Sen Jia’s scientific contributions

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Publications (2)


Diff5T: Benchmarking Human Brain Diffusion MRI with an Extensive 5.0 Tesla K-Space and Spatial Dataset
  • Preprint

December 2024

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40 Reads

Shanshan Wang

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Shoujun Yu

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Jian Cheng

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[...]

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Hairong Zheng

Diffusion magnetic resonance imaging (dMRI) provides critical insights into the microstructural and connectional organization of the human brain. However, the availability of high-field, open-access datasets that include raw k-space data for advanced research remains limited. To address this gap, we introduce Diff5T, a first comprehensive 5.0 Tesla diffusion MRI dataset focusing on the human brain. This dataset includes raw k-space data and reconstructed diffusion images, acquired using a variety of imaging protocols. Diff5T is designed to support the development and benchmarking of innovative methods in artifact correction, image reconstruction, image preprocessing, diffusion modelling and tractography. The dataset features a wide range of diffusion parameters, including multiple b-values and gradient directions, allowing extensive research applications in studying human brain microstructure and connectivity. With its emphasis on open accessibility and detailed benchmarks, Diff5T serves as a valuable resource for advancing human brain mapping research using diffusion MRI, fostering reproducibility, and enabling collaboration across the neuroscience and medical imaging communities.


Figure 1. The schematic diagrams of the MRI RF coil. (A) Schematic of a traditional RF coil system. (B) Conceptual diagram of the NFC coil system. Some electronic components are omitted in the figures.
Figure 3. The impact of inter-element decoupling performance on the imaging SNR of NFC coils. (A) Poorly decoupled and well-decoupled NFC coils. (B) In the poorly decoupled NFC coil, the coupling coefficient between adjacent elements exceeds -10
Figure 4. Phantom-based MRI validations for the sensitivity combination of the NFC coil system. (A) The photos of the 6-element NFC coil (B) Axial plane images of a phantom using a 4-channel spine coil array and a 6-element NFC coil. P1-P4 represent images obtained using a single pickup coil element, while "combination" represents the image obtained using all four pickup coils. (C) Mutual inductance coefficients using 3D electromagnetic simulation software between each channel of the four pickup coils and the six NFC elements. (D) MRI images, measured SNR maps, and simulated B1-maps show the combined sensitivity and per-channel sensitivity distribution of the NFC coil system. SNR maps and B1-field have both been normalized to their maximum values. The dominant NFC elements shown in the images and maps are consistent with the dominant NFC elements listed in the mutual inductance coefficient table.
Figure 5. Comparison of the SNR performance of NFC coil system, its pickup coil, and a commercial knee coil. (A) The SNR maps obtained using the NFC coil system, its pickup coils and the knee coil. (B) The noise matrices used for calculating the signal-to-noise ratio (SNR). (C) The SNR comparisons along reference line 1 (Ref. line1) and reference line 2 (Ref. line2). The SNR of the images obtained along the reference line using NFC coil system improved by approximately 360% compared to the pickup coil and by about 60% compared to the knee coil. (D) The NFC coil and pickup coil used for acquiring the SNR map.
Figure 6. Comparison of the parallel imaging capabilities of NFC coil system, its pickup coil, and the knee coil. The figure shows the mean g-factor (highlighted in white, where a smaller g-factor corresponds to better acceleration performance) and the 1/g maps under the same acceleration settings.
Near-Field Coupling Coil System: A Novel Radiofrequency Coil Solution for MRI
  • Preprint
  • File available

September 2024

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188 Reads

The performance of radiofrequency (RF) coils has a significant impact on the quality and speed of magnetic resonance imaging (MRI). Consequently, rigid coils with attached cables are commonly employed to achieve optimal SNR performance and parallel imaging capability. However, since the adoption of MRI in clinical imaging, both patients and doctors have long suffered from the poor examination experience and physical strain caused by the bulky housings and cumbersome cables of traditional coils. This paper presents a new architectural concept, the Near-Field Coupling (NFC) coil system, which integrates a pickup coil array within the magnet with an NFC coil worn by the patient. In contrast to conventional coils, the NFC coil system obviates the necessity for bed-mounted connectors. It provides a lightweight, cost-effective solution that enhances patient comfort and supports disposable, custom designs for the NFC coils. The paper also derives the SNR expression for the NFC coil system, proposes two key design principles, and demonstrates the system's potential in SNR and parallel imaging through an implementation case.

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