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# Proof of concept study for UTE-based dosimetry in Head and Neck cancers

Nils Tanneau, Laura Sayaque, Benjamin Leporq, Charlène Bouyer, Frank Pilleul, Vincent Gregoire, Olivier BEUF

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Nils Tanneau, Laura Sayaque, Benjamin Leporq, Charlène Bouyer, Frank Pilleul, et al. Proof of concept study for UTE-based dosimetry in Head and Neck cancers. ESMRMB 2025, Oct 2025, Marseiile, France. hal-05324639

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https://hal.science/hal-05324639v1

Submitted on 21 Oct 2025

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Distributed under a Creative Commons Attribution 4.0 International License
## Introduction

The use of MRI in radiotherapy (RT) planning has increased over the years as MRI is a non-ionizing modality that provided exquisite contrast between soft tissues compared to CT scan and an opportunity for personalized RT. However, a limitation for MRI-only dosimetry is the absence of a direct physical link between MR images and electron density (ED) maps of tissues that is provided by the CT scan. ED maps are required by dosimetry algorithms to compute the dose distribution. Efforts to overcome this limitation have been oriented toward the generation of synthetic CT images using artificial intelligence algorithms [5]. Another study [4] suggested that the hydrogen density quantification could be used for dose planning. Previous works demonstrated encouraging initial results [3] on few patients that had to be confirmed for MRI-based dose planning in head and neck radiation therapy. Hence, this work aims to provide a refined methodology using Ultrashort Echo Time (UTE) MR images as a proxy of the hydrogen density of tissues and its use to plan the dose in head and neck radiation therapy.

## Materials & Methods

A phantom with variable proton density was built using water tubes diluted with D2O in various percentages. UTE images were acquired on a 3T Siemens VIDA (Siemens Healthineers, Erlangen, Germany). The average signal value in each tube was compared to the corresponding theoretical proton density and calibration was established using linear regression.

A study was designed using 3T MR and CT images of thirteen patients with various tumors locations. The MRI protocol included a Spiral VIBE UTE sequence which was set up to recover a signal proportional to the proton density of the tissues (flip angle = 5° and TE = 0.03 ms). Preprocessing included N4 bias field correction of the MR images and registration of CT in the MRI space using the SimpleITK framework. [6] The UTE signal of the tissues was expressed relatively to the UTE signal of voxels assuming a mostly water content.

The ED voxel values were obtained by using an equation proposed by Seco et al.[4]. This equation expressed the ED in a tissue as a function of its mass density and atomic composition. Combining it with the results of Demol et al.[2] allowed to simplify the equation and express it as a function of the proton density and mass density.

$$ \rho_{e tissue} = \frac{\rho_{H tissue}}{2} + \frac{\rho_{tissue}}{2} \quad (1) $$

with $\rho_{e tissue}$ the ED of the tissue relative to water, $\rho_{H tissue}$ the UTE signal relative to water and $\rho_{tissue}$ the tissue's mass density.

A categorical mask of the main tissue types (air, bone, fat and soft tissues) was derived from the CT through Hounsfield Unit thresholding. A specific tissue correction was then derived using the mask and mass density values from the literature [2]. Figure 1 B & C shows the final UTE based maps: $UTE_{tissues}$ refers to the maps with tissue correction, while $UTE_{water}$ refers to maps where water mass density was used in the whole body. Dosimetry plans used for the treatment of each patient were recomputed without re-optimization using MONACO (Elekta AB). Resulting dose maps were analysed through dose differences at 95% of tumoural volumes (TVs) and at 2% organs at risks volumes (OaRs). Gamma pass rate at 3%/3mm and 2%/2mm were computed using PyMedPhys. [1]

<figure><img src="image_3.png" /><figcaption>**Figure 1:** Sagittal slice of a patient, CT (A), UTE based map without tissue correction ($UTE_{water}$) (B), UTE based map with tissue correction ($UTE_{tissues}$) (C) Values in the color legend represent the ED multiplied by 100 to respect the DICOM formalism.</figcaption></figure>

## Results

Measurements of the UTE signal were plotted against the theoretical proton density within the tubes. Fig 2, B shows the resulting curve indicating a linear relationship between UTE signal and proton density ($R^2 = 0,987$).

<figure><img src="image_4.png" /><figcaption>**Figure 2:** A) UTE image of the proton density phantom (proportion (in %) of $D_2O$ indicated on each tube), (B) Calibration curve of the theoretical proton density as a function of the measured UTE signal</figcaption></figure>

Gamma pass rate computations at 3%/3mm provide a mean value of 99.4±0,5% and 99.1±0,5% for the doses computed from $UTE_{tissues}$ and $UTE_{water}$ respectively. At 2%/2mm, mean values are 97.9±1.5% and 97.1±1,7% with a significant difference shown by a Wilcoxon test (p≤0,05). The dose differences are negative in most OaRs and TVs for the $UTE_{tissues}$ maps while closer to zero in $UTE_{water}$ maps (fig 3-4). Globally mean dose values ranged between -4% and 3,7% with $\sigma \in [0, 7\%; 6\%]$.

<figure><img src="image_5.png" /><figcaption>**Figure 3:** Max relative dose difference (in %) in OARs volumes</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Figure 4:** Mean relative dose difference (in %) for tumoural volumes</figcaption></figure>

<caption>**Figure 5:** Gamma pass rates at 3%/3mm and 2%/2mm</caption>

## Discussion

Gamma pass rates indicate a good correspondence between the UTE based dosimetry and the CT based one, the tissue correction appears to improve performance in that regard. Globally ED values in both UTE-based maps were higher than expected leading to a reduced dose deposition as the doses were computed without re-optimization of the treatment plan. A larger study will be conducted to improve reliability of the results as well as the accuracy of the tissue correction on the dosimetry. Also, as the UTE signal can be affected by T1-weighting, assessment of its impact on the dosimetry and its correction will be necessary. In this work, the CT has been used to derive a tissues mask. This step could be easily done by combining UTE acquisition with DIXON or CSE-MRI acquisitions.

## Conclusion

This study suggests that UTE derived dosimetry maps could have the potential to plan the dose in head and neck cancers only using MRI images although optimizations and verifications are still required. An additional chemical shift encoded scheme in the spiral VIBE UTE could allow fat-water decomposition and avoid the need of CT images for tissue categorical mask computation.

## References

[1] Simon Biggs, Matthew Jennings, Stuart Swerdloff, Phillip Chlap, Derek Lane, Jacob Rembish, Jacob McAloney, Paul King, Rafael Ayala, Fada Guan, Nicola Lambri, Cody Crewson, and Matthew Sobolewski. Pymedphys: A community effort to develop an open, python-based standard library for medical physics applications. *Journal of Open Source Software*, 7(78):4555, 2022.

[2] Benjamin Demol, Romain Viard, and Nick Reynaert. Monte carlo calculation based on hydrogen composition of the tissue for mv photon radiotherapy. *Journal of Applied Clinical Medical Physics*, 16(5):117-130, 2015.

[3] Laura Sayaque, Benjamin Leporq, Charlène Bouyer, Frank Pilleul, Olivier Hamelin, Vincent Gregoire, and Olivier Beuf. Magnetic resonance imaging with ultra-short echo time sequence for head and neck radiotherapy planning. *Physica Medica*, 133:104974, 2025.

[4] J. Seco and P. M. Evans. Assessing the effect of electron density in photon dose calculations. *Medical Physics*, 33(2):540-552, 2006.

[5] Maria Francesca Spadea, Matteo Maspero, Paolo Zaffino, and Joao Seco. Deep learning based synthetic-ct generation in radiotherapy and pet: A review. *Medical Physics*, 48(11):6537-6566, 2021.

[6] Ziv Yaniv, Bradley C. Lowekamp, Hans J. Johnson, and Richard Beare. Simpleitk image-analysis notebooks: a collaborative environment for education and reproducible research. *Journal of Digital Imaging*, 31(3):290-303, Jun 2018.

## Acknowledgments

This study was funded by French Research Agency HERESP (ANR-22-CE19-0027), within the framework of LabEx PRIMES (ANR-11-LABX-0063) and SIRIC LyriCAN+ (INCa INSERM DGOS 12563).

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LABEX
PRIMES
UNIVERSITÉ DE LYON

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