POD-Based Recursive Temperature Estimation for MR-Guided RF Hyperthermia Cancer Treatment: A Pilot Study

Hendrikx, R. W. M.; Curto, Sergio; Jager, Bram de; Maljaars, E.; Rhoon, Gerard C. van; Paulides, Margarethus M.; Heemels, W.P.M.H.

doi:10.1109/cdc.2018.8619745

Cited by 7 publications

(20 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, it offers the possibility to characterize treatment efficacy by observing the required thermal metrics in real time during treatment. This technique brings opportunities for dynamic treatment delivery feedback control [ 24 , 25 , 26 , 27 , 28 , 29 , 30 ], as well as treatment planning validation [ 13 , 17 , 31 ], and assessment of thermoregulation in tissues [ 32 , 33 , 34 , 35 ]. There are several MR thermometry methods; the proton resonance frequency shift (PRFS) method is the most widely used due to its linearity and sensitivity [ 36 , 37 , 38 , 39 , 40 ].…”

Section: Introductionmentioning

confidence: 99%

MR Thermometry Accuracy and Prospective Imaging-Based Patient Selection in MR-Guided Hyperthermia Treatment for Locally Advanced Cervical Cancer

VilasBoas‐Ribeiro

Curto

Rhoon

et al. 2021

Cancers

View full text Add to dashboard Cite

The efficacy of a hyperthermia treatment depends on the delivery of well-controlled heating; hence, accurate temperature monitoring is essential for ensuring effective treatment. For deep pelvic hyperthermia, there are no comprehensive and systematic reports on MR thermometry. Moreover, data inclusion generally lacks objective selection criteria leading to a high probability of bias when comparing results. Herein, we studied whether imaging-based data inclusion predicts accuracy and could serve as a tool for prospective patient selection. The accuracy of the MR thermometry in patients with locally advanced cervical cancer was benchmarked against intraluminal temperature. We found that gastrointestinal air motion at the start of the treatment, quantified by the Jaccard similarity coefficient, was a good predictor for MR thermometry accuracy. The results for the group that was selected for low gastrointestinal air motion improved compared to the results for all patients by 50% (accuracy), 26% (precision), and 80% (bias). We found an average MR thermometry accuracy of 2.0 °C when all patients were considered and 1.0 °C for the selected group. These results serve as the basis for comprehensive benchmarking of novel technologies. The Jaccard similarity coefficient also has good potential to prospectively determine in which patients the MR thermometry will be valuable.

show abstract

Section: Introductionmentioning

confidence: 99%

MR Thermometry Accuracy and Prospective Imaging-Based Patient Selection in MR-Guided Hyperthermia Treatment for Locally Advanced Cervical Cancer

VilasBoas‐Ribeiro

Curto

Rhoon

et al. 2021

Cancers

View full text Add to dashboard Cite

show abstract

“…where the superscript p indicates the model-based prediction; x p t denotes the predicted temperature at time t; A t and B t denote the reduced-order discrete-time system matrices obtained by projecting the time-discretized dynamics on the precomputed POD subspace,which we obtained from multiple time simulations. 14 Furthermore, x t−1 denotes the estimated temperature at time t − 1; u t is the control input at time t using the settings of the RF applicator antennas, thus B t u t is the heat load delivered by the RF applicator. Last, P t denotes the predicted state covariance, and Q is the process noise covariance, which is approximated by a diagonal covariance matrix and this defined as identity matrix.…”

Section: Kalman Filter For Recursive Temperature Estimationmentioning

confidence: 99%

“…Last, P t denotes the predicted state covariance, and Q is the process noise covariance, which is approximated by a diagonal covariance matrix and this defined as identity matrix. 14 In the second step of the process, the Kalman filter combines the model-based prediction with the filtered MR thermometry. To this end, at each time step, we compute the so-called Kalman gain (K t ) that incorporates how much and where to trust the model-based prediction and MR thermometry based on their respective covariance matrices.…”

Section: Kalman Filter For Recursive Temperature Estimationmentioning

confidence: 99%

“…Hence, new developments are urgently needed to improve MR thermometry measurements [11][12][13] and enable thermal dosimetry during treatment. A combined proper orthogonal decomposition (POD)-Kalman filtering method was recently proposed, 14 but its performance in experimental and in in vivo settings has not been rigorously studied. To establish its potential, we studied the reproducibility of this method using several heating experiments.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

POD–Kalman filtering for improving noninvasive 3D temperature monitoring in MR‐guided hyperthermia

et al. 2022

View full text Add to dashboard Cite

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 MRThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

“…Kalman Filter theory is largely used in the biomedical field for estimating the state of the controlled quantity, and permits to manage noisy measurements and potential inaccuracies of the medical device, such as its placement inside the anatomical district [17]. In the scenario of the thermal treatments, Kalman Filter has been proposed to predict temperature during MR-guided thermal therapy delivery in the presence of noise or corrupted data [16], [18], [19]. Image-based thermometry can suffer from inaccuracy related to motion or artifact in the images, but can still benefit from a large number of information within the image field of view.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Thermometry for Laser Ablation Procedure Using Kalman Filters and Sparse Temperature Measurements

Schulmann

Soltani-Sarvestani

Landro

et al. 2022

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

We implement a data-assimilation Bayesian framework for the reconstruction of the spatiotemporal profile of the tissue temperature during laser irradiation. The predictions of a physical model simulating the heat transfer in the tissue are associated with sparse temperature measurements, using an Unscented Kalman Filter. Methods: We compare a standard state-estimation filtering procedure with a joint-estimation (state and parameters) approach: whereas in the state-estimation only the temperature is evaluated, in the joint-estimation the filter corrects also uncertain model parameters (i.e., the medium thermal diffusivity, and laser beam properties). We have tested the method on synthetic temperature data, and on the temperature measured on agar-gel phantom and porcine liver with fiber optic sensors. Results: The joint-estimation allows retrieving an accurate estimate of the temperature distribution with a maximal error < 1.5 °C in both synthetic and liver 1D data, and < 2 °C in phantom 2D data. Our approach allows also suggesting a strategy for optimizing the temperature estimation based on the positions of the sensors. Under the constraint of using only two sensors, optimal temperature estimation is obtained when one sensor is placed in proximity of the source, and the other one is non-symmetrical. Conclusion: The joint-estimation significantly improves the predictive capability of the physical model. Significance: This work opens new perspectives on the benefit of data-assimilation frameworks for laser therapy monitoring.

show abstract

POD-Based Recursive Temperature Estimation for MR-Guided RF Hyperthermia Cancer Treatment: A Pilot Study

Cited by 7 publications

References 21 publications

MR Thermometry Accuracy and Prospective Imaging-Based Patient Selection in MR-Guided Hyperthermia Treatment for Locally Advanced Cervical Cancer

MR Thermometry Accuracy and Prospective Imaging-Based Patient Selection in MR-Guided Hyperthermia Treatment for Locally Advanced Cervical Cancer

POD–Kalman filtering for improving noninvasive 3D temperature monitoring in MR‐guided hyperthermia

Model-Based Thermometry for Laser Ablation Procedure Using Kalman Filters and Sparse Temperature Measurements

Contact Info

Product

Resources

About