Cross-scanner and cross-protocol variability of diffusion magnetic resonance imaging (dMRI) data are known to be major obstacles in multi-site clinical studies since they limit the ability to aggregate dMRI data and derived measures. Computational algorithms that harmonize the data and minimize such variability are critical to reliably combine datasets acquired from different scanners and/or protocols, thus improving the statistical power and sensitivity of multi-site studies. Different computational approaches have been proposed to harmonize diffusion MRI data or remove scanner-specific differences. To date, these methods have mostly been developed for or evaluated on single b-value diffusion MRI data. In this work, we present the evaluation results of 19 algorithms that are developed to harmonize the cross-scanner and cross-protocol variability of multi-shell diffusion MRI using a benchmark database. The proposed algorithms rely on various signal representation approaches and computational tools, such as rotational invariant spherical harmonics, deep neural networks and hybrid biophysical and statistical approaches. The benchmark database consists of data acquired from the same subjects on two scanners with different maximum gradient strength (80 and 300 ​mT/m) and with two protocols. We evaluated the performance of these algorithms for mapping multi-shell diffusion MRI data across scanners and across protocols using several state-of-the-art imaging measures. The results show that data harmonization algorithms can reduce the cross-scanner and cross-protocol variabilities to a similar level as scan-rescan variability using the same scanner and protocol. In particular, the LinearRISH algorithm based on adaptive linear mapping of rotational invariant spherical harmonics features yields the lowest variability for our data in predicting the fractional anisotropy (FA), mean diffusivity (MD), mean kurtosis (MK) and the rotationally invariant spherical harmonic (RISH) features. But other algorithms, such as DIAMOND, SHResNet, DIQT, CMResNet show further improvement in harmonizing the return-to-origin probability (RTOP). The performance of different approaches provides useful guidelines on data harmonization in future multi-site studies.

Objective: To investigatecardiac activation maps estimated using electrocardiographic imaging and to find methods reducing line-of-block (LoB) artifacts, while preserving real LoBs. Methods: Body surface potentials were computed for 137 simulated ventricular excitations. Subsequently, the inverse problem was solved to obtain extracellular potentials (EP) and transmembrane voltages (TMV). From these, activation times (AT) were estimated using four methods and compared to the ground truth. This process was evaluated with two cardiac mesh resolutions. Factors contributing to LoB artifacts were identified by analyzing the impact of spatial and temporal smoothing on the morphology of source signals. Results: AT estimation using a spatiotemporal derivative performed better than using a temporal derivative. Compared to deflection-based AT estimation, correlation-based methods were less prone to LoB artifacts but performed worse in identifying real LoBs. Temporal smoothing could eliminate artifacts for TMVs but not for EPs, which could be linked to their temporal morphology. TMVs led to more accurate ATs on the septum than EPs. Mesh resolution had anegligible effect on inverse reconstructions, but small distances were important for cross-correlation-based estimation of AT delays. Conclusion: LoB artifacts are mainly caused by the inherent spatial smoothing effect of the inverse reconstruction. Among the configurations evaluated, only deflection-based AT estimation in combination with TMVs and strong temporal smoothing can prevent LoB artifacts, while preserving real LoBs. Significance: Regions of slow conduction are of considerable clinical interest and LoB artifacts observed in non-invasive ATs can lead to misinterpretations. We addressed this problem by identifying factors causing such artifacts.

Purpose: To substantially shorten the acquisition time required for quantitative three‐dimensional (3D) chemical exchange saturation transfer (CEST) and semisolid magnetization transfer (MT) imaging and allow for rapid chemical exchange parameter map reconstruction.Methods: Three‐dimensional CEST and MT magnetic resonance fingerprinting (MRF) datasets of L‐arginine phantoms, whole‐brains, and calf muscles from healthy volunteers, cancer patients, and cardiac patients were acquired using 3T clinical scanners at three different sites, using three different scanner models and coils. A saturation transfer‐oriented generative adversarial network (GAN‐ST) supervised framework was then designed and trained to learn the mapping from a reduced input data space to the quantitative exchange parameter space, while preserving perceptual and quantitative content.Results: The GAN‐ST 3D acquisition time was 42–52 s, 70% shorter than CEST‐MRF. The quantitative reconstruction of the entire brain took 0.8 s. An excellent agreement was observed between the ground truth and GAN‐based L‐arginine concentration and pH values (Pearson's r > 0.95, ICC > 0.88, NRMSE < 3%). GAN‐ST images from a brain‐tumor subject yielded a semi‐solid volume fraction and exchange rate NRMSE of and , respectively, and SSIM of and , respectively. The mapping of the calf‐muscle exchange parameters in a cardiac patient, yielded NRMSE < 7% and SSIM > 94% for the semi‐solid exchange parameters. In regions with large susceptibility artifacts, GAN‐ST has demonstrated improved performance and reduced noise compared to MRF.Conclusion: GAN‐ST can substantially reduce the acquisition time for quantitative semi‐solid MT/CEST mapping, while retaining performance even when facing pathologies and scanner models that were not available during training.

Goal: To evaluate state-of-the-art signal processing methods for epicardial potential-based noninvasive electrocardiographic imaging reconstructions of single-site pacing data. Methods: Experimental data were obtained from two torso-tank setups in which Langendorff-perfused hearts (n = 4) were suspended and potentials recorded simultaneously from torso and epicardial surfaces. 49 different signal processing methods were applied to torso potentials, grouped as i) high-frequency noise removal (HFR) methods ii) baseline drift removal (BDR) methods and iii) combined HFR+BDR. The inverse problem was solved and reconstructed electrograms and activation maps compared to those directly recorded. Results: HFR showed no difference compared to not filtering in terms of absolute differences in reconstructed electrogram amplitudes nor median correlation in QRS waveforms (p > 0.05). However, correlation and mean absolute error of activation times and pacing site localization were improved with all methods except a notch filter. HFR applied post-reconstruction produced no differences compared to pre-reconstruction. BDR and BDR+HFR significantly improved absolute and relative difference, and correlation in electrograms (p <; 0.05). While BDR+HFR combined improved activation time and pacing site detection, BDR alone produced significantly lower correlation and higher localization errors (p <; 0.05). Conclusion: BDR improves reconstructed electrogram morphologies and amplitudes due to a reduction in lambda value selected for the inverse problem. The simplest method (resetting the isoelectric point) is sufficient to see these improvements. HFR does not impact electrogram accuracy, but does impact post-processing to extract features such as activation times. Removal of line noise is insufficient to see these changes. HFR should be applied post-reconstruction to ensure over-filtering does not occur.

Background: Late gadolinium enhancement cardiac magnetic resonance imaging is an effective and reproducible method for characterizing myocardial infarction. However, gadolinium‐based contrast agents are contraindicated in patients with acute and chronic renal insufficiency. In addition, several recent studies have noted tissue deposition of free gadolinium in patients who have undergone serial contrast‐enhanced magnetic resonance imaging. There is a clinical need for alternative forms of magnetic resonance imaging contrast agents that are acceptable in the setting of renal insufficiency. Methods and Results: Three days after 80 minutes of ischemia/reperfusion of the left anterior descending coronary artery, cardiac magnetic resonance imaging was performed to assess myocardial lesion burden using both contrast agents. Late gadolinium enhancement cardiac magnetic resonance imaging was examined 10 and 15 minutes after contrast injection. Contrast agents were administered in alternating manner with a 2‐ to 3‐hour washout period between contrast agent injections. Lesion evaluation and image processing were performed using Segment Medviso software. Mean infarct size and transmurality, measured using RVP‐001, were not different compared with those measured using late gadolinium enhancement images. Bland‐Altman analysis demonstrated a nominal bias of 0.13 mL (<1% of average total lesion volume) for RVP‐001 in terms of gross infarct size measurement. Conclusions: The experimental manganese‐based contrast agent RVP‐001 appears to be an effective agent for assessment of myocardial infarction location, size, and transmurality, and it may be useful as an alternative to gadolinium‐based agents.