Improving necrosis estimation in MR-guided prostate focused ultrasound ablations with a probabilistic thermal dose model.

False positive necrosis classification close to ablation zones in magnetic resonance (MR)-guided transurethral ultrasound ablation of the prostate poses an issue for real-time assessment of the treatment. Typically, apriori knowledge about transducers is utilized to filter temperature maps, thereby reducing false positives. Our innovation, however, lies in the ability of our probabilistic CEM43 thermal dose model (pCEM43) to decrease false positives without the need for prior knowledge beyond the noise statistics estimated from baseline images. The original pCEM43 was modified improving statistical thermal dose estimation and applied to transurethral ultrasound ablations. The modified model was evaluated on 22 ablation data sets to determine its accuracy and computation time. MR thermometry was acquired by the TULSA Pro system with an EPI sequence using the proton resonance frequency shift (PRFS). Accuracy was determined with Sørensen-Dice coefficients (DSC), relative false positive rates (rFPR), sensitivities. Additionally, over- and underestimation of ablation volumes was investigated with mean contour overestimation (MCO) and mean contour underestimation (MCU) metrics. Results of pCEM43 were compared to an unfiltered CEM43 (CEM43) and a spatiotemporally filtered CEM43 model (STF-CEM43). Computation times were measured for each data set and compared between models. Median DSCs were 38.9% (32.4%-49.3%), 64.2% (53.7%-73.6%), and 70.7% (66.6%-76.3%) (p≤0.001) for the CEM43, the STF-CEM43, and the pCEM43 model, respectively. Mean rFPRs were 2.18 ±1.03, 0.61 ±0.39, and 0.32 ±0.28 (p≤0.001), and median sensitivities were 75.6% (71.7%-83.1%), 72.0% (66.0%-76.2%), and 70.6% (65.1%-74.5%) (p≤0.001), respectively. Median MCOs were 21.3 (13.7 - 43.0), 1.1 (0.6 - 2.1), and 0.5 (0.3 - 0.8) (p≤0.001) for the CEM43, the STF-CEM43, and the pCEM43 model, respectively. Mean MCUs were 0.6 ±0.4, 0.5 ±0.2, and 0.5 ±0.2 (p=0.025). The modified pCEM43 improves monitoring accuracy and reduces overestimation of ablation volumes without the need for hardware-specific apriori knowledge in real-time. This could increase success rates of treatments and reduce the risk of underablation and subsequent recurrence of tumors. To determine the generalizability of the proposed model, future work will be concerned with performing evaluations for different thermoablation techniques in different types of tissue.