Výsledky vyhledávání - "GRISWOLD, MARK A."

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3D MR Fingerprinting for Dynamic Contrast-Enhanced Imaging of Whole Mouse Brain

Autor: Zhu, Yuran, Wang, Guanhua, Gu, Yuning, Zhao, Walter, Lu, Jiahao, Zhu, Junqing, MacAskill, Christina J., Dupuis, Andrew, Griswold, Mark A., Ma, Dan, Flask, Chris A., Yu, Xin

Quantitative MRI enables direct quantification of contrast agent concentrations in contrast-enhanced scans. However, the lengthy scan times required by conventional methods are inadequate for tracking contrast agent transport dynamically in mouse bra

Externí odkaz: http://arxiv.org/abs/2405.00513

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Datacenter Ethernet and RDMA: Issues at Hyperscale

Autor: Hoefler, Torsten, Roweth, Duncan, Underwood, Keith, Alverson, Bob, Griswold, Mark, Tabatabaee, Vahid, Kalkunte, Mohan, Anubolu, Surendra, Shen, Siyuan, Kabbani, Abdul, McLaren, Moray, Scott, Steve

We observe that emerging artificial intelligence, high-performance computing, and storage workloads pose new challenges for large-scale datacenter networking. RDMA over Converged Ethernet (RoCE) was an attempt to adopt modern Remote Direct Memory Acc

Externí odkaz: http://arxiv.org/abs/2302.03337

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Automated Design of Pulse Sequences for Magnetic Resonance Fingerprinting using Physics-Inspired Optimization

Autor: Jordan, Stephen P., Hu, Siyuan, Rozada, Ignacio, McGivney, Debra F., Boyacioglu, Rasim, Jacob, Darryl C., Huang, Sherry, Beverland, Michael, Katzgraber, Helmut G., Troyer, Matthias, Griswold, Mark A., Ma, Dan

Publikováno v: PNAS 118(40):e2020516118, 2021

Magnetic Resonance Fingerprinting (MRF) is a method to extract quantitative tissue properties such as T1 and T2 relaxation rates from arbitrary pulse sequences using conventional magnetic resonance imaging hardware. MRF pulse sequences have thousands

Externí odkaz: http://arxiv.org/abs/2106.04740

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A Fast MR Fingerprinting Simulator for Direct Error Estimation and Sequence Optimization

Autor: Hu, Siyuan, Jordan, Stephen, Boyacioglu, Rasim, Rozada, Ignacio, Troyer, Matthias, Griswold, Mark, McGivney, Debra, Ma, Dan

MR Fingerprinting is a novel quantitative MR technique that could simultaneously provide multiple tissue property maps. When optimizing MRF scans, modeling undersampling errors and field imperfections in cost functions will make the optimization resu

Externí odkaz: http://arxiv.org/abs/2105.11594

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Game of Learning Bloch Equation Simulations for MR Fingerprinting

Autor: Yang, Mingrui, Jiang, Yun, Ma, Dan, Mehta, Bhairav B., Griswold, Mark A.

Purpose: This work proposes a novel approach to efficiently generate MR fingerprints for MR fingerprinting (MRF) problems based on the unsupervised deep learning model generative adversarial networks (GAN). Methods: The GAN model is adopted and modif

Externí odkaz: http://arxiv.org/abs/2004.02270

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