An Open-Source, Reproducible MRI Data Acquisition and Reconstruction Workflow Using Pulseq: Multi-Site and Cross-Vendor Validation.

BART Online Open-Source Sequence Toolbox for Computational MRI

This work enables reproducibility of advanced computational MRI methods within a comprehensive end-to-end open-source framework and proves that quantitative MRI methods consisting of acquisition and reconstruction were successfully implemented in BART.

Clinical Implementation and Validation of Automated Slice-to-Volume Reconstruction for Fetal MRI Using a Vendor-Neutral MONAI-Based Imaging Informatics Pipeline.

This study aims to evaluate the clinical performance and operational reliability of a fully automated, vendor-neutral imaging informatics pipeline for fetal MRI slice-to-volume reconstruction using open-source Medical Open Network for Artificial Intelligence (MONAI) Deploy Express, DICOM routing, and PACS return. In this retrospective implementation study, consecutive singleton fetal MRI examinations performed at a tertiary fetal care center between October and December 2025 were processed using a MONAI Deploy Express pipeline triggered by DICOM routing. The pipeline performed automated series selection and motion-corrected SVR reconstruction for fetal brain and body imaging using routine single-shot fast spin-echo inputs. Reconstructed volumes were returned to PACS for radiologist review. Three fellowship-trained fetal radiologists independently scored reconstruction quality using a 5-point Likert scale. Primary outcomes included examination-level reconstruction acceptability, inter-reader reliability, failure modes, and operational time to PACS availability after transition to the finalized production workflow. Among 67 triggered examinations, 2 represented technical workflow events and were excluded from clinical performance analysis, leaving 65 examinations for image-quality evaluation. Mean gestational age was 28.9 weeks (range, 19-36 weeks) and most fetuses had structural abnormalities. Brain SVR was rated pass-or-higher in 59/65 examinations (90.8%) and body SVR was rated pass-or-higher in 57/65 examinations (87.7%). Inter-reader reliability was excellent for brain (ICC, 0.93) and body (ICC, 0.92) reconstruction quality assessments. Reconstruction failures were primarily associated with suboptimal or insufficient input stacks, severe fetal motion, amniotic fluid extremes, complex anatomic distortions, and early gestational age. In 57 consecutively processed examinations after finalized production integration, median time to PACS availability was 10 min 28 s for brain SVR and 11 min 50 s for body SVR. Automated fetal MRI SVR was operationally feasible using a DICOM-based, vendor-neutral deployment architecture integrated with routine PACS workflow. In this single-center, single-vendor cohort, the pipeline returned radiologist-acceptable reconstructions within a clinically practical timeframe. Multi-vendor and comparative reader studies are needed to establish geometric accuracy, generalizability, and diagnostic benefit.

Intra-vendor inter-scanner agreement of negative-HU measurements on fast kV-switching dual-energy CT virtual monoenergetic imaging: a phantom study.

A commissioning workflow for quantitative ¹⁷⁷Lu SPECT/CT across different Siemens scanner–reconstruction configurations

The accuracy of quantitative lutetium-177 single-photon emission computed tomography/computed tomography (¹⁷⁷Lu SPECT/CT) depends on the imaging system, acquisition protocol, reconstruction algorithm, and calibration strategy. This study aimed to develop and evaluate a practical commissioning workflow to optimize ¹⁷⁷Lu imaging across different commercial scanner–reconstruction configurations. Three Siemens workflows were evaluated: FLASH3D and xSPECT Quant on the Symbia Intevo Bold, and FLASH3D+ on the Symbia Pro.specta. A uniform phantom was used to assess image noise and derive calibration factors for the ordered-subset expectation maximization (OSEM)-based reconstructions. Recovery coefficients (RCs) were evaluated via an International Electrotechnical Commission/National Electrical Manufacturers Association phantom at various sphere-to-background ratios and modeled using a three-parameter logistic function for partial volume correction. Reconstruction parameters were optimized by jointly evaluating RC convergence and image noise, followed by quantitative validation using an anthropomorphic phantom. OSEM-based workflows showed a predictable dependence on equivalent iterations, yielding lower image noise. Conversely, xSPECT Quant exhibited complex parameter dependence and higher noise, but provided superior activity recovery—with RCs closer to unity—and the lowest quantification errors during validation. A reconstruction setting of two subsets and 30 iterations was selected for all workflows. For FLASH3D and FLASH3D+, this configuration provided RCs closest to unity while maintaining the coefficient of variation below the predefined 15% threshold. The same setting was adopted for xSPECT Quant to ensure methodological consistency despite its intrinsically higher image noise. This commissioning workflow provides a practical framework for local ¹⁷⁷Lu SPECT/CT optimization. Among the configurations, xSPECT Quant yielded higher recovery coefficients and lower quantification errors, albeit with increased noise. These findings highlight the need for locally optimized protocols for patient-specific dosimetry and do not establish the general superiority of any one reconstruction algorithm.

Field-Correcting GRAPPA (FCG): a technique to correct spatiotemporal-varying phase errors in Echo Planar Imaging

Purpose To develop a Field-Correcting GRAPPA (FCG) technique to correct the spatiotemporal-varying phase errors in EPI caused by eddy currents. Methods The fast-changing gradient in EPI causes strong eddy current effects and associated spatiotemporal-varying phase errors, producing significant image artifacts. The use of higher gradient amplitude, slew rate, and ramp sampling factor for faster imaging exacerbates this problem. In this work, FCG was developed to address this challenge by using a multi-layer perceptron (MLP) to provide a compact representation of a family of GRAPPA-like kernels that correct the spatiotemporal-varying phase errors in the data. A dedicated calibration pipeline was designed to acquire high-quality source and target data for MLP training in both slice-by-slice and simultaneous multi-slice (SMS) acquisitions. To validate FCG’s assumptions and performance, a field camera was used to provide ground-truth measurement of phase patterns. The performance of FCG was further validated on phantom and in vivo experiments using demanding EPI trajectories across multiple 3T and 7T systems. Results Field camera measurements revealed strong spatiotemporal phase variations along the kx direction that repeat along ky during EPI readouts. The experiments on high-performance systems across 3T and 7T demonstrate that FCG can provide superior correction for the artifacts induced by spatiotemporal-varying phase errors compared with existing approaches. Conclusion FCG is an effective and robust method for correcting spatiotemporal phase errors in EPI, enabling improved image quality on high-performance systems.

PRIME-SVR: Physics-infoRmed Implicit Multi-Echo Slice-to-Volume Reconstruction for Fetal T2 mapping

Slice-to-volume reconstruction (SVR) is the standard method for obtaining high-resolution (HR) 3D fetal brain volumes from motion-corrupted 2D MRI slice stacks acquired in multiple orientations. Existing SVR methods are optimized and validated only for clinical-range echo times (TEs), limiting their use at non-clinical TEs and making them incompatible with quantitative T2 mapping, a protocol- and center-independent biomarker of fetal brain maturation requiring HR reconstructions across multiple TEs. We present PRIME-SVR, the first implicit neural representation (INR) framework for joint HR reconstruction from multi-echo MRI. A single fully connected network models a continuous function from spatial coordinates to signal intensities across TEs, while a second network estimates slice-specific acquisition degradations. Cross-TE coherence is enforced via a Bloch equation-derived regularization penalizing deviations from expected T2 decay, with adaptive weighting that strengthens coupling for degraded stacks. The method is fully self-supervised. We validate PRIME-SVR on 39 in vivo fetal acquisitions (13 subjects x 3 TEs) from two centers, two vendors, and two field strengths (1.5 T and 0.55 T). Compared to state-of-the-art SVR, PRIME-SVR improves reconstruction sharpness by 47%, anatomical accuracy by 30%, and cross-TE structural consistency by 14%. It enables reconstruction at late TEs previously inaccessible to SVR, yielding the first 0.8 mm isotropic T2 maps at 0.55 T and the first T2 maps derived from INR-based SVR. PRIME-SVR also accelerates quantitative imaging by reducing the data needed for multi-TE reconstruction, cutting acquisition from 15 to 10 minutes while keeping T2 accuracy within 1.7% in white and deep gray matter, or to 5 minutes with a mean T2 error of 2.3% for high-quality acquisitions.