Sharing research code in an open access version-controlled repository offers significant benefits for both science as a whole and for individual researchers. In this article, we focus on this practice, which is fully aligned with the NIH's Gold Standard Science (GSS) program as well as FAIR (findable, accessible, interoperable, reusable) and TRUST (transparency, responsibility, user focus, sustainability, technology) principles. Gold Standard Science supports open science by emphasizing transparency, reproducibility, and the use of best practices that enable others to verify and extend research. Pairing a research article's cited data snapshot with a versioned, environment-specific code release, deposited in a companion code repository, ensures that, upon submission to a medical journal, readers and reviewers can directly verify results. An executable and updatable companion code repository complements, rather than replaces, established research data repositories. When code underlying medical research results is made openly available, then other scientists can inspect, run, and validate analyses. These activities enhance reproducibility, which is a core aim of GSS. Shared code also facilitates collaborative innovation by allowing researchers to extend the utility of the code to new datasets and applications. For researchers, code sharing can increase visibility, credibility, and citation impact. Demonstrating transparency through shared executable and updatable code builds trust with journal readers, peer reviewers, funders, and peers. Shared code in an open access repository signals adherence to high standards of scientific integrity and attracts opportunities for collaboration. A researcher who shares code receives recognition as a leader in reproducible, trustworthy research consistent with NIH's GSS principles.

Introduction: An error grid compares measured versus reference glucose concentrations to assign clinical risk values to observed errors. Widely used error grids for blood glucose monitors (BGMs) have limited value because they do not also reflect clinical accuracy of continuous glucose monitors (CGMs). Methods: Diabetes Technology Society (DTS) convened 89 international experts in glucose monitoring to (1) smooth the borders of the Surveillance Error Grid (SEG) zones and create a user-friendly tool—the DTS Error Grid; (2) define five risk zones of clinical point accuracy (A-E) to be identical for BGMs and CGMs; (3) determine a relationship between DTS Error Grid percent in Zone A and mean absolute relative difference (MARD) from analyzing 22 BGM and nine CGM accuracy studies; and (4) create trend risk categories (1-5) for CGM trend accuracy. Results: The DTS Error Grid for point accuracy contains five risk zones (A-E) with straight-line borders that can be applied to both BGM and CGM accuracy data. In a data set combining point accuracy data from 18 BGMs, 2.6% of total data pairs equally moved from Zones A to B and vice versa (SEG compared with DTS Error Grid). For every 1% increase in percent data in Zone A, the MARD decreased by approximately 0.33%. We also created a DTS Trend Accuracy Matrix with five trend risk categories (1-5) for CGM-reported trend indicators compared with reference trends calculated from reference glucose. Conclusion: The DTS Error Grid combines contemporary clinician input regarding clinical point accuracy for BGMs and CGMs. The DTS Trend Accuracy Matrix assesses accuracy of CGM trend indicators.

With the initiation of human hyperpolarized 13C (HP‐13C) 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‐13C MR spectroscopic imaging through means of Tensor Rank truncation‐Image enhancement (TRI) and optimal receiver combination (ORC).

PurposeTo compare the performance of an 8-channel surface coil/clamshell transmitter and 32-channel head array coil/birdcage transmitter for hyperpolarized C-13 brain metabolic imaging. MethodsTo determine the field homogeneity of the radiofrequency transmitters, B-1+ 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 C-13 dynamic echo-planar imaging was performed on a patient with a brain tumor for each hardware configuration following injection of hyperpolarized [1-C-13]pyruvate. Signal-to-noise ratio (SNR) was evaluated from pre-whitened phantom and temporally summed patient data after coil combination based on optimal weights. ResultsThe birdcage transmitter produced more uniform B-1+ 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). ConclusionThe 8- and 32-channel hardware configurations each have advantages in particular brain anatomy.

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 cover image, by is based on the Research Article A comparison of coil combination strategies in 3D multi-channel MRSI reconstruction for patients with brain tumors by Maryam Vareth et al., https://doi.org/10.1002/nbm.3929.

Introduction: MR spectroscopic imaging (MRSI) is a powerful tool for diagnosis of brain tumors but long acquisition times cause patient discomfort and artifacts. The increasing availability of 7 Tesla MR scanners and multi-channel coil arrays allow for shorter acquisition times, higher spatial resolution, and larger spatial coverage for H-1 MRSI data, facilitating patient studies. Self-calibrating parallel imaging techniques such as GRAPPA, and SPIRiT, are particularly attractive for this application because coil sensitivity information is estimated from the data itself, but the dense sampling of the center of k-space required for accurate reconstruction is disadvantageous for MRSI where the typical 16x16 phase encodes allows for a 5x5 kernel. The purpose of this study was to develop a flexible platform on which to evaluate variable density sampling patterns and reconstruction strategies for accurate and robust metabolic imaging of the brain at 7T. Methods: Sampling and Reconstruction: The ability to acquire arbitrary, variable density sampling patterns was incorporated into our H-1 MRSI sequence to allow undersampling (US) of kspace along any phase encoding direction, with an option of including an interleaved flyback echo-planar trajectory. Sampling patterns were generated in MATLAB and played in the scanner. A modified SPIRiT reconstruction algorithm, a generalized form of GRAPPA with the ability to enforce an additional L1 constraint in order to take advantage of compressed sensing (L1SPRiT), was implemented in MATLAB such that the spectral dimension could be utilized in the estimation to improve the accuracy of weights from the restricted calibration region. Data Acquisition: Anatomic MR images and MRSI data were acquired on an MRS phantom and 2 human subjects using a 32-channel receive array with volume coil transmit on a GE 7 Tesla scanner. 2D H-1 MRSI was localized with CHESS water suppression, 8 VSS outer volume suppression, and spin echo slice selection, using a TE/TR=90/2000ms and spectra array=18x22. A B1-map was acquired to calculate the optimal transmit power for H-1 MRSI. Fully-sampled, water-suppressed (WS) and nonwater-suppressed (NWS) data were acquired in 13min and used as either a gold standard or to determine the optimal calibration for a 60% US scheme, respectively. The % US was then increased to 63,71,76, and 80%, corresponding to acquisition times of ~ 5, 4, 3 and 2min using the four variable density sampling patterns shown in Fig 1. The data from all 32 channels were combined and processed as described previously. Data Analyses: To evaluate the accuracy of each reduced k-space and calibration scheme, the fully sampled data was reconstructed as a gold standard and regression plots of all brain voxels were used to compare metabolite peak heights and ratios among different calibration regions and between each SPIRiT US scheme and full kspace data. The R value from each linear regression was then utilized as a measure of accuracy. Results: The reconstructions of fully-sampled spectra and SPIRiT for the 4 sampling patterns is shown in Fig 1 for 3 voxels at various locations in a phantom. The variable density pattern with more structure near the center and pseudo-random patterns outside performed best, tolerating a higher acceleration even in areas of lower SNR (Fig 1, bottom purple row). Although in the phantom, the difference between R values obtained from fully-sampled NWS and 5x5 calibration region compared to the original data were not different (.99 for peak heights and .90 for ratios for both calibrations), the choice of calibration impacted the performance of SPIRiT in the volunteer data, as shown in Fig 2, where the SPIRiT recon with 18x22 NWS acquisition serving as auto-calibrating signal (in blue) more closely reflects the fully sampled spectra (in white) than the center 5x5 calibration region (in pink). Significantly higher R values were obtained for all metabolic peak heights and ratios for the fully-sampled 18x22 NWS compared to the 5x5 SPIRiT calibration region. Utilizing the fully-sampled NWS calibration subsequently allowed for accelerations of up to 80% US without compromising reconstruction quality as shown by the R values in Fig 3.