Fast Temperature Estimation from Undersampled k-Space with Fully Sampled Center for Real Time MR Guided Microwave Ablations
Fuyixue Wang1, Zijing Dong1, Shuo Chen2, Bingyao Chen3, Jiafei Yang3, Xing Wei3, Shi Wang2, and Kui Ying2

1Department of Biomedical Engineering, Tsinghua University, Beijing, China, People's Republic of, 2Key Laboratory of Particle and Radiation Imaging, Ministry of Education, Medical Engineering and Institute, Department of Engineering Physics, Tsinghua University, Beijing, China, People's Republic of, 3Department of Orthopedics, First Affiliated Hospital of PLA General Hospital, Beijing, China, People's Republic of

Synopsis

Real time thermometry is desirable for thermal therapy such as microwave ablation to ensure patient safety. MR temperature imaging using proton resonance frequency (PRF) shift technique can provide temperature maps during the treatment. In this work, we proposed a novel reconstruction framework that estimates temperature changes from undersampled k-space with a few fully sampled k-space points. Simulation studies, phantom heating experiments and human experiments were performed to validate the proposed method. The proposed method can provide temperature images with relatively high accuracy and short reconstruction time at a reduction factor of 4 in presence of motion.

Target Audience

Radiologists and scientists working on MR thermometry.

Purpose

Microwave ablation is a minimally invasive thermal therapy to kill tumor cells in various human regions. MR temperature imaging using proton resonance frequency (PRF) shift technique 1 allows us to monitor temperature during the treatment. To ensure patient safety, acceleration of data acquisition is required to obtain temperature images with high temporal resolution. Besides, techniques reducing motion artifacts are also needed especially in abdominal organs. K-space method 2 based on hybrid model 3 is an effective solution to these issues, but this approach becomes inaccurate when motion states are not covered by the baseline library and its reconstruction time is relatively long for real time temperature mapping.

In this study, we developed an accelerated temperature estimation method with relatively short reconstruction time and high temperature accuracy in presence of the motion not covered by baseline library.

Methods

Theory: The proposed method estimates temperature changes from undersampled k-space based on the hybrid model 3. Different from the conventional k-space method 2, several lines in the center of k-space are acquired in addition to the uniform undersampling acquisition to get low resolution images. These low resolution images are used to match the baseline images and eliminate phase shifts due to magnetic field drift. In this way, we can avoid the loop iterations of the conventional k-space method in order to speed up the estimation, and reduce the steps to adjust the sparsity coefficient during the process of reconstruction. Rigid registration is also implemented to further correct temperature errors due to the displacement of the subject by utilizing the center k-space data. The main steps of the proposed method are shown in Fig. 1.

Experiments: A phantom simulation was performed to simulate a Gaussian-shaped heat-induced phase shift (maximum temperature change: 30℃, standard deviation: 2.5 pixels) on the heating target.

To evaluate the ability of the proposed method to estimate temperature when the motion states are not covered by the baseline library, motion simulation was performed to simulate a moving process of the object. The simulated baseline images consisted of 11 images in which the object was moved 2 pixels/frame along the frequency encoding direction from -10 pixels to 10 pixels relative to the starting point. The moving process during the treatment was then simulated in which the object was moved at a speed of 2 pixels/frame from 10 pixels to -10 pixels in the first 12 frames and moved 1 pixel/frame back to 10 pixels in the last 20 frames as shown in Fig. 3c. So the first twelve positions were all included in the baseline library, while half of the last twenty positions were not.

To verify the ability of the proposed method to estimate temperature changes in practice, a phantom heating experiment was performed using an MR-compatible microwave probe (Nanjing ECO Microwave System Co., Ltd, Nanjing, China). A fiber optic thermometer was inserted into the phantom next to the microwave heating probe.

In-vivo non-heated experiment was also performed to acquire spinal cord images on healthy volunteers with free-breathing. The standard deviations of different time points were calculated for comparison.

Fully sampled k-space data were acquired with the 2D FFE sequence on a 3.0T Phillips scanner (Philips, Best, the Netherlands) and then retrospectively under-sampled. Reconstructed temperature maps of the proposed method were compared with SPIRiT 4 and the conventional k-space estimation reconstruction thermometry 2 using the same undersampling pattern.

Results

The results of phantom heating simulation are shown in Fig. 2. The proposed method results in the lowest RMSEs and uses much shorter time than the conventional k-space method. Fig. 3 shows the results of motion simulation. There are smaller motion-induced temperature errors using the proposed method. The results of phantom heating and in-vivo experiment are shown in Fig. 4 and Fig. 5. The temperature evolution curve of the proposed method agrees well with the optical fiber (Fig. 4a) and the spatial standard deviations of the proposed method are much less than the conventional k-space method in in-vivo data (Fig. 5).

Discussion

Both the phantom heating simulation and the phantom heating experiment demonstrate the ability of the proposed method to estimate temperature with less computation time and higher accuracy. Motion simulation and in-vivo experiment further show the feasibility of the proposed method for monitoring temperature in presence of motion.

Conclusion

The proposed method can accelerate MR thermometry at an acceleration factor of 4 and provide temperature maps with improved accuracy and robustness to motion, which makes real time imaging for MR-guided microwave ablation possible.

Acknowledgements

This work is supported by National Nature Science Foundation of China, 61571257. The authors acknowledge and thank William Grissom for suggestions.

References

1. Ishihara Y, Calderon A, Watanabe H, et al. A precise and fast temperature mapping using water proton chemical shift. Magnetic Resonance in Medicine, 1995, 34(6): 814-823.

2. Gaur P, Grissom W A. Accelerated MRI thermometry by direct estimation of temperature from undersampled k-space data. Magnetic Resonance in Medicine, 2015, 73(5): 1914-1925.

3. Grissom W A, Rieke V, Holbrook A B, et al. Hybrid referenceless and multibaseline subtraction MR thermometry for monitoring thermal therapies in moving organs. Medical physics, 2010, 37(9): 5014-5026.

4. Lustig M, Pauly J M. SPIRiT: Iterative self-consistent parallel imaging reconstruction from arbitrary k-space. Magnetic Resonance in Medicine, 2010, 64(2): 457-471.

Figures

FIG. 1. Illustration of the framework of the proposed method.

FIG. 2. Phantom simulation experiment: (a) Comparison of fast k-space, conventional k-space and SPIRiT for temperature estimation, with RMSEs of ROI in heated region listed on the lower right of each reconstructed image. (b) Computation times of the three methods with different accelerated factors.

FIG. 3. Motion simulation: (a,b) Temperature error maps of the two methods. (c) Simulated phantom displacement relative to the starting point. The first twelve positions are included in the baseline library, while half of the last twenty positions are not. (d) RMSEs of different time points in ROI (Reduction factor=4).

FIG. 4. Phantom heating experiment: (a) Comparison of the three methods (Reduction factor = 3.5) for temperature estimation with the optical fiber. (b) The RMSEs and the computation times of the three methods.

FIG. 5. In-vivo experiment: Temperature estimations (Reduction factor = 3.5) of the proposed method and the conventional method in ROI at different time frames, with standard deviations on the bottom of each image.



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