Tensor image enhancement and optimal multichannel receiver combination analyses for human hyperpolarized 13 C MRSI.

PURPOSE: With the initiation of human hyperpolarized 13 C (HP-13 C) trials at multiple sites and the development of improved acquisition methods, there is an imminent need to maximally extract diagnostic information to facilitate clinical interpretation. This study aims to improve human HP-13 C MR spectroscopic imaging through means of Tensor Rank truncation-Image enhancement (TRI) and optimal receiver combination (ORC). METHODS: A data-driven processing framework for dynamic HP 13 C MR spectroscopic imaging (MRSI) was developed. Using patient data sets acquired with both multichannel arrays and single-element receivers from the brain, abdomen, and pelvis, we examined the theory and application of TRI, as well as 2 ORC techniques: whitened singular value decomposition (WSVD) and first-point phasing. Optimal conditions for TRI were derived based on bias-variance trade-off. RESULTS: TRI and ORC techniques together provided a 63-fold mean apparent signal-to-noise ratio (aSNR) gain for receiver arrays and a 31-fold gain for single-element configurations, which particularly improved quantification of the lower-SNR-[13 C]bicarbonate and [1-13 C]alanine signals that were otherwise not detectable in many cases. Substantial SNR enhancements were observed for data sets that were acquired even with suboptimal experimental conditions, including delayed (114 s) injection (8× aSNR gain solely by TRI), or from challenging anatomy or geometry, as in the case of a pediatric patient with brainstem tumor (597× using combined TRI and WSVD). Improved correlation between elevated pyruvate-to-lactate conversion, biopsy-confirmed cancer, and mp-MRI lesions demonstrated that TRI recovered quantitative diagnostic information. CONCLUSION: Overall, this combined approach was effective across imaging targets and receiver configurations and could greatly benefit ongoing and future HP 13 C MRI research through major aSNR improvements.

Comparison between 8- and 32-channel phased-array receive coils for in vivo hyperpolarized 13 C imaging of the human brain.

PURPOSE: To compare the performance of an 8-channel surface coil/clamshell transmitter and 32-channel head array coil/birdcage transmitter for hyperpolarized 13 C brain metabolic imaging. METHODS: To determine the field homogeneity of the radiofrequency transmitters, B1 + mapping was performed on an ethylene glycol head phantom and evaluated by means of the double angle method. Using a 3D echo-planar imaging sequence, coil sensitivity and noise-only phantom data were acquired with the 8- and 32-channel receiver arrays, and compared against data from the birdcage in transceiver mode. Multislice frequency-specific 13 C dynamic echo-planar imaging was performed on a patient with a brain tumor for each hardware configuration following injection of hyperpolarized [1-13 C]pyruvate. Signal-to-noise ratio (SNR) was evaluated from pre-whitened phantom and temporally summed patient data after coil combination based on optimal weights. RESULTS: The birdcage transmitter produced more uniform B1 + compared with the clamshell: 0.07 versus 0.12 (fractional error). Phantom experiments conducted with matched lateral housing separation demonstrated 8- versus 32-channel mean transceiver-normalized SNR performance: 0.91 versus 0.97 at the head center; 6.67 versus 2.08 on the sides; 0.66 versus 2.73 at the anterior; and 0.67 versus 3.17 on the posterior aspect. While the 8-channel receiver array showed SNR benefits along lateral aspects, the 32-channel array exhibited greater coverage and a more uniform coil-combined profile. Temporally summed, parameter-normalized patient data showed SNRmean,slice ratios (8-channel/32-channel) ranging 0.5-2.00 from apical to central brain. White matter lactate-to-pyruvate ratios were conserved across hardware: 0.45 ± 0.12 (8-channel) versus 0.43 ± 0.14 (32-channel). CONCLUSION: The 8- and 32-channel hardware configurations each have advantages in particular brain anatomy.

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.

The "Ecosystem as a Service (EaaS)" approach to advance clinical artificial intelligence (cAI).

The application of machine learning and artificial intelligence to clinical settings for prevention, diagnosis, treatment, and the improvement of clinical care have been demonstrably cost-effective. However, current clinical AI (cAI) support tools are predominantly created by non-domain experts and algorithms available in the market have been criticized for the lack of transparency behind their creation. To combat these challenges, the Massachusetts Institute of Technology Critical Data (MIT-CD) consortium, an affiliation of research labs, organizations, and individuals that contribute to research in and around data that has a critical impact on human health, has iteratively developed the "Ecosystem as a Service (EaaS)" approach, providing a transparent education and accountability platform for clinical and technical experts to collaborate and advance cAI. The EaaS approach provides a range of resources, from open-source databases and specialized human resources to networking and collaborative opportunities. While mass deployment of the ecosystem still faces several hurdles, here we discuss our initial implementation efforts. We hope this will promote further exploration and expansion of the EaaS approach, while also informing or realizing policies that will accelerate multinational, multidisciplinary, and multisectoral collaborations in cAI research and development, and provide localized clinical best practices for equitable healthcare access.