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The reproducibility of scientific reports is crucial to advancing human knowledge. This paper is a summary of our experience in replicating a balanced SSFP half‐radial dual‐echo imaging technique (bSTAR) using open‐source frameworks as a response to the 2023 ISMRM “repeat it with me” Challenge.

We replicated the bSTAR technique for thoracic imaging at 0.55T. The bSTAR pulse sequence is implemented in Pulseq, a vendor neutral open‐source rapid sequence prototyping environment. Image reconstruction is performed with the open‐source Berkeley Advanced Reconstruction Toolbox (BART). The replication of bSTAR, termed open‐source bSTAR, is tested by replicating several figures from the published literature. Original bSTAR, using the pulse sequence and image reconstruction developed by the original authors, and open‐source bSTAR, with pulse sequence and image reconstruction developed in this work, were performed in healthy volunteers.

Both echo images obtained from open‐source bSTAR contain no visible artifacts and show identical spatial resolution and image quality to those in the published literature. A direct head‐to‐head comparison between open‐source bSTAR and original bSTAR on a healthy volunteer indicates that open‐source bSTAR provides adequate SNR, spatial resolution, level of artifacts, and conspicuity of pulmonary vessels comparable to original bSTAR.

We have successfully replicated bSTAR lung imaging at 0.55T using two open‐source frameworks. Full replication of a research method solely relying on information on a research paper is unfortunately rare in research, but our success gives greater confidence that a research methodology can be indeed replicated as described.

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To develop and evaluate a fast and effective method for deblurring spiral real‐time MRI (RT‐MRI) using convolutional neural networks.

We demonstrate a 3‐layer residual convolutional neural networks to correct image domain off‐resonance artifacts in speech production spiral RT‐MRI without the knowledge of field maps. The architecture is motivated by the traditional deblurring approaches. Spatially varying off‐resonance blur is synthetically generated by using discrete object approximation and field maps with data augmentation from a large database of 2D human speech production RT‐MRI. The effect of off‐resonance range, shift‐invariance of blur, and readout durations on deblurring performance are investigated. The proposed method is validated using synthetic and real data with longer readouts, quantitatively using image quality metrics and qualitatively via visual inspection, and with a comparison to conventional deblurring methods.

Deblurring performance was found superior to a current autocalibrated method for in vivo data and only slightly worse than an ideal reconstruction with perfect knowledge of the field map for synthetic test data. Convolutional neural networks deblurring made it possible to visualize articulator boundaries with readouts up to 8 ms at 1.5 T, which is 3‐fold longer than the current standard practice. The computation time was 12.3 ± 2.2 ms per frame, enabling low‐latency processing for RT‐MRI applications.

Convolutional neural networks deblurring is a practical, efficient, and field map‐free approach for the deblurring of spiral RT‐MRI. In the context of speech production imaging, this can enable 1.7‐fold improvement in scan efficiency and the use of spiral readouts at higher field strengths such as 3 T.

To provide 3D real‐time MRI of speech production with improved spatio‐temporal sharpness using randomized, variable‐density, stack‐of‐spiral sampling combined with a 3D spatio‐temporally constrained reconstruction.

We evaluated five candidate (k,t) sampling strategies using a previously proposed gradient‐echo stack‐of‐spiral sequence and a 3D constrained reconstruction with spatial and temporal penalties. Regularization parameters were chosen by expert readers based on qualitative assessment. We experimentally determined the effect of spiral angle increment andk_ztemporal order. The strategy yielding highest image quality was chosen as the proposed method. We evaluated the proposed and original 3D real‐time MRI methods in 2 healthy subjects performing speech production tasks that invoke rapid movements of articulators seen in multiple planes, using interleaved 2D real‐time MRI as the reference. We quantitatively evaluated tongue boundary sharpness in three locations at two speech rates.

The proposed data‐sampling scheme uses a golden‐angle spiral increment in thek_x−k_yplane and variable‐density, randomized encoding alongk_z. It provided a statistically significant improvement in tongue boundary sharpness score (P < .001) in the blade, body, and root of the tongue during normal and 1.5‐times speeded speech. Qualitative improvements were substantial during natural speech tasks of alternating high, low tongue postures during vowels. The proposed method was also able to capture complex tongue shapes during fast alveolar consonant segments. Furthermore, the proposed scheme allows flexible retrospective selection of temporal resolution.

We have demonstrated improved 3D real‐time MRI of speech production using randomized, variable‐density, stack‐of‐spiral sampling with a 3D spatio‐temporally constrained reconstruction.

To mitigate a common artifact in spiral real‐time MRI, caused by aliasing of signal outside the desired FOV. This artifact frequently occurs in midsagittal speech real‐time MRI.

Simulations were performed to determine the likely origin of the artifact. Two methods to mitigate the artifact are proposed. The first approach, denoted as “large FOV” (LF), keeps an FOV that is large enough to include the artifact signal source during reconstruction. The second approach, denoted as “estimation‐subtraction” (ES), estimates the artifact signal source before subtracting a synthetic signal representing that source in multicoil k‐space raw data. Twenty‐five midsagittal speech‐production real‐time MRI data sets were used to evaluate both of the proposed methods. Reconstructions without and with corrections were evaluated by two expert readers using a 5‐level Likert scale assessing artifact severity. Reconstruction time was also compared.

The origin of the artifact was found to be a combination of gradient nonlinearity and imperfect anti‐aliasing in spiral sampling. The LF and ES methods were both able to substantially reduce the artifact, with an averaged qualitative score improvement of 1.25 and 1.35 Likert levels for LF correction and ES correction, respectively. Average reconstruction time without correction, with LF correction, and with ES correction were 160.69 ± 1.56, 526.43 ± 5.17, and 171.47 ± 1.71 ms/frame.

Both proposed methods were able to reduce the spiral aliasing artifacts, with the ES‐reduction method being more effective and more time efficient.

To improve the depiction and tracking of vocal tract articulators in spiral real‐time MRI (RT‐MRI) of speech production by estimating and correcting for dynamic changes in off‐resonance.

The proposed method computes a dynamic field map from the phase of single‐TE dynamic images after a coil phase compensation where complex coil sensitivity maps are estimated from the single‐TE dynamic scan itself. This method is tested using simulations and in vivo data. The depiction of air–tissue boundaries is evaluated quantitatively using a sharpness metric and visual inspection.

Simulations demonstrate that the proposed method provides robust off‐resonance correction for spiral readout durations up to 5 ms at 1.5T. In ‐vivo experiments during human speech production demonstrate that image sharpness is improved in a majority of data sets at air–tissue boundaries including the upper lip, hard palate, soft palate, and tongue boundaries, whereas the lower lip shows little improvement in the edge sharpness after correction.

Dynamic off‐resonance correction is feasible from single‐TE spiral RT‐MRI data, and provides a practical performance improvement in articulator sharpness when applied to speech production imaging.