Hepatic Portography by Direct Catheterization of the Portal Vein Through the Round Ligament of the Liver (Ligamentum Teres)

Chiandussi, L; Juliani, G; Greco, Franco; Cravero, D; Sardi, G; Toscano, Giuseppe

doi:10.2214/ajr.99.3.625

Cited by 10 publications

(1 citation statement)

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“…The liver has a dual-blood supply: one is from the hepatic artery and another from the portal vein. In the normal liver parenchyma, portal venous perfusion contributes to 70-80% of the total perfusion, and hepatic arterial perfusion about 20-30% (Chiandussi et al 1968). Diseases and tumors in the liver can alter both portal venous and hepatic arterial perfusion in the tissue (Hashimoto et al 2006, Leggett et al 1997, Van Beers et al 2001.…”

Section: Introductionmentioning

confidence: 99%

GPU-accelerated voxelwise hepatic perfusion quantification

Wang

Cao

2012

Phys. Med. Biol.

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Voxelwise quantification of hepatic perfusion parameters from dynamic contrast enhanced (DCE) imaging greatly contributes to assessment of liver function in response to radiation therapy. However, the efficiency of the estimation of hepatic perfusion parameters voxel-by-voxel in the whole liver using a dual-input single-compartment model requires substantial improvement for routine clinical applications. In this paper, we utilize the parallel computation power of a graphics processing unit (GPU) to accelerate the computation, while maintaining the same accuracy as the conventional method. Using CUDA-GPU, the hepatic perfusion computations over multiple voxels are run across the GPU blocks concurrently but independently. At each voxel, non-linear least squares fitting the time series of the liver DCE data to the compartmental model is distributed to multiple threads in a block, and the computations of different time points are performed simultaneously and synchronically. An efficient fast Fourier transform in a block is also developed for the convolution computation in the model. The GPU computations of the voxel-by-voxel hepatic perfusion images are compared with ones by the CPU using the simulated DCE data and the experimental DCE MR images from patients. The computation speed is improved by 30 times using a NVIDIA Tesla C2050 GPU compared to a 2.67 GHz Intel Xeon CPU processor. To obtain liver perfusion maps with 626400 voxels in a patient’s liver, it takes 0.9 min with the GPU-accelerated voxelwise computation, compared to 110 min with the CPU, while both methods result in perfusion parameters differences less than 10−6. The method will be useful for generating liver perfusion images in clinical settings.

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