Serhat Ilbey^{1}, Ali Özen^{1}, Pia Jungmann^{2,3}, Johannes Fischer^{1}, Matthias Jung^{2}, and Michael Bock^{1}

^{1}Dept.of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany, ^{2}Department of Diagnostic and Interventional Radiology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany, ^{3}Department of Radiology, Cantonal Hospital Grisons, Chur, Switzerland

Single point imaging (SPI) is intrinsically time-inefficient resulting in very long scan times in PETRA. Compressed sensing PETRA (csPETRA) shortens the acquisition time – here, various csPETRA undersampling schemes were generated and an optimal undersampling scheme was determined experimentally. It was shown in csPETRA knee imaging that in Poisson sampling combined with a spherical fully-sampled center results in minimum error compared to the fully-sampled acquisitions.

In this work, we investigated the effect of the undersampling scheme on the image quality of csPETRA for knee imaging using acceleration factors (

In this study, various SPI sampling masks were generated by varying the distribution of the sampling positions, the density of samples, and the shape of the fully sampled region to improve the image quality. For comparison, 5 different undersampling schemes were generated: Random-Cube(RC), Poisson-Cube(PC), Poisson-Sphere(PS), variable-density Poisson-Sphere(vdPS), and variable-density Poisson-Cube(vdPC). In RC, sampling points are determined fully-randomly with a cubical fully-sampled region whereas in the other schemes the pseudo-random Poisson disk distribution

The performance was measured using the Structural Similarity (SSIM) index

Experiments were performed at a 3T MRI system (MAGNETOM Prisma, Siemens, Erlangen) using a 15‑channel Tx/Rx knee coil (QED, Cleveland, OH). In-vivo PETRA data of the knee of six healthy volunteers was acquired with a full SPI section: FOV=(18cm)3, TR=2ms, TE=48µs, $$$\tau_{RF}$$$=8µs, $$$\alpha$$$=6°, voxel size=(0.45mm)

1. Weiger M, Pruessmann KP. MRI with Zero Echo Time. In: Harris RK, editor. Encyclopedia of Magnetic Resonance. Chichester, UK: John Wiley & Sons, Ltd; 2012. p. emrstm1292. doi: 10.1002/9780470034590.emrstm1292.

2. Grodzki DM, Jakob PM, Heismann B. Ultrashort echo time imaging using pointwise encoding time reduction with radial acquisition (PETRA). Magn. Reson. Med. 2012;67:510–518 doi: 10.1002/mrm.23017.

3. Ilbey S, Fischer J, Bock M, Ozen AC. Compressed Sensing PETRA MRI. In: Proceedings of the 29th scientific meeting, International Society for Magnetic Resonance in Medicine, Virtual; p. 908.

4. Froidevaux R, Weiger M, Rösler MB, Brunner DO, Pruessmann KP. HYFI: Hybrid filling of the dead‐time gap for faster zero echo time imaging. NMR in Biomedicine 2021;34 doi: 10.1002/nbm.4493.

5. Boyd S, Parikh N, Chu E. Distributed optimization and statistical learning via the alternating direction method of multipliers. Now Publishers Inc; 2011.

6. Tamir JI, Ong F, Cheng JY, Uecker M, Lustig M. Generalized magnetic resonance image reconstruction using the Berkeley advanced reconstruction toolbox. In: ISMRM Workshop on Data Sampling & Image Reconstruction, Sedona, AZ. ; 2016.

7. Cook RL. Stochastic sampling in computer graphics. ACM Trans. Graph. 1986;5:51–72 doi: 10.1145/7529.8927.

8. Z Wang A, C Bovik H, Sheikh R, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 2004;13:600–612.

9. Jamieson S. Likert scales: how to (ab)use them. Med Educ 2004;38:1217–1218 doi: 10.1111/j.1365-2929.2004.02012.x.

10. Landis JR, Koch GG. The Measurement of Observer Agreement for Categorical Data. Biometrics 1977;33:159 doi: 10.2307/2529310.

DOI: https://doi.org/10.58530/2022/1707