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Groen JA, Crezee J, van Laarhoven HWM, Bijlsma MF, Kok HP. Quantification of tissue property and perfusion uncertainties in hyperthermia treatment planning: Multianalysis using polynomial chaos expansion. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107675. [PMID: 37339535 DOI: 10.1016/j.cmpb.2023.107675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 05/26/2023] [Accepted: 06/08/2023] [Indexed: 06/22/2023]
Abstract
INTRODUCTION Hyperthermia treatment planning (HTP) tools can guide treatment delivery, particularly with locoregional radiative phased array systems. Uncertainties in tissue and perfusion property values presently lead to quantitative inaccuracy of HTP, leading to sub-optimal treatment. Assessment of these uncertainties would allow for better judgement of the reliability of treatment plans and improve their value for treatment guidance. However, systematically investigating the impact of all uncertainties on treatment plans is a complex, high-dimensional problem and too computationally expensive for traditional Monte Carlo approaches. This study aims to systematically quantify the treatment-plan impact of tissue property uncertainties by investigating their individual contribution to, and combined impact on predicted temperature distributions. METHODS A novel Polynomial Chaos Expansion (PCE)-based HTP uncertainty quantification was developed and applied for locoregional hyperthermia of modelled tumours in the pancreatic head, prostate, rectum, and cervix. Patient models were based on the Duke and Ella digital human models. Using Plan2Heat, treatment plans were created to optimise tumour temperature (represented by T90) for treatment using the Alba4D system. For all 25-34 modelled tissues, the impact of tissue property uncertainties was analysed individually i.e., electrical and thermal conductivity, permittivity, density, specific heat capacity and perfusion. Next, combined analyses were performed on the top 30 uncertainties with the largest impact. RESULTS Uncertainties in thermal conductivity and heat capacity were found to have negligible impact on the predicted temperature ( < 1 × 10-10 °C), density and permittivity uncertainties had a small impact (< 0.3 °C). Uncertainties in electrical conductivity and perfusion can lead to large variations in predicted temperature. However, variations in muscle properties result in the largest impact at locations that could limit treatment quality, with a standard deviation up to almost 6 °C (pancreas) and 3.5 °C (prostate) for perfusion and electrical conductivity, respectively. The combined influence of all significant uncertainties leads to large variations with a standard deviation up to 9.0, 3.6, 3.7 and 4.1 °C for the pancreatic, prostate, rectal and cervical cases, respectively. CONCLUSION Uncertainties in tissue and perfusion property values can have a large impact on predicted temperatures from hyperthermia treatment planning. PCE-based analysis helps to identify all major uncertainties, their impact and judge the reliability of treatment plans.
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Affiliation(s)
- Jort A Groen
- Amsterdam UMC location University of Amsterdam, Radiation Oncology, Meibergdreef 9, Amsterdam, the Netherlands; Cancer Center Amsterdam, Cancer biology and immunology, Amsterdam, the Netherlands; Cancer Center Amsterdam, Treatment and quality of life, Amsterdam, the Netherlands.
| | - Johannes Crezee
- Amsterdam UMC location University of Amsterdam, Radiation Oncology, Meibergdreef 9, Amsterdam, the Netherlands; Cancer Center Amsterdam, Cancer biology and immunology, Amsterdam, the Netherlands; Cancer Center Amsterdam, Treatment and quality of life, Amsterdam, the Netherlands
| | - Hanneke W M van Laarhoven
- Amsterdam UMC location University of Amsterdam, Department of Medical Oncology, Amsterdam, the Netherlands; Cancer Center Amsterdam, Treatment and quality of life, Amsterdam, the Netherlands
| | - Maarten F Bijlsma
- Amsterdam UMC location University of Amsterdam, Center for Experimental and Molecular Medicine, Laboratory for Experimental Oncology and Radiobiology, Meibergdreef 9, Amsterdam, the Netherlands; Cancer Center Amsterdam, Cancer biology and immunology, Amsterdam, the Netherlands; Cancer Center Amsterdam, Imaging and biomarkers, Amsterdam, the Netherlands; Oncode Institute, Amsterdam, the Netherlands
| | - H Petra Kok
- Amsterdam UMC location University of Amsterdam, Radiation Oncology, Meibergdreef 9, Amsterdam, the Netherlands; Cancer Center Amsterdam, Cancer biology and immunology, Amsterdam, the Netherlands; Cancer Center Amsterdam, Treatment and quality of life, Amsterdam, the Netherlands
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VilasBoas-Ribeiro I, Nouwens SAN, Curto S, Jager BD, Franckena M, van Rhoon GC, Heemels WPMH, Paulides MM. POD-Kalman filtering for improving noninvasive 3D temperature monitoring in MR-guided hyperthermia. Med Phys 2022; 49:4955-4970. [PMID: 35717578 PMCID: PMC9545729 DOI: 10.1002/mp.15811] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 05/26/2022] [Accepted: 06/02/2022] [Indexed: 12/21/2022] Open
Abstract
Background During resonance frequency (RF) hyperthermia treatment, the temperature of the tumor tissue is elevated to the range of 39–44°C. Accurate temperature monitoring is essential to guide treatments and ensure precise heat delivery and treatment quality. Magnetic resonance (MR) thermometry is currently the only clinical method to measure temperature noninvasively in a volume during treatment. However, several studies have shown that this approach is not always sufficiently accurate for thermal dosimetry in areas with motion, such as the pelvic region. Model‐based temperature estimation is a promising approach to correct and supplement 3D online temperature estimation in regions where MR thermometry is unreliable or cannot be measured. However, complete 3D temperature modeling of the pelvic region is too complex for online usage. Purpose This study aimed to evaluate the use of proper orthogonal decomposition (POD) model reduction combined with Kalman filtering to improve temperature estimation using MR thermometry. Furthermore, we assessed the benefit of this method using data from hyperthermia treatment where there were limited and unreliable MR thermometry measurements. Methods The performance of POD–Kalman filtering was evaluated in several heating experiments and for data from patients treated for locally advanced cervical cancer. For each method, we evaluated the mean absolute error (MAE) concerning the temperature measurements acquired by the thermal probes, and we assessed the reproducibility and consistency using the standard deviation of error (SDE). Furthermore, three patient groups were defined according to susceptibility artifacts caused by the level of intestinal gas motion to assess if the POD–Kalman filtering could compensate for missing and unreliable MR thermometry measurements. Results First, we showed that this method is beneficial and reproducible in phantom experiments. Second, we demonstrated that the combined method improved the match between temperature prediction and temperature acquired by intraluminal thermometry for patients treated for locally advanced cervical cancer. Considering all patients, the POD–Kalman filter improved MAE by 43% (filtered MR thermometry = 1.29°C, POD–Kalman filtered temperature = 0.74°C). Moreover, the SDE was improved by 47% (filtered MR thermometry = 1.16°C, POD–Kalman filtered temperature = 0.61°C). Specifically, the POD–Kalman filter reduced the MAE by approximately 60% in patients whose MR thermometry was unreliable because of the great amount of susceptibilities caused by the high level of intestinal gas motion. Conclusions We showed that the POD–Kalman filter significantly improved the accuracy of temperature monitoring compared to MR thermometry in heating experiments and hyperthermia treatments. The results demonstrated that POD–Kalman filtering can improve thermal dosimetry during RF hyperthermia treatment, especially when MR thermometry is inaccurate.
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Affiliation(s)
- Iva VilasBoas-Ribeiro
- Department of Radiotherapy, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Sven A N Nouwens
- Control System Technology Group, Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Sergio Curto
- Department of Radiotherapy, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Bram de Jager
- Control System Technology Group, Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Martine Franckena
- Department of Radiotherapy, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Gerard C van Rhoon
- Department of Radiotherapy, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.,Department of Radiation Science and Technology, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
| | - W P M H Heemels
- Control System Technology Group, Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Margarethus M Paulides
- Department of Radiotherapy, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.,Care and Cure Research Lab (EM-4C&C) of the Electromagnetics Group, Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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