for any kind of solid, cystic, or liquid tumor were included. The main findings of the reviewed studies were summarized, providing also the risk of bias of each study assessed using a modified QUADAS-2 tool. For each imaging method, this review provides the methodological background, and the benefits and shortcomings of each method from the imaging perspective. Finally, a set of recommendations is proposed to support future research

Direct comparison of [11C] choline and [18F] FET PET to detect glioma infiltration: a diagnostic accuracy study in eight patients

Background Positron emission tomography (PET) is increasingly used to guide local treatment in glioma. The purpose of this study was a direct comparison of two potential tracers for detecting glioma infiltration, O-(2-[18F]-fluoroethyl)-l-tyrosine ([18F] FET) and [11C] choline. Methods Eight consecutive patients with newly diagnosed diffuse glioma underwent dynamic [11C] choline and [18F] FET PET scans. Preceding craniotomy, multiple stereotactic biopsies were obtained from regions inside and outside PET abnormalities. Biopsies were assessed independently for tumour presence by two neuropathologists. Imaging measurements were derived at the biopsy locations from 10 to 40 min [11C] choline and 20–40, 40–60 and 60–90 min [18F] FET intervals, as standardized uptake value (SUV) and tumour-to-brain ratio (TBR). Diagnostic accuracies of both tracers were compared using receiver operating characteristic analysis and generalized linear mixed modelling with consensus histopathological assessment as reference. Results Of the 74 biopsies, 54 (73%) contained tumour. [11C] choline SUV and [18F] FET SUV and TBR at all intervals were higher in tumour than in normal samples. For [18F] FET, the diagnostic accuracy of TBR was higher than that of SUV for intervals 40–60 min (area under the curve: 0.88 versus 0.81, p = 0.026) and 60–90 min (0.90 versus 0.81, p = 0.047). The diagnostic accuracy of [18F] FET TBR 60–90 min was higher than that of [11C] choline SUV 20–40 min (0.87 versus 0.67, p = 0.005). Conclusions [18F] FET was more accurate than [11C] choline for detecting glioma infiltration. Highest accuracy was found for [18F] FET TBR for the interval 60–90 min post-injection

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection

Accurate MR Image Registration to Anatomical Reference Space for Diffuse Glioma

To summarize the distribution of glioma location within a patient population, registration of individual MR images to anatomical reference space is required. In this study, we quantified the accuracy of MR image registration to anatomical reference space with linear and non-linear transformations using estimated tumor targets of glioblastoma and lower-grade glioma, and anatomical landmarks at pre- and post-operative time-points using six commonly used registration packages (FSL, SPM5, DARTEL, ANTs, Elastix, and NiftyReg). Routine clinical pre- and post-operative, post-contrast T1-weighted images of 20 patients with glioblastoma and 20 with lower-grade glioma were collected. The 2009a Montreal Neurological Institute brain template was used as anatomical reference space. Tumors were manually segmented in the patient space and corresponding healthy tissue was delineated as a target volume in the anatomical reference space. Accuracy of the tumor alignment was quantified using the Dice score and the Hausdorff distance. To measure the accuracy of general brain alignment, anatomical landmarks were placed in patient and in anatomical reference space, and the landmark distance after registration was quantified. Lower-grade gliomas were registered more accurately than glioblastoma. Registration accuracy for pre- and post-operative MR images did not differ. SPM5 and DARTEL registered tumors most accurate, and FSL least accurate. Non-linear transformations resulted in more accurate general brain alignment than linear transformations, but tumor alignment was similar between linear and non-linear transformation. We conclude that linear transformation suffices to summarize glioma locations in anatomical reference space