Longitudinal Diffusion MRI for Treatment Response Assessment: Preliminary Experience using an MRI-Guided tri-Cobalt 60 Radiotherapy System
Yingli Yang1, Minsong Cao1, Ke Sheng1, Yu Gao2, Allen M Chen1, Mitchell Kamrava1, Percy Lee1, Nzhde Agazaryan1, James Lamb1, David H Thomas1, Daniel A Low1, and Peng Hu2

1Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States, 2Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States

Synopsis

Diffusion weighted MRI is promising for early prediction of response to radiotherapy 1, 2, and for adaptive radiotherapy, wherein the treatment plan is adapted during treatment based on patients’ response assessed by imaging. Currently DWI-based adaptive radiotherapy is not widely adopted because of scientific and practical challenges. Most importantly, the timing for DWI imaging is not well studied without longitudinal diffusion MRI data at a finer time interval (every 2-5 days) throughout the course of treatment. A recently commercialized MRI-guided radiotherapy system (ViewRay) may eliminate the current challenges and bring diffusion MRI-guided adaptive radiation therapy closer to clinical utility.

Introduction

MRI is increasingly incorporated into radiotherapy workflow. Recently, a MRI-guided radiotherapy system (ViewRay) has become commercially available. This system has a 0.35 Tesla magnet and 3 Cobalt 60 radiation sources. We hypothesize that such a system will enable practical adaptive radiotherapy wherein the therapy plan is adapted during the course of therapy based on tumor response assessed by diffusion MRI. In this work, we demonstrate for the first time the preliminary feasibility of a longitudinal diffusion MRI strategy using ViewRay for assessing patient response to radiotherapy.

Methods

We implemented a spin echo (SE)-based diffusion sequence on the ViewRay 0.35 Tesla MRI system (18 mT/m maximum gradient and 200 mT/m/ms max slew rate) using a single-shot echo planar imaging (EPI) readout. The diffusion encoding gradients were symmetrically played on both sides of the refocusing radiofrequency (RF) pulse of the spin echo. The sequence was tested in a diffusion phantom and subsequently used for in vivo studies.

Six patients (3 head and neck cancer, 3 sarcoma) who underwent fractionated radiotherapy were enrolled in this study. The pulse sequence parameters included: flip angle=90°, echo time (TE) = 160ms, repetition time (TR) = 2600ms, slice thickness=6 mm, EPI factor=128, field of view (FOV) = 350 mm × 350 mm, b-values = 0, 100, 200, 300, 400, 500 s/mm2, 5 averages and total scan time of 70 seconds for all 10 slices. The same pulse sequence was used to acquire longitudinal diffusion data (every 2-5 days) on the 6 patients throughout the entire course of radiotherapy (ranging from 8-35 fractions). Regions of interest (ROIs) were drawn in the tumor on the diffusion images based on the GTV contours from each patient’s standard clinical simulation. To evaluate the reproducibility and reliability of our ADC measurements, a separate reference ROI was drawn in the brain stem for the three head and neck cancer patients. The ADC values for these reference ROIs were not expected to change over the course of the treatment and were used to assess the reproducibility of our ADC measurements.

Results

In diffusion phantom studies, the ADC values measured on the ViewRay 0.35T system matched well with reference ADC values acquired on 3T with <5% error for a range of ground truth diffusion coefficients of 0.4 - 1.1 ×10-3 mm2/s.

All the patients in this study completed the longitudinal diffusion MRI with no complications. Each patient underwent 4 - 7 diffusion MRI scans depending on their treatment length. Figure 1 shows ADC maps from a 45 y.o H&N cancer patient acquired at 7 time points during the course of treatment. The patient had squamous cell carcinoma (SCC) of the left maxillary sinus. The brainstem ADC values remained stable throughout the treatment and the mean brainstem ADC was between 0.47 ×10-3 mm2/s and 0.57 ×10-3 mm2/s for all 7 times points, which confirms the reproducibility of our ADC measurements. The mean ADC for the tumor, however, increased from 1.3 ×10-3 mm2/s at the 4th fraction to 1.6 ×10-3 mm2/s at the 31st fraction. In another head and neck cancer patient shown in Fig. 2, the brainstem ADC values also remained relatively stable throughout the treatment (between 0.49 ×10-3 mm2/s to 0.56 ×10-3 mm2/s); however, the tumor ADC value substantially decreased from 1.5 ×10-3 mm2/s at the 2nd fraction of the treatment to 1.0 ×10-3 mm2/s at the 29th fraction (33% reduction).

We hypothesize that for large tumors, our ADC maps may be used to assess localized treatment response for tumor subregions. Figure 3 shows a sarcoma patient with a 32 x 22 x 14 cm3 tumor. The simulation CT image (Fig. 3a) did not differentiate well between tumor and surrounding normal tissue. The diffusion-weighted image (Fig. 3b, b=500) clearly shows the hyper-intense tumor that matched well with the patient’s gross tumor volume (GTV) contour, which was drawn by a clinical radiation oncologist based on the simulation CT. In the corresponding ADC map (Fig. 3c); there was considerable heterogeneity within the tumor. The ADC values within the right lateral region of the tumor had much higher ADC values than other regions.

Conclusions

We demonstrated that longitudinal diffusion MRI on a weekly basis or more often is feasible at 0.35 Tesla using the ViewRay system. Our longitudinal diffusion data show different temporal variations in ADC values during the course of treatment. Larger patient cohort studies are warranted to correlate our longitudinal diffusion imaging to patient outcome and tumor control. Such an approach may overcome many of the scientific and practical challenges of diffusion MRI-based adaptive radiotherapy.

Acknowledgements

No acknowledgement found.

References

1 P. Bhatnagar, M. Subesinghe, C. Patel, R. Prestwich, and A.F. Scarsbrook, “Functional imaging for radiation treatment planning, response assessment, and adaptive therapy in head and neck cancer.,” Radiographics 33(7), 1909–29 (2013).

2 C. Tsien, Y. Cao, and T. Chenevert, “Clinical applications for diffusion magnetic resonance imaging in radiotherapy.,” Semin. Radiat. Oncol. 24(3), 218–26 (2014).

3 D. Le Bihan, E. Breton, D. Lallemand, P. Grenier, E. Cabanis, and M. Laval-Jeantet, “MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders.,” Radiology 161(2), 401–7 (1986).

Figures

Figure 1: Longitudinal diffusion data from a 45 y.o. head and neck cancer patient. The brainstem ADC values did not significantly change over the course of the treatment, which is expected. For this patient, the average tumor ADC increased consistently over time from 1.3 ×10-3 mm2/s to 1.6 ×10-3 mm2/s.

Figure 2: Longitudinal diffusion data of a 51 y.o. head and neck cancer patient. The average tumor ADC was ~1.5 ×10-3 mm2/s during the first 3 weeks of radiotherapy, and decreased to 1 ×10-3 mm2/s from week 4 until the end of treatment. The ADC of the brain stem was relatively constant.

Figure 3: a) Simulation CT for a sarcoma patient who underwent radiotherapy using ViewRay, which did not differentiate tumor from normal tissue. b) DWI of the patient (b=500 s/mm2) where the tumor clearly differentiated from surrounding tissue. c) The ADC map of the same patient demonstrating great intratumoral heterogeneity of ADC.



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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