BlochSolver is a fast 3D MRI simulator accelerated by graphics processor units (GPUs).

The structure of BlochSolver is shown below.

BlochSolver requires a numerical phantom, system parameter maps, and a pulse sequence.

The numerical phantom consists of a proton density map, T1 map, T2 map, diffusion coefficient map, etc.

System parameter maps consist of B0 map (inhomogeneity or resonance frequency map), B1 map (transmission and reception), gradient field map (nonlinearity).

The pulse  sequence can be developed using Python pulse sequence development kit (Python PSDK) described in this website.

 

The performance of BlochSolver depends on the kinds of pulse sequences. This is because the calculation time for a pulse sequence depends on the number of the total number of short pulses used for RF excitations and that of data acquisition points

 

Relative performance for a 3D RF spoiled gradient echo sequence:

The calculation time of RTX 3080  was 152.272 s.

 

Relative performance for a 2D multislice proton density weighted FSE sequence:

The calculation time of RTX 3080 was 158.319 s.

 

RTX 3080: 8704 cuda cores, RTX 2080Ti: 4352 cuda cores, GTX 1080Ti: 3584 cud cores, RTX 2070: 2560 cuda cores, GTX 1070: 1920 cuda cores, GTX 1050Ti: 768 cuda cores,  Xeon E5-2696 x 2: 36 cores, core i7 5960X: 8 cores, core i7 8750QH: 6 cores, core i7 7700HQ: 4 cores

 

 

         GPU (GTX 1080Ti)             GPU (RTX 2080Ti)              GPU (RTX 3080) New!

 

Gaming laptop PCs are useful devices for BlochSolver:

<Original papers>

  1. Kose R, Kose K. BlochSolver: A GPU-optimized fast 3D MRI simulator for experimentally compatible pulse sequences. J Magn Reson 281:51-65 (2017).
  2. Kose R, Setoi A, Kose K. A fast GPU-optimized 3D MRI simulator for arbitrary k-space sampling. Magn Reson Med Sci, Magn Reson Med Sci 18, 208-218 (2019).
  3. Kose R, Kose K. An Accurate Dictionary Creation Method for MR Fingerprinting Using a Fast Bloch Simulator. Magn Reson Med Sci, 19(3), 247-253 (2020).
  4. Kose R, Kose K, Terada Y, Tamada D, Motosugi U. Development of a method for the Bloch image simulation of biological tissues. Magn Reson Imaging. 74, 250-257 (2020).

<International conferences>

  1. K. Kose, R. Kose, T. Haishi, Development of a fast 3D MRI simulator using general-purpose graphic processor units. 13th International Conference on Magnetic Resonance Microscopy, Munich, August 2015, P-020.
  2. R. Kose, K. Kose, GPU optimized fast 3D MRI simulator. 24th Annual Meeting & Exhibition of the International Society of Magnetic Resonance in Medicine, Singapore, May 2016, 3202.
  3. R. Kose, K. Kose, Fast Bloch-Torrey simulation of 3D RF spoiled gradient echo sequences using a number of subvoxels and molecular diffusion effect. 25th Annual Meeting & Exhibition of International Society of Magnetic Resonance in Medicine, Honolulu, April 2017, 1465.
  4. R. Kose, K. Kose, GPU optimized fast Bloch simulator for arbitrary MRI pulse sequences. 25th Annual Meeting & Exhibition of International Society of Magnetic Resonance in Medicine, Honolulu, April 2017, 1509.
  5. R. Kose, A. Setoi. K. Kose, GPU-optimized 3D fast MRI simulator for non-Cartesian sampling. 14th International Conference on Magnetic Resonance Microscopy, Halifax, August 2017, P-29.
  6. R. Kose, A. Setoi, K. Kose, GPU-optimized fast 3D MRI simulator for arbitrary trajectory sampling. Joint Annual Meeting ISMRM-ESMRMB, Paris, June, 2018, 2706.
  7. K. Kose, R. Kose, An accurate dictionary generation method for MR fingerprinting using a fast Bloch image simulator. 27th Annual Meeting & Exhibition of International Society of Magnetic Resonance in Medicine, Montreal, May 2019, 4525.
  8. K. Kose, R. Kose, Bloch image simulations of brain pulse sequences using a GPU-installed gaming PC. 27th Annual Meeting & Exhibition of International Society of Magnetic Resonance in Medicine, Montreal, May 2019, 4829.