Hyperpolarized 13C-pyruvate MRI detects real-time metabolic flux in prostate cancer metastases to bone and liver: a clinical feasibility study.

BackgroundHyperpolarized (HP) 13C-pyruvate MRI is a stable-isotope molecular imaging modality that provides real-time assessment of the rate of metabolism through glycolytic pathways in human prostate cancer. Heretofore this imaging modality has been successfully utilized in prostate cancer only in localized disease. This pilot clinical study investigated the feasibility and imaging performance of HP 13C-pyruvate MR metabolic imaging in prostate cancer patients with metastases to the bone and/or viscera.MethodsSix patients who had metastatic castration-resistant prostate cancer were recruited. Carbon-13 MR examination were conducted on a clinical 3T MRI following injection of 250 mM hyperpolarized 13C-pyruvate, where pyruvate-to-lactate conversion rate (kPL) was calculated. Paired metastatic tumor biopsy was performed with histopathological and RNA-seq analyses.ResultsWe observed a high rate of glycolytic metabolism in prostate cancer metastases, with a mean kPL value of 0.020 ± 0.006 (s-1) and 0.026 ± 0.000 (s-1) in bone (N = 4) and liver (N = 2) metastases, respectively. Overall, high kPL showed concordance with biopsy-confirmed high-grade prostate cancer including neuroendocrine differentiation in one case. Interval decrease of kPL from 0.026 at baseline to 0.015 (s-1) was observed in a liver metastasis 2 months after the initiation of taxane plus platinum chemotherapy. RNA-seq found higher levels of the lactate dehydrogenase isoform A (Ldha,15.7 ± 0.7) expression relative to the dominant isoform of pyruvate dehydrogenase (Pdha1, 12.8 ± 0.9).ConclusionsHP 13C-pyruvate MRI can detect real-time glycolytic metabolism within prostate cancer metastases, and can measure changes in quantitative kPL values following treatment response at early time points. This first feasibility study supports future clinical studies of HP 13C-pyruvate MRI in the setting of advanced prostate cancer

Current Methods for Hyperpolarized [1-13C]pyruvate MRI Human Studies

MRI with hyperpolarized (HP) 13C agents, also known as HP 13C MRI, can measure processes such as localized metabolism that is altered in numerous cancers, liver, heart, kidney diseases, and more. It has been translated into human studies during the past 10 years, with recent rapid growth in studies largely based on increasing availability of hyperpolarized agent preparation methods suitable for use in humans. This paper aims to capture the current successful practices for HP MRI human studies with [1-13C]pyruvate - by far the most commonly used agent, which sits at a key metabolic junction in glycolysis. The paper is divided into four major topic areas: (1) HP 13C-pyruvate preparation, (2) MRI system setup and calibrations, (3) data acquisition and image reconstruction, and (4) data analysis and quantification. In each area, we identified the key components for a successful study, summarized both published studies and current practices, and discuss evidence gaps, strengths, and limitations. This paper is the output of the HP 13C MRI Consensus Group as well as the ISMRM Hyperpolarized Media MR and Hyperpolarized Methods & Equipment study groups. It further aims to provide a comprehensive reference for future consensus building as the field continues to advance human studies with this metabolic imaging modality

Concentric rings K‐space trajectory for hyperpolarized 13C MR spectroscopic imaging

PurposeTo develop a robust and rapid imaging technique for hyperpolarized (13)C MR Spectroscopic Imaging and investigate its performance.MethodsA concentric rings readout trajectory with constant angular velocity is proposed for hyperpolarized (13)C spectroscopic imaging and its properties are analyzed. Quantitative analyses of design tradeoffs are presented for several imaging scenarios. The first application of concentric rings on (13)C phantoms and in vivo animal hyperpolarized (13)C MR Spectroscopic Imaging studies were performed to demonstrate the feasibility of the proposed method. Finally, a parallel imaging accelerated concentric rings study is presented.ResultsThe concentric rings MR Spectroscopic Imaging trajectory has the advantages of acquisition timesaving compared to echo-planar spectroscopic imaging. It provides sufficient spectral bandwidth with relatively high efficiency compared to echo-planar spectroscopic imaging and spiral techniques. Phantom and in vivo animal studies showed good image quality with half the scan time and reduced pulsatile flow artifacts compared to echo-planar spectroscopic imaging. Parallel imaging accelerated concentric rings showed advantages over Cartesian sampling in g-factor simulations and demonstrated aliasing-free image quality in a hyperpolarized (13)C in vivo study.ConclusionThe concentric rings trajectory is a robust and rapid imaging technique that fits very well with the speed, bandwidth, and resolution requirements of hyperpolarized (13)C MR Spectroscopic Imaging

A comparison of coil combination strategies in 3D multi‐channel MRSI reconstruction for patients with brain tumors

The goal of this study was to find the most robust algorithm for a phase-sensitive coil combination of 3D single-cycle and lactate-edited, multi-channel H-1 point-resolved spectroscopy (PRESS) localized echo planar spectroscopic imaging (EPSI) data for clinical applications in the brain. Data were acquired over 5-10 minutes at 3T using 8- or 32-channel array coils. Peak referencing with residual water and N-acetyl-aspartate, first-point phasing, generalized least squared (GLS) and whitened singular-value decomposition (WSVD) combination algorithms were evaluated relative to unsuppressed water with data from a phantom, six volunteers and 55 patients with brain tumors. Comparison metrics were signal-to-noise ratio, coefficient of variance and percent signal increase. Where residual water was present, using it as a reference peak for phasing and weighting factors from an imaging calibration scan gave the best overall performance. Greater improvement was seen for large selected volumes (>720 cm3 ) and for the 32-channel array (25%) compared with the 8-channel array (19%). Applying voxel-by-voxel phase corrections produced a larger increase in performance for the 32- versus 8-channel coil. We conclude that, for clinically relevant 3D H-1 PRESS localized EPSI studies, the most robust technique employed individual phase maps generated from high residual water and individual amplitude maps generated from calibration scans

Synthesizing Complex-Valued Multicoil MRI Data from Magnitude-Only Images

Despite the proliferation of deep learning techniques for accelerated MRI acquisition and enhanced image reconstruction, the construction of large and diverse MRI datasets continues to pose a barrier to effective clinical translation of these technologies. One major challenge is in collecting the MRI raw data (required for image reconstruction) from clinical scanning, as only magnitude images are typically saved and used for clinical assessment and diagnosis. The image phase and multi-channel RF coil information are not retained when magnitude-only images are saved in clinical imaging archives. Additionally, preprocessing used for data in clinical imaging can lead to biased results. While several groups have begun concerted efforts to collect large amounts of MRI raw data, current databases are limited in the diversity of anatomy, pathology, annotations, and acquisition types they contain. To address this, we present a method for synthesizing realistic MR data from magnitude-only data, allowing for the use of diverse data from clinical imaging archives in advanced MRI reconstruction development. Our method uses a conditional GAN-based framework to generate synthetic phase images from input magnitude images. We then applied ESPIRiT to derive RF coil sensitivity maps from fully sampled real data to generate multi-coil data. The synthetic data generation method was evaluated by comparing image reconstruction results from training Variational Networks either with real data or synthetic data. We demonstrate that the Variational Network trained on synthetic MRI data from our method, consisting of GAN-derived synthetic phase and multi-coil information, outperformed Variational Networks trained on data with synthetic phase generated using current state-of-the-art methods. Additionally, we demonstrate that the Variational Networks trained with synthetic k-space data from our method perform comparably to image reconstruction networks trained on undersampled real k-space data