Prediction of pelvic tumour coverage by magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU) from referral imaging

Abstract

Background

Patient suitability for magnetic resonance-guided high intensity focused ultrasound (MRgHIFU) ablation of pelvic tumors is initially evaluated clinically for treatment feasibility using referral images, acquired using standard supine diagnostic imaging, followed by MR screening of potential patients lying on the MRgHIFU couch in a ‘best-guess’ treatment position. Existing evaluation methods result in ≥40% of referred patients being screened out because of tumor non-targetability. We hypothesize that this process could be improved by development of a novel algorithm for predicting tumor coverage from referral imaging.

Methods

The algorithm was developed from volunteer images and tested with patient data. MR images were acquired for five healthy volunteers and five patients with recurrent gynaecological cancer. Subjects were MR imaged supine and in oblique-supine-decubitus MRgHIFU treatment positions. Body outline and bones were segmented for all subjects, with organs-at-risk and tumors also segmented for patients. Supine images were aligned with treatment images to simulate a treatment dataset. Target coverage (of patient tumors and volunteer intra-pelvic soft tissue), i.e. the volume reachable by the MRgHIFU focus, was quantified. Target coverage predicted from supine imaging was compared to that from treatment imaging.

Results

Mean (±standard deviation) absolute difference between supine-predicted and treatment-predicted coverage for 5 volunteers was 9 ± 6% (range: 2–22%) and for 4 patients, was 12 ± 7% (range: 4–21%), excluding a patient with poor acoustic coupling (coverage difference was 53%).

Conclusion

Prediction of MRgHIFU target coverage from referral imaging appears feasible, facilitating further development of automated evaluation of patient suitability for MRgHIFU.

Keywords:

• Treatment planning
• magnetic resonance imaging guidance
• high intensity focused ultrasound
• human body deformation
• pelvis
• referral imaging

Acknowledgements

The authors would like to thank Matthew Blackledge, Simon Doran and Matthew Orton from the Institute of Cancer Research (ICR) for their technical support, and Ari Partanen and others from Profound Medical for their support. We are grateful to Philips for their loan of the Sonalleve system to The Royal Marsden Hospital (RMH), and we acknowledge the support of the RMH MRI team, volunteers and patients, the Focused Ultrasound Foundation, CRUK and EPSRC in association with MRC & Department of Health (C1060/A10334, C1060/A16464), the NHS, the NIHR Biomedical Research Centre, the Clinical Research Facility in Imaging, and the Cancer Research Network. This study resulted from research performed as part of a studentship supported by Philips. The views expressed are those of the authors, and not necessarily those of the National Health Service (NHS), the Department of Health, the ICR, the RMH, Profound Medical, Philips or the NIHR.

Disclosure statement

The lead author is the recipient of a studentship supported by Philips.

Data availability statement

The data that support the findings of this study are not available due to limitations in the ethical review.

Additional information

Funding

The first author (NFDL) produced this work whilst on a studentship supported by Philips. Funding from the NIHR Research for Patient Benefit programme (PB-PG-0815-20001) enabled the acquisition of data for the study.