Mr. Derick N. Rosario-Berrios Profile

Derick N. Rosario-Berrios

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Proceedings Article | 7 April 2023 Presentation + Paper

Spectral CT thermometry with improved temperature sensitivity for image-guided thermal ablation

Leening Liu, Rizza Pua, Derick Rosario-Berrios, Olivia Sandvold, Kevin Brown, Grace Gang, Michael Soulen, Nadav Shapira, Peter Noël

Proceedings Volume 12463, 124630P (2023) https://doi.org/10.1117/12.2649111

KEYWORDS: Denoising, Thermometry, Ablation, Computed tomography, Tumors

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Percutaneous ablation procedures have been increasingly utilized to non-invasively treat tumors, such as hepatocellular carcinoma, by heating tumor cells beyond the lethal threshold. Intraprocedural temperature monitoring via spectral CT thermometry with a sensitivity less than 3 °C can reduce local recurrence rates by ensuring the tumor and its surrounding safety margin reach lethal temperatures. Because temperature sensitivity is reliant on noise, the effect of additional denoising, radiation dose, slice thickness, and iterative reconstruction levels on temperature sensitivity was evaluated on physical density slices utilized to generate temperature maps. Three different denoising algorithms (total variation, bilateral filtering, and non-local means) were applied to input images prior to generating physical density maps. Differences in noise in physical density and temperature sensitivity were calculated for each combination of parameters. All three denoising algorithms did not significantly affect quantification with an average difference of 1 x 10^-4 g/mL from standard reconstructions, while generally non-local means denoising performed best with noise decreasing to 2 x 10^-4 g/mL. The reduction in noise corresponded to temperature sensitivity decreasing from 15 ± 4 °C with standard reconstructions to 3 ± 2 °C with non-local means denoising at 2 mGy with 2 mm slices. Overall, temperature sensitivity at low radiation doses improved to clinically satisfactory levels with additional denoising. These accurate temperature maps from spectral CT thermometry will enable real-time, non-invasive temperature monitoring to ensure critical structures are not thermally damaged and the entire tumor and safety margin reach the lethal threshold, reducing local recurrences.

Proceedings Article | 18 October 2022 Open Access

Non-invasive real-time thermometry via spectral CT physical density quantifications

Nadav Shapira, Leening Liu, Rizza Pua, Derick Rosario, Johoon Kim, David Cormode, Gregory Nadolski, Matthew Hung, Michael Soulen, Peter Noël

Proceedings Volume 12304, 1230404 (2022) https://doi.org/10.1117/12.2647018

KEYWORDS: Thermometry, Computed tomography, Liver, Liver cancer, X-rays

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Efficient removal of solid focal tumors is a major challenge in modern medicine. Percutaneous thermal ablation is a first-line treatment for patients not fit for surgical resection or when the disease burden is low, mainly due to expedited patient recovery times, lower rates of post-operative morbidity, and reduced healthcare costs. While continuously gaining popularity, ~100,000 yearly thermal hepatic ablation procedures are currently performed without actively monitoring temperature distributions, leading to high rates of incomplete ablations, local recurrences, and damage to surrounding structures. Recent advancements in computed tomography (CT), especially spectral CT, provide promising opportunities for lowering these rates. The additional information available with spectral CT can provide the necessary capabilities to achieve accurate, reliable, on-demand, and non-invasive thermometry during ablation procedures. By taking advantage of our newly developed spectral physical density maps and their direct relation with temperature changes, we performed experiments on phantoms and ex vivo tissue to develop, evaluate, optimize, and refine a method for generating thermometry maps from spectral CT scans. Our results validate the accuracy of the spectral physical density model, allowing “whole-organ” mass quantifications that are accurate within one percent, as well as demonstrate an ability to extract temperature changes (linear correlation coefficient of 0.9781) non-invasively and in real-time.

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