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Diffusion time-dependence of diffusivity and kurtosis in locally advanced head and neck squamous cell carcinoma before and after chemo-radiation therapy
Gregory Lemberskiy1, Steven Baete1, Dmitry S Novikov1, Els Fieremans1, Elcin Zan1, Kenneth Hu2, and Sungheon Gene Kim1

1Radiology, NYU School of Medicine, New York, NY, United States, 2Radiation Oncology, NYU Langone Health, New York, NY, United States

### Synopsis

Synopsis: The effect of chemo-radiation therapy on advanced head & neck squamous cell carcinoma was evaluated via diffusion and kurtosis time-dependence. We found opposing diffusion limiting regimes pre and post therapy, where prior to therapy the tissue was well described by the long-time limit (Karger Model applies), and where after therapy the tissue was well described by the short-time S/V limit. This reversal of imaging regimes can serve as a signature of the minimal effective dose required for treatment.

### Introduction

Diffusion and Kurtosis have been proposed as imaging markers to assess cell viability to evaluate the early treatment response.1-5 However, most of previous studies were conducted with short diffusion times (30-100 ms), thereby limiting the sensitivity of these measurements to relatively short length scales $l=\sqrt{6Dt}=10-25\,\mu m$, and have not explore the full potential of diffusion MRI to measure specific tissue microstructural properties. In this abstract, we evaluate diffusion and kurtosis time-dependence for head and neck squamous cell carcinoma (HNSCC) before and after therapy over a wider interval for longer diffusion times (100-700 ms).

### Methods

Data Acquisition: HNSCC patients (n=6) were imaged on a Siemens 3T PRISMA system using a 20-channel head/neck coil. An in-house developed stimulated echo acquisition mode (STEAM) EPI sequence was used to acquire 5 diffusion times, [t=100,200,300,500,700 ms], over 4 b-shells [b=500,1000,2000,3000 s/mm2] with 3 diffusion directions along x, y, and z axes. The mixing time, tm, was [80,180,280,480,680] ms varying with t. Other parameters include, TR=5000 ms, TE=66 ms, resolution=1.5x1.5x4.0 mm3, FOV=190 mm, partial Fourier 6/8, and GRAPPA with R=2. Each patient was imaged twice: once before initiating chemo-radiation therapy and then 4-weeks later after starting the therapy.

Analysis: Each set of images was denoised6, de-Gibbsed7, and affine registered8 over all b and t. The estimated noise-level9 was then used to correct the signal for Rician bias. Following post-processing, diffusion and kurtosis maps were generated via a weighted linear least square fit method10. Due to the proximity of to the Rician floor, b=3000 was discarded in the final analysis.

### Discussion & Conclusion

The trends of empirical D(t) and K(t) observed in this study indicate a qualitative microstructural change from the long-time to the short-time limit following chemo-radiation. Further study is warranted to assess whether the shift between Karger and S/V limits can serve as indicators of whether chemo-radiation therapy has been successful and to which degree the dose can be reduced such that the patient toxicity is minimized.

### Acknowledgements

No acknowledgement found.

### References

1. Padhani AR and others. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia 2009;11(2):102-125.

2. Kim S and others. Diffusion-weighted magnetic resonance imaging for predicting and detecting early response to chemoradiation therapy of squamous cell carcinomas of the head and neck. Clinical cancer research : an official journal of the American Association for Cancer Research 2009;15(3):986-994. 3. Thoeny HC and others. Predicting and Monitoring Cancer Treatment Response with Diffusion-Weighted MRI. Journal of Magnetic Resonance Imaging 2010;32(1):2-16.

4. Jansen JF and others. Non-gaussian analysis of diffusion-weighted MR imaging in head and neck squamous cell carcinoma: A feasibility study. AJNR American journal of neuroradiology 2010;31(4):741-748.

5. Goshima S and others. Diffusion kurtosis imaging to assess response to treatment in hypervascular hepatocellular carcinoma. AJR American journal of roentgenology 2015;204(5):W543-549.

6. Veraart J and others. Denoising of diffusion MRI using random matrix theory. Neuroimage 2016;142:394-406.

7. Kellner E and others. Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn Reson Med 2016;76(5):1574-1581.

8. Klein S and others. elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging 2010;29(1):196-205.

9. Veraart J and others. Diffusion MRI noise mapping using random matrix theory. Magn Reson Med 2016;76(5):1582-1593.

10. Veraart J and others. Weighted linear least squares estimation of diffusion MRI parameters: strengths, limitations, and pitfalls. Neuroimage 2013;81:335-346.

11. Novikov DS and others. Quantifying brain microstructure with diffusion MRI: Theory and parameter estimation. ArXiv e-prints. Volume 16122016.

12. Fieremans E and others. Monte Carlo study of a two-compartment exchange model of diffusion. NMR Biomed 2010;23(7):711-724.

13. Lemberskiy G and others. Validation of surface-to-volume ratio measurements derived from oscillating gradient spin echo on a clinical scanner using anisotropic fiber phantoms. NMR Biomed 2017;30(5).

14. Mitra PP and others. Short-time behavior of the diffusion coefficient as a geometrical probe of porous media. Phys Rev B Condens Matter 1993;47(14):8565-8574.

### Figures

Parametric maps of kurtosis and diffusion at the shortest available time (t=100 ms), and the longest available time (t=700 ms). Diffusivity is largely unchanged prior to chemo-radiation therapy, while kurtosis seems to decay. Post therapy, the diffusivities experience a much larger change, while kurtosis is largely unchanged.

6 Patients averaged over HNSCC lesions before (red) and after (blue) therapy. Signal decay as a function of b, compared with (A) short and (B) long t, where there is an apparent increase in diffusivity and drop in kurtosis post-therapy. (C) Diffusivity and (D) kurtosis time dependence, where the S/V limit is used to fit the diffusivity post therapy and the Karger model is used to fit the kurtosis pre-therapy. A summary of parameters derived from these ROI averaged curves is displayed in table (E).

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)
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