Two Pools of ATP Detected in the Brains of Pediatric Patients with Myelin Oligodendrocyte Glycoprotein Antibody Disorders (MOGAD) by 3D 31P MR Spectroscopic Imaging (MRSI) at 7T

Over the past four decades, ATP, the obligatory energy molecule for keeping all cells alive and functioning, was thought to contribute only one set of 31P MR signals in the human brain. Here we report for the first time the simultaneous detection of two pools of ATP in the human brain by high-resolution 3D 31P MRSI at ultrahigh field 7T. These two ATP pools differ in cytosolic Mg2+ concentration (1:0.5 ratio), with a resonance separation of 0.5 ppm at beta-ATP, a well-established imaging marker of intracellular Mg2+ concentration. Mg2+ is a cofactor of ATPase and its deficiency is associated with immune dysfunction, free radical damage, perturbations in Ca2+ homeostasis, development of atherosclerosis and dyslipidemia, and a number of neurological disorders, such as cerebral vasospasm, stroke, migraine, Alzheimer's disease, and Parkinson's disease. Our study documents reduced Mg levels in the brain of patients with myelin oligodendrocyte glycoprotein antibody disorders (MOGAD), which is an idiopathic, inflammatory, demyelinating condition of the central nervous system (CNS) more common in pediatric patients. Low-Mg2+ ATP signals in MOGAD were detected mostly in the white matter regions, which may suggest Mg2+ deficiency in oligodendrocytes, which are primarily responsible for maintenance and generation of the axonal myelin sheath. This preliminary study demonstrates the utility of the 7T 3D 31P MSRI for revealing altered energy metabolism with reduced Mg availability at a normal ATP level. The potential correlation between [Mg2+] and disease progression over time should be assessed in larger cohorts.

NIMG-16. DEEP LEARNING SUPER-RESOLUTION MR SPECTROSCOPIC IMAGING TO MAP TUMOR METABOLISM IN MUTANT IDH GLIOMA PATIENTS

Metabolic imaging can map spatially abnormal molecular pathways with higher specificity for cancer compared to anatomical imaging. However, acquiring high resolution metabolic maps similar to anatomical MRI is challenging in patients due to low metabolite concentrations, and alternative approaches that increase resolution by post-acquisition image processing can mitigate this limitation. We developed deep learning super-resolution MR spectroscopic imaging (MRSI) to map tumor metabolism in patients with mutant IDH glioma. We used a generative adversarial network (GAN) architecture comprised of a UNet neural network as the generator network and a discriminator network for adversarial training. For initial training we simulated a large data set of 9600 images with realistic quality for acquired MRSI to effectively train the deep learning model to upsample by a factor of four. Two types of training were performed: 1) using only the MRSI data, and 2) using MRSI and prior information from anatomical MRI to further enhance structural details. The performance of super-resolution methods was evaluated by peak SNR (PSNR), structure similarity index (SSIM), and feature similarity index (FSIM). After training on simulations, GAN was evaluated on measured MRSI metabolic maps acquired with resolution 5.2×5.2 mm2 and upsampled to 1.3×1.3 mm2. The GAN trained only on MRSI achieved PSNR = 27.94, SSIM = 0.88, FSIM = 0.89. Using prior anatomical MRI improved GAN performance to PSNR = 30.75, SSIM = 0.90, FSIM = 0.92. In the patient measured data, GAN super-resolution metabolic images provided clearer tumor margins and made apparent the tumor metabolic heterogeneity. Compared to conventional image interpolation such as bicubic or total variation, deep learning methods provided sharper edges and less blurring of structural details. Our results indicate that the proposed deep learning method is effective in enhancing the spatial resolution of metabolite maps which may better guide treatment in mutant IDH glioma patients.

Characterization of Distinctive In Vivo Metabolism between Enhancing and Non-Enhancing Gliomas Using Hyperpolarized Carbon-13 MRI

The development of hyperpolarized carbon-13 (13C) metabolic MRI has enabled the sensitive and noninvasive assessment of real-time in vivo metabolism in tumors. Although several studies have explored the feasibility of using hyperpolarized 13C metabolic imaging for neuro-oncology applications, most of these studies utilized high-grade enhancing tumors, and little is known about hyperpolarized 13C metabolic features of a non-enhancing tumor. In this study, 13C MR spectroscopic imaging with hyperpolarized [1-13C]pyruvate was applied for the differential characterization of metabolic profiles between enhancing and non-enhancing gliomas using rodent models of glioblastoma and a diffuse midline glioma. Distinct metabolic profiles were found between the enhancing and non-enhancing tumors, as well as their contralateral normal-appearing brain tissues. The preliminary results from this study suggest that the characterization of metabolic patterns from hyperpolarized 13C imaging between non-enhancing and enhancing tumors may be beneficial not only for understanding distinct metabolic features between the two lesions, but also for providing a basis for understanding 13C metabolic processes in ongoing clinical trials with neuro-oncology patients using this technology.

Preoperative Imaging of Glioblastoma Patients using Hyperpolarized 13C Pyruvate: Potential Role in Clinical Decision Making

Background Glioblastoma remains incurable despite treatment with surgery, radiation therapy, and cytotoxic chemotherapy, prompting the search for a metabolic pathway unique to glioblastoma cells. 13C MR spectroscopic imaging with hyperpolarized pyruvate can demonstrate alterations in pyruvate metabolism in these tumors. Methods Three patients with diagnostic MRI suggestive of a glioblastoma were scanned at 3T 1-2 days prior to tumor resection using a 13C/ 1H dual-frequency RF coil and a 13C/ 1H-integrated MR protocol, which consists of a series of 1H MR sequences (T2 FLAIR, arterial spin labeling and contrast-enhanced (CE) T1) and 13C spectroscopic imaging with hyperpolarized [1- 13C]pyruvate. Dynamic spiral chemical shift imaging was used for 13C data acquisition. Surgical navigation was used to correlate the locations of tissue samples submitted for histology with the changes seen on the diagnostic MR scans and the 13C spectroscopic images. Results Each tumor was histologically confirmed to be a WHO grade IV glioblastoma with isocitrate dehydrogenase wild type. Total hyperpolarized 13C signals detected near the tumor mass reflected altered tissue perfusion near the tumor. For each tumor, a hyperintense [1- 13C]lactate signal was detected both within CE and T2-FLAIR regions on the 1H diagnostic images (p = 0.008). [ 13C]Bicarbonate signal was maintained or decreased in the lesion but the observation was not significant (p = 0.3). Conclusions Prior to surgical resection, 13C MR spectroscopic imaging with hyperpolarized pyruvate reveals increased lactate production in regions of histologically confirmed glioblastoma.