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

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.