- Workshop presentation
- Open Access
Scanner-efficient diffusion tensor imaging of human cardiac microstructure using the fast composite splitting reconstruction algorithm
© Giannakidis et al.; licensee BioMed Central Ltd. 2014
- Published: 16 January 2014
- Fractional Anisotropy
- Diffusion Tensor Imaging
- Compress Sense
- Mean Diffusivity
- Acceleration Factor
Diffusion tensor imaging (DTI) has been shown  to be extremely promising for characterizing the hierarchical microstructure of myocardium. DTI studies are hampered by lengthy acquisition times, needed for high spatial resolution and/or improved SNR. Compressed sensing (CS) algorithms recover data from under-sampled acquisitions, and have been used  to reduce scan time in MRI. The fast composite splitting algorithm (FCSA)  impressively outperforms other classical CS reconstruction methods by providing more accurate results in less CPU time. In this study, we investigate the feasibility of applying FCSA CS to DTI of an excised human heart. To our knowledge, this is the first time CS reconstruction has been applied to DTI of a human heart.
3D DTI of a whole human heart was performed on a 3T Siemens Skyra using a monopolar spin echo sequence with 30 diffusion directions at b = 770 smm-2 (+b0) with 1 mm isotropic resolution.
Diffusion tensor data was reconstructed and the left-ventricular wall was analyzed. Maps of fractional anisotropy (FA), mean diffusivity (MD), and helix angles (HA) were computed for the fully-sampled and reconstructed under-sampled datasets. To evaluate the accuracy of FCSA CS, root mean square errors (RMSEs) of FA, MD, and HA were estimated between the full-sampled and the accelerated data-sets. All computations were performed using custom-made software in Matlab.
Root-mean square error (RMSE) of fractional anisotropy (FA), mean diffusivity (MD), and helix angles (HA) for the 4 sampling ratios.
MD (10-3 mm2/s)
We have demonstrated that CS using FCSA has potential to shorten acquisition times of cardiac DTI without compromising accuracy. These results can be used to minimize patient discomfort and mitigate growing healthcare costs through increasing contemporary scanner throughput.
This work was supported by the National Institute of Health Research Cardiovascular Biomedical Research Unit at the Royal Brompton Hospital and Imperial College, London.
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