GE Healthcare is an American multinational conglomerate incorporated in New York and headquartered in Chicago, Illinois. As of 2017, the company is a manufacturer and distributor of diagnostic imaging agents and radiopharmaceuticals for imaging modalities that are used in medical imaging procedures. The company offers dyes that are used in magnetic-resonance-imaging procedures. GE Healthcare also manufactures medical diagnostic equipment including CT image machines. Further, it develops Health technology for medical imaging and information technologies, medical diagnostics, patient monitoring systems, disease research, drug discovery, and biopharmaceutical manufacturing. The company was incorporated in 1994 and operates in more than 100 countries. GE Healthcare operates as a subsidiary of General Electric.
Neurovascular coupling (NVC) reflects the coordination between brain activity and cerebral blood flow, while glymphatic function indicates the capacity to clear metabolic waste from the brain. However, differences in these two factors between patients on peritoneal dialysis (PD) and hemodialysis (HD), as well as their interrelationship, remain unclear. Functional magnetic resonance imaging, three-dimensional pseudo-continuous arterial spin labeling, and diffusion tensor imaging were prospectively performed in 56 patients on PD, 54 patients on HD, and 52 healthy controls (HC). The study calculated the amplitude of low frequency fluctuation-cerebral blood flow (ALFF-CBF) coupling coefficient and the diffusion tensor image analysis along the perivascular space (DTI-ALPS) index, which respectively represent the overall NVC level and the glymphatic function. Compared to HC, patients on PD and HD exhibited lower ALFF-CBF coupling coefficients and DTI-ALPS indices, but there were no significant differences between the PD and HD groups. Additionally, positive correlations were observed between ALFF-CBF coupling coefficients and DTI-ALPS indices across all three groups. he degrees of neurovascular decoupling and altered glymphatic function are comparable between patients on PD and HD. Importantly, neurovascular decoupling may be associated with altered glymphatic function in patients on dialysis. The degrees of neurovascular decoupling and altered glymphatic function are comparable between patients on PD and HD. Importantly, neurovascular decoupling may be associated with altered glymphatic function in patients on dialysis.
To provide a thorough comparison of the SNR between sodium MRI k-space sampling schemes in the brain within clinically feasible time constraints (∼10 min) at 3 T. Density-adapted radial (DA-3DPR), constant-amplitude radial, Cartesian, FLORET, rotated spiral, and 3D cones trajectories were designed with parameters optimized for brain tissue SNR. The sequences were acquired in both a phantom and 13 healthy participants (age = 28.7 ± 3.4, M:F = 7:6). SNR was measured and corrected for point-spread function (PSF) volume and scan duration for a less-biased assessment. CSF-to-brain-tissue contrast and CNR were also measured. The data were linearly modeled, and ANOVA was used to determine if the sampling scheme contributed to the variance with the obtained metrics. The sampling schemes contributed significantly to the variance (p < 0.001) for all metrics. The DA-3DPR sampling scheme provided the highest SNR in both the phantom and the participants. The Cartesian sampling scheme had the highest absolute contrast, but the largest CNR was shared between the DA-3DPR, 3D cones, and FLORET sampling schemes. When considering the PSF and the requirement for a clinically feasible scan time, a 15 ms read-out DA-3DPR trajectory provides the highest SNR at 3 T, without losing any desired contrast.
In positron emission tomography (PET)/magnetic resonance imaging (MRI), attenuation correction (AC) for PET of the head is achieved by MRI data to generate pseudo-computed tomography (CT) images. However, for the torso, AC becomes more challenging due to the complexity of separating bone components. Additionally, generating accurate MRI-based CT using deep learning poses significant difficulties for the chest, primarily because perfectly paired MRI and CT training data are hard to obtain owing to respiratory motion and body movements. We previously demonstrated that MRI-to-CT conversion can be achieved without deformation, even using unsupervised learning for zero echo time (ZTE) MRI and CT data from different individuals. Building on this foundation, our study aims to apply this approach to AC in chest PET/MRI and assess their quantitative accuracy, reproducibility, and external validity. The datasets used included (1) training dataset (unpaired ZTE MRI and CT of PET/CT, n = 360 and 500, respectively); (2) test dataset (paired PET/MRI and PET/CT, n = 25 and 25, respectively); (3) repeatability assessment dataset (repeated PET/MRI, n = 15 × 2 scans for the same patient); and (4) external validation dataset (paired MRI component of PET/MRI and CT, n = 30 and 30, respectively, acquired at another institution). Unpaired training data were used to train the deep learning model of pseudo-CT generation from ZTE. The accuracy, repeatability, and reproducibility of the PET/MRI scans using ZTE- and deep learning-based AC (MRACZTE) were evaluated based on the similarity of the histograms and the mean standardized uptake value (SUVmean) of physiological background of bone and liver. The histogram correlation coefficients between MRACZTE and the AC map based on the CT (CTAC) for the spine were significantly higher than those between conventional AC (MRACDixon) and CTAC. Additionally, bone SUVmean obtained using MRACZTE showed reduced bias relative to CTAC compared with MRACDixon. This method proved to be reproducible on each patient level and robust against external validation. Unsupervised learning with unpaired ZTE and CT data enabled pseudo-CT generation with bone components that closely matched CT-based attenuation maps. Integration into MR-based attenuation correction resulted in stable physiological uptake measurements in chest PET/MRI, supporting the feasibility of this approach.
Magnetic Resonance Imaging is a critical imaging modality in clinical diagnosis and research, yet its complexity and heterogeneity hinder scalable, generalizable machine learning. Although foundation models have revolutionized language and vision tasks, their application to MRI remains constrained by data scarcity and narrow anatomical focus. We present Decipher-MR, a 3D MRI-specific vision-language foundation model trained on 200,000 MRI series from over 22,000 studies spanning diverse anatomical regions, sequences, and pathologies. Decipher-MR integrates self-supervised vision learning with report-guided text supervision to build robust representations for broad applications. To enable efficient use, Decipher-MR supports a modular design that enables tuning of lightweight, task-specific decoders attached to a frozen pretrained encoder. Following this setting, we evaluate Decipher-MR across disease classification, demographic prediction, anatomical localization, and cross-modal retrieval, demonstrating consistent improvements over existing foundation models and task-specific approaches. These results support Decipher-MR as a promising and reusable foundation for MRI-based AI, within the scope of the tasks and datasets evaluated.
Magnetic Resonance Fingerprinting (MRF) is a time-efficient approach to quantitative MRI, enabling the mapping of multiple tissue properties from a single, accelerated scan. However, achieving accurate reconstructions remains challenging, particularly in highly accelerated and undersampled acquisitions, which are crucial for reducing scan times. While deep learning techniques have advanced image reconstruction, the recent introduction of diffusion models offers newpossibilities for imaging tasks and their application in the medical field. Notably, diffusion models have only recently been explored for quantitative MRI and remain largely unstudied for MRF reconstruction. In this work, we propose a conditional diffusion model to reconstruct MRF data, demonstrating its potential for accurate quantitative MRI from accelerated acquisitions. Our findings are supported by qualitative and quantitative comparisons on in-vivo brain scan data, demonstrating that the proposed approach can outperform established deep learning and classical compressed sensing algorithms for MRF reconstruction in highly accelerated regimes. In our experiments, our approach achieves reductions in mean percentage errors of at least 0.71% and 2.15% for T1 and T2 reconstructions, respectively, at an acceleration factor of R=5. A range of ablation studies also explore strategies to improve computational efficiency of our approach.
