Siamak Nejad-Davarani^{1}, Michelle Mierzwa^{1}, Thorsten Feiweier^{2}, Keith Casper^{3}, and Yue Cao^{1,4}

^{1}Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States, ^{2}Siemens Healthcare GmbH, Erlangen, Germany, ^{3}Department of Otolaryngology, University of Michigan, Ann Arbor, MI, United States, ^{4}Department of Radiology, University of Michigan, Ann Arbor, MI, United States

*In vivo* microstructure imaging based on diffusion weighted imaging can provide valuable information on tumor characteristics and response to cancer therapy. An oscillating diffusion gradient spin-echo sequence was used to acquire DW images with short diffusion times, along with those acquired by a pulsed diffusion gradient spin-echo sequence. Using a random walk with barriers model, microstructure/diffusion parameters in primary and nodal head and neck tumors were estimated prior to, and after two weeks of cancer treatment. Results show feasibility of this method to detect significant changes in these parameters, across the two imaging time points.

Diffusion Weighted (DW) images were acquired on a 3.0T scanner (Skyra, Siemens) with a prototype sequence at 3 or 4 diffusion times (

All DW images, after spatial alignment to resolve intra-session motion within each time-point, were low-pass filtered for noise reduction. A mono-exponential diffusion model was then fit to yield apparent diffusion coefficient (ADC) maps at each diffusion time

The RWBM describes water diffusion with permeable cell membranes, which can be used to characterize microstructure and diffusion parameters

$$ D(t) = D_{0}\left[1-\frac{S}{Vd}\left(\frac{4\sqrt{D_{0}t}}{3\sqrt{\pi}}-\kappa t\right)\right] $$ (1)

(

$$D(t) \simeq D_{\infty} + At^{-\nu},t\rightarrow\infty$$

(2)

($$$D_{\infty}$$$: bulk diffusion coefficient,

Validity of the STL (Equation 1) was examined initially using 2-3 ADCs measured at short

The RWBM was applied to primary and nodal gross tumor volumes (GTVs) contoured by a physician on post-contrast T1W images as well as subvolumes of tumors classified by fuzzy c-means clustering of ADC maps at the shortest diffusion time into groups of high and low ADC values.

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2011 Jun 1;7(6):508-514

3. Lemberskiy G, Fieremans E, Veraart J, Deng F, Rosenkrantz
AB, Novikov DS, “Characterization of Prostate Microstructure Using Water
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Figure 1. Top
row: Diffusion weighted images at b
value = 300 s/mm2 acquired using the prototype oscillating diffusion
gradient spin echo (OGSE) and pulsed gradient spin echo (PGSE) sequences with
diffusion times of ts = 6.9, 9.7, 21.3 and 28.1 ms (from left to right). Bottom
row: Apparent diffusion coefficient maps at the corresponding diffusion times. Yellow and purple contours denote primary and
nodal gross tumor volumes, respectively.

Figure 2. Time-dependent ADC values in a primary gross tumor volume (GTVp) and a nodal GTV (as shown in Figure 1) at diffusion times of ts = 6.9, 9.7, 21.3 and 28.1 ms. Note that ADC values decreases with increases in diffusion times.

Figure 3. Mean and standard deviation of estimated parameters (V/S, D_{0}, D_{inf} and κ) in the primary and lymph nodal GTVs across all 14 patients as well as in the subvolumes of GTVs with high and low ADC values at the shortest diffusion time.

Figure 4. Mean and standard deviation of estimated parameters (V/S, D_{0}, D_{inf} and κ) in the primary and lymph nodal GTVs of 6 patients pre-CRT and after starting 2 weeks of CRT, as well as in the subvolumes of GTVs with high and low ADC values at the shortest diffusion time.

DOI: https://doi.org/10.58530/2022/1979