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Dynamic PRF and T₁-based 3D Thermometry in the Liver using a Variable Flip Angle Stack-of-Radial Technique

Le Zhang¹, Tess Armstrong^1,2, and Holden H. Wu^1,2,3
¹Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States, ²Physics and Biology in Medicine, University of California, Los Angeles, Los Angeles, CA, United States, ³Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States

Synopsis

MR thermometry in the liver is challenged by mismatch between baseline and dynamic images caused by motion, leading to temperature errors. To address motion, previous methods had to compromise spatial coverage to increase temporal resolution. We propose a variable-flip-angle (VFA) 3D stack-of-radial technique for combined proton resonance frequency shift (PRF) and T₁-based MR thermometry with volumetric coverage and high spatiotemporal resolution. Accurate VFA T₁ calculation is achieved by synthesizing B₁+ maps that match the liver position in dynamic images. A multi-baseline approach is used for accurate dynamic PRF measurements. Results from non-heating scans demonstrate reliable liver T₁ and PRF measurements.

Introduction

Proton resonance frequency shift (PRF) is widely used to measure temperature during MR-guided thermal therapies^1,2. However, when PRF is applied in the liver, breathing motion causes substantial error due to mismatch between dynamic and baseline images. To address this, a baseline image library can be collected before heating to cover the expected breathing motion range, and an appropriate baseline image can be selected to calculate temperature change at each time point. Previous studies^3-5 have adopted this multi-baseline approach for fast liver PRF thermometry using echo-planar imaging sequences. However, these methods only provided limited through-plane coverage (1-5 slices) to achieve the temporal resolution needed to resolve breathing motion. We build on a previously-developed variable-flip-angle golden-angle-ordered (GA) 3D stack-of-radial sequence⁶ for dynamic PRF and T₁-based MR thermometry in liver with 3D coverage and high spatiotemporal resolution . T₁ can be used in conjunction with PRF as it is less susceptible to motion effects⁷. Moreover, T₁ can provide temperature information in fatty tissues where PRF fails⁸.

Methods

In an IRB-approved study, N=5 healthy subjects (3 males, 2 females) with age of 31±11 years and body mass index of 26.6±7.4kg/m² underwent non-heating free-breathing abdominal scans at 3T (Prisma, Siemens) with body and spine arrays. Fig.1A shows the variable-flip-angle stack-of-radial PRF and T₁ mapping sequence (“radial VFA”). After gradient calibration⁹ and dummy scans to establish steady state, segments of 32 GA radial angles were acquired with alternating flip angles. GA ordering continued across segments so that radial spokes can be grouped together during reconstruction, where a k-space weighted image contrast (KWIC) filter¹⁰ was applied.

For dynamic radial VFA T₁ mapping (Fig.1B), coil- and echo-combined magnitude images acquired with α₁ were matched to those with α₂ in neighboring segments with the highest similarity coefficient¹¹. To calibrate flip angles, breath-held B₁+ maps¹² were acquired at end-expiration and end-inspiration. Dynamic B₁+ maps were synthesized at each time point as a weighted sum of the two breath-held B₁+ maps according to their similarity coefficient. A static T₁ map was acquired using non-time-resolved radial VFA for comparison.

For PRF calculation (Fig.1C), dynamic phase images were reconstructed using virtual reference coils and echo-combined¹³ to an effective TE=10ms. The first n=60 sets of phase images (temporal footprint=71s) were grouped into a baseline library and principal component analysis (PCA) was performed¹⁴. The first m=6~10 principal vectors were retained such that their eigenvalues λ satisfied $$$\frac{\Sigma^{m}_{i=1}\lambda^2}{\Sigma^{n}_{i=1}\lambda^2}>0.99$$$ and could represent the expected range of motion. These m vectors were linearly combined to provide baseline images at each time point for PRF calculation¹⁴. Table 1 lists scanning parameters.

For comparison, dynamic T₁ was also calculated by using only end-expiration B₁+ maps and PRF was calculated using only a single baseline phase image acquired at end-expiration. To characterize the stability of radial VFA thermometry, the temporal coefficient of variation (COV) of T₁ and temporal standard deviation (SD) of PRF temperature change were calculated on slices that were observed at all time points. One region of interest (ROI) of 2cm² was drawn in each subject in one of these slices to track T₁ and PRF fluctuations.

Results

Fig.2A shows static T₁ maps and Figs.2B and 2D show dynamic T₁ maps calculated using synthesized dynamic B₁+ maps. Figs.2C and 2E show that dynamic T₁ maps using only end-expiration B₁+ maps have errors due to misalignment. Figs.3A and 3C show absolute relative PRF temperature change maps calculated using the multi-baseline method, while Figs.3B and 3D show that PRF maps using a single end-expiration baseline have large errors especially towards the liver dome. Figs.4A and 4B show COV maps of T₁ using synthesized B₁+ maps and SD maps of PRF using multi-baseline, respectively, in one subject. The temporal fluctuations of mean T₁ and PRF temperature inside the ROI in the same subject are plotted in Figs.4C and 4D. Figs.4E and 4F compare the temporal mean and SD of T₁ and PRF inside five ROIs across all five subjects during entire scans. Synthesized B₁+ maps reduced T₁ COV substantially to an average of (3.9±0.6)% in ROIs across all subjects, while multi-baseline reduced PRF SD to (1.86±0.30)°C.

Discussion

In non-heating free-breathing liver scans, our proposed radial VFA achieved 3D coverage (16 slices), in-plane resolution of 1.6x1.6mm², and temporal resolution of <1s. The use of dynamic synthesized B₁+ maps for VFA T₁ calculation and multi-baseline PRF approach substantially improved the stability of both measurements compared to reference approaches. COV of T₁ and SD of PRF temperature change were below 5% and 2.5°C, respectively, throughout the 6-minute scans even in the liver dome, which experienced prominent motion. Some blurring could be observed near liver boundaries due to the 15~20s temporal footprint of the KWIC filter. This could be reduced by incorporating parallel imaging acceleration¹⁵. Reference-less methods could also mitigate the need for baseline images and improve PRF accuracy^4,16. The feasibility of using radial VFA to monitor temperature change should be evaluated in future thermal ablation experiments.

Conclusion

In vivo non-heating evaluations of our proposed variable-flip-angle GA stack-of-radial PRF-T₁ thermometry technique in the liver demonstrated good stability with dynamic 3D coverage and high spatiotemporal resolution. This technique has the potential to improve monitoring/control of thermal therapies in moving organs during free-breathing.

Acknowledgements

This study was supported in part by Siemens Healthineers and UCLA Radiological Sciences. The authors thank the study coordinators at UCLA.

References

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Figures

Figure 1: (A) Diagram of the radial VFA sequence and k-space weighted image contrast (KWIC) reconstruction. (B) Diagram showing the matching of magnitude images acquired with both flip angles (α₁, α₂) and the synthesis of dynamic B₁+ maps for calculating dynamic VFA T₁ maps. (C) Diagram showing the reconstruction of phase images using virtual reference coils and the use of principal component analysis (PCA) in a multi-baseline approach for dynamic PRF temperature change calculation.

Figure 2: (A) Static T₁ maps acquired with a non-time-resolved radial VFA protocol. (B-C) Dynamic T₁ maps at two respiratory motion states calculated with synthesized dynamic B₁+ maps. (D-E) Dynamic T₁ maps at the same time points in (B-C) calculated with an end-expiration B₁+ map, which was misaligned with the end-inspiration state and led to T₁ errors.

Figure 3: (A-B) Absolute PRF temperature change maps at two respiratory motion states calculated with the proposed multi-baseline approach. (C-D) Absolute PRF temperature change maps at the same time points in (A-B) calculated with a single end-expiration baseline, which was misaligned with the dynamic images and led to errors in PRF.

Figure 4: (A) Temporal coefficient of variation (COV) maps of dynamic T₁ and (B) Temporal standard deviation (SD) maps of absolute PRF temperature change in a subset of slices seen in all time points. (C) Plot of mean T₁ during entire scan using synthesized and single end-expiration B₁+ map. Green arrow shows static T₁. (D) Plot of mean PRF temperature change during the entire scan using multi-baseline and single-baseline methods. Bar graphs showing (E) mean±SD of T₁ (numbers above are COV values) and (F) mean±SD of PRF temperature change during entire scan in ROIs across all five subjects.

Table 1: List of parameters for non-heating liver scans in healthy subjects at 3T. All scans were acquired in the axial orientation. TE: echo time; TR: repetition time; KWIC: k-space weighted image contrast; PRF: proton resonance frequency.

Proc. Intl. Soc. Mag. Reson. Med. 28 (2020)

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