Tumor types are classically distinguished based on biopsies of the tumor itself, as well as a radiological interpretation using diverse MRI modalities. In the current study, the overarching goal is ...to demonstrate that primary (glioblastomas) and secondary (brain metastases) malignancies can be differentiated based on the microstructure of the peritumoral region. This is achieved by exploiting the extracellular water differences between vasogenic edema and infiltrative tissue and training a convolutional neural network (CNN) on the Diffusion Tensor Imaging (DTI)-derived free water volume fraction. We obtained 85% accuracy in discriminating extracellular water differences between local patches in the peritumoral area of 66 glioblastomas and 40 metastatic patients in a cross-validation setting. On an independent test cohort consisting of 20 glioblastomas and 10 metastases, we got 93% accuracy in discriminating metastases from glioblastomas using majority voting on patches. This level of accuracy surpasses CNNs trained on other conventional DTI-based measures such as fractional anisotropy (FA) and mean diffusivity (MD), that have been used in other studies. Additionally, the CNN captures the peritumoral heterogeneity better than conventional texture features, including Gabor and radiomic features. Our results demonstrate that the extracellular water content of the peritumoral tissue, as captured by the free water volume fraction, is best able to characterize the differences between infiltrative and vasogenic peritumoral regions, paving the way for its use in classifying and benchmarking peritumoral tissue with varying degrees of infiltration.
Characterization of healthy versus pathological tissue in the peritumoral area is confounded by the presence of edema, making free water estimation the key concern in modeling tissue microstructure. ...Most methods that model tissue microstructure are either based on advanced acquisition schemes not readily available in the clinic or are not designed to address the challenge of edema. This underscores the need for a robust free water elimination (FWE) method that estimates free water in pathological tissue but can be used with clinically prevalent single-shell diffusion tensor imaging data. FWE in single-shell data requires the fitting of a bi-compartment model, which is an ill-posed problem. Its solution requires optimization, which relies on an initialization step. We propose a novel initialization approach for FWE, FERNET, which improves the estimation of free water in edematous and infiltrated peritumoral regions, using single-shell diffusion MRI data. The method has been extensively investigated on simulated data and healthy dataset. Additionally, it has been applied to clinically acquired data from brain tumor patients to characterize the peritumoral region and improve tractography in it.
Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on ...a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations.
INTRODUCTION Fiber-tracking aids neurosurgical planning by delineating peritumoral eloquent tracts. The most commonly used FDA-approved solutions employ deterministic tracking strategies based on ...diffusion tensor imaging (DTI) acquisitions. This strategy can be confounded by edema. Resultantly, underlying white matter tracts may erroneously appear truncated on tractography maps which may contribute to a suboptimal surgical plan. METHODS Edema correction was performed on clinically-acquired DTI data, by applying a two-compartment modeling algorithm (FERNET), followed by deterministic fiber-tracking using the corrected DTI dataset. Results were compared to tracking with the original data prior to correction visually and quantitatively using metrics mean track count, voxel count, and mean track length. Ten patients presenting with glioblastoma and peritumoral edema in proximity to and/or infiltrating language (5 patients) and motor (5 patients) pathways were included. Preoperative fMRI and intraoperative DES were available in each case for correlation. RESULTS Twenty-five tracts were analyzed before and after free-water correction. Mean track count increased to 441 from 337 (P = .017) with the application of edema invariance algorithm. Voxel count, a proxy measurement for tract volume, increased to 2266 versus 1382 (P = .00005). Mean track length increased to 100 mm versus 91mm (P = .0007). Standard fractional anisotropy across all tracts decreased to 0.46 from 0.50 (P = .00004). Language patients in particular showed a clear increase in visible tracts. Overlay with functional activation maps confirms increased number of visualized fibers near functional locations. Correlation with DES also demonstrated increased number of visualized fibers resulting based on the corrected model. CONCLUSION Edema correction in standard clinical DTI results in improved visualization of motor and language tracts in patients with glioblastoma-associated peritumoral edema, informing pre-surgical planning for optimal tumor resection.
Characterization of healthy versus pathological tissue in the peritumoral area is confounded by the presence of edema, making free water estimation the key concern in modeling tissue microstructure. ...Most methods that model tissue microstructure are either based on advanced acquisition schemes not readily available in the clinic or are not designed to address the challenge of edema. This underscores the need for a robust free water elimination (FWE) method that estimates free water in pathological tissue but can be used with clinically prevalent single-shell diffusion tensor imaging data. FWE in single-shell data requires the fitting of a bi-compartment model, which is an ill-posed problem. Its solution requires optimization, which relies on an initialization step. We propose a novel initialization approach for FWE, FERNET, which improves the estimation of free water in edematous and infiltrated peritumoral regions, using single-shell diffusion MRI data. The method has been extensively investigated on simulated data and healthy dataset. Additionally, it has been applied to clinically acquired data from brain tumor patients to characterize the peritumoral region and improve tractography in it.
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