James S. Cordova

Whole-brain spectroscopic MRI biomarkers identify infiltrating margins in glioblastoma patients

BACKGROUND The standard of care for glioblastoma (GBM) is maximal safe resection followed by radiation therapy with chemotherapy. Currently, contrast-enhanced MRI is used to define primary treatment volumes for surgery and radiation therapy. However, enhancement does not identify the tumor entirely, resulting in limited local control. Proton spectroscopic MRI (sMRI), a method reporting endogenous metabolism, may better define the tumor margin. Here, we develop a whole-brain sMRI pipeline and validate sMRI metrics with quantitative measures of tumor infiltration. METHODS Whole-brain sMRI metabolite maps were coregistered with surgical planning MRI and imported into a neuronavigation system to guide tissue sampling in GBM patients receiving 5-aminolevulinic acid fluorescence-guided surgery. Samples were collected from regions with metabolic abnormalities in a biopsy-like fashion before bulk resection. Tissue fluorescence was measured ex vivo using a hand-held spectrometer. Tissue samples were immunostained for Sox2 and analyzed to quantify the density of staining cells using a novel digital pathology image analysis tool. Correlations among sMRI markers, Sox2 density, and ex vivo fluorescence were evaluated. RESULTS Spectroscopic MRI biomarkers exhibit significant correlations with Sox2-positive cell density and ex vivo fluorescence. The choline to N-acetylaspartate ratio showed significant associations with each quantitative marker (Pearsons ρ = 0.82, P < .001 and ρ = 0.36, P < .0001, respectively). Clinically, sMRI metabolic abnormalities predated contrast enhancement at sites of tumor recurrence and exhibited an inverse relationship with progression-free survival. CONCLUSIONS As it identifies tumor infiltration and regions at high risk for recurrence, sMRI could complement conventional MRI to improve local control in GBM patients.

A convolutional neural network to filter artifacts in spectroscopic MRI.

Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiation or injected contrast agent. Magnetic resonance spectroscopic imaging has been shown to be a viable imaging modality for studying several neuropathologies. However, a key hurdle in the routine clinical adoption of MRSI is the presence of spectral artifacts that can arise from a number of sources, possibly leading to false information.

SU-F-J-93: Automated Segmentation of High-Resolution 3D WholeBrain Spectroscopic MRI for Glioblastoma Treatment Planning

PURPOSE We report on an automated segmentation algorithm for defining radiation therapy target volumes using spectroscopic MR images (sMRI) acquired at nominal voxel resolution of 100 microliters. METHODS Wholebrain sMRI combining 3D echo-planar spectroscopic imaging, generalized auto-calibrating partially-parallel acquisitions, and elliptical k-space encoding were conducted on 3T MRI scanner with 32-channel head coil array creating images. Metabolite maps generated include choline (Cho), creatine (Cr), and N-acetylaspartate (NAA), as well as Cho/NAA, Cho/Cr, and NAA/Cr ratio maps. Automated segmentation was achieved by concomitantly considering sMRI metabolite maps with standard contrast enhancing (CE) imaging in a pipeline that first uses the water signal for skull stripping. Subsequently, an initial blob of tumor region is identified by searching for regions of FLAIR abnormalities that also display reduced NAA activity using a mean ratio correlation and morphological filters. These regions are used as starting point for a geodesic level-set refinement that adapts the initial blob to the fine details specific to each metabolite. RESULTS Accuracy of the segmentation model was tested on a cohort of 12 patients that had sMRI datasets acquired pre, mid and post-treatment, providing a broad range of enhancement patterns. Compared to classical imaging, where heterogeneity in the tumor appearance and shape across posed a greater challenge to the algorithm, sMRIs regions of abnormal activity were easily detected in the sMRI metabolite maps when combining the detail available in the standard imaging with the local enhancement produced by the metabolites. Results can be imported in the treatment planning, leading in general increase in the target volumes (GTV60) when using sMRI+CE MRI compared to the standard CE MRI alone. CONCLUSION Integration of automated segmentation of sMRI metabolite maps into planning is feasible and will likely streamline acceptance of this new acquisition modality in clinical practice.

Automated Verification of IGRT‐based Patient Positioning

A system for automated quality assurance in radiotherapy of a therapists registration was designed and tested in clinical practice. The approach compliments the clinical softwares automated registration in terms of algorithm configuration and performance, and constitutes a practical approach for ensuring safe patient setups. Per our convergence analysis, evolutionary algorithms perform better in finding the global optima of the cost function with discrepancies from a deterministic optimizer seen sporadically. PACS number(s): 87.55.Qr, 87.55.T, 87.55.NA system for automated quality assurance in radiotherapy of a therapists registration was designed and tested in clinical practice. The approach compliments the clinical softwares automated registration in terms of algorithm configuration and performance, and constitutes a practical approach for ensuring safe patient setups. Per our convergence analysis, evolutionary algorithms perform better in finding the global optima of the cost function with discrepancies from a deterministic optimizer seen sporadically. PACS number(s): 87.55.Qr, 87.55.T, 87.55.N.

TH-A-BRF-09: Integration of High-Resolution MRSI Into Glioblastoma Treatment Planning

PURPOSE Identification of a metabolite signature that shows significant tumor cell infiltration into normal brain in regions that do not appear abnormal on standard MRI scans would be extremely useful for radiation oncologists to choose optimal regions of brain to treat, and to quantify response beyond the MacDonald criteria. We report on integration of high-resolution magnetic resonance spectroscopic imaging (HR-MRSI) with radiation dose escalation treatment planning to define and target regions at high risk for recurrence. METHODS We propose to supplement standard MRI with a special technique performed on an MRI scanner to measure the metabolite levels within defined volumes. Metabolite imaging was acquired using an advanced MRSI technique combining 3D echo-planar spectroscopic imaging (EPSI) with parallel acquisition (GRAPPA) using a multichannel head coil that allows acquisition of whole brain metabolite maps with 108 μl resolution in 12 minutes implemented on a 3T MR scanner. Elevation in the ratio of two metabolites, choline (Cho, elevated in proliferating high-grade gliomas) and N-acetyl aspartate (NAA, a normal neuronal metabolite), was used to image infiltrating high-grade glioma cells in vivo. RESULTS The metabolite images were co-registered with standard contrast-enhanced T1-weighted MR images using in-house registration software and imported into the treatment-planning system. Regions with tumor infiltration are identified on the metabolic images and used to create adaptive IMRT plans that deliver a standard dose of 60 Gy to the standard target volume and an escalated dose of 75 Gy (or higher) to the most suspicious regions, identified as areas with elevated Cho/NAA ratio. CONCLUSION We have implemented a state-of-the-art HR-MRSI technology that can generate metabolite maps of the entire brain in a clinically acceptable scan time, coupled with introduction of an imaging co-registration/ analysis program that combines MRSI data with standard imaging studies in a clinically useful fashion.

SU-E-J-139: Fuzzy Clustering Segmentation of Glioblastoma in T1-MRI Imaging for Clinical Trials

PURPOSE Generating brain tumor volume measurements in a reproducible and efficient manner is a difficult, yet necessary, component of response assessment. The purpose of this study was to adapt and validate a multilevel Fuzzy C-means clustering algorithms for ROI tumor segmentation to allow consistent volumetric comparisons at multiple sites. METHODS Preoperative contrast-enhanced T1W images from 37 glioblastoma cases were segmented using Fuzzy C-means clustering-based methods and compared to manually contoured volumes created by specialists. The same was done post-operatively, using subtracted images to eliminate intrinsically T1-hyperintense material (blood). Volume computations based on the MacDonald criteria were also used for comparison. Agreement and inter-rater variability between volumes produced with each method was assessed by determining the concordance correlation coefficient (CCC). RESULTS The MacDonald criteria method had poor agreement (CCC=0.350-0.972) with manual contouring pre- and postoperatively, while the proposed semi-automated methods exhibited very high agreement (CCC=0.839-0.995) with manual contouring before and after resection. Fuzzy C-means clustering with three classes was the most robust semi-automated method, showing better inter-rater agreement than the MacDonald criteria method for both pre- (CCC of 0.990 and 0.975, respectively) and post-operative cases (CCC of 0.983 and 0.576, respectively). Post-operative inter-rater agreement was significantly different between these methods (p < 0.001). CONCLUSION The proposed semi-automated segmentation methods allow tumor volume measurements of MR images in a reliable and reproducible fashion necessary for measuring treatment response in glioblastoma patients in multicenter clinical trials.