Accelerating interventional real-time MRI

This thesis investigates novel MRI techniques which are aimed at improving the use of MRI for treatment guidance during HIFU and radiotherapy treatments. To guide these treatments effectively, the MRI scans should encompass a large spatial coverage and have a high imaging frame rate such that motion can be compensated for. The first part of the thesis focuses on SMS MRI, a technique where multiple slices are imaged simultaneously. This is evaluated for temperature mapping during HIFU and for motion compensation during radiotherapy treatments. The second part focuses on a latency analysis of various MRI sequences. Minimizing the MRI latency is important when MRI is used as input for a real-time feedback loop, automatically adjusting the treatment based on the most current state of the anatomy

Accelerating interventional real-time MRI

This thesis investigates novel MRI techniques which are aimed at improving the use of MRI for treatment guidance during HIFU and radiotherapy treatments. To guide these treatments effectively, the MRI scans should encompass a large spatial coverage and have a high imaging frame rate such that motion can be compensated for. The first part of the thesis focuses on SMS MRI, a technique where multiple slices are imaged simultaneously. This is evaluated for temperature mapping during HIFU and for motion compensation during radiotherapy treatments. The second part focuses on a latency analysis of various MRI sequences. Minimizing the MRI latency is important when MRI is used as input for a real-time feedback loop, automatically adjusting the treatment based on the most current state of the anatomy

A planning strategy for combined motion-assisted/gated MR guided focused ultrasound treatment of the pancreas

Objective: To develop and evaluate a combined motion-assisted/gated MRHIFU heating strategy designed to accelerate the treatment procedure by reducing the required number of sonications to ablate a target volume in the pancreas. Methods: A planning method for combined motion-assisted/gated MRHIFU using 4D-MRI and motion characterization is introduced. Six healthy volunteers underwent 4D-MRI for target motion characterization on a 3.0-T clinical scanner. Using displacement patterns, simulations were performed for all volunteers for three sonication approaches: gated, combined motion-assisted/gated, and static. The number of sonications needed to ablate the pancreas head was compared. The influence of displacement amplitude and target volume size was investigated. Spherical target volumes (8, 15, 20 and 34 mL) and displacement amplitudes ranging from 5 to 25 mm were evaluated. For this case, the number of sonications required to ablate the whole target was determined. Results: The number of required sonications was lowest for a static target, 62 on average (range 49-78). The gated approach required most sonications, 126 (range 97-159). The combined approach was almost as efficient as the hypothetical static case, with an average of 78 (range 53-123). Simulations showed that with a 5-mm displacement amplitude, the target could be treated by making use of motion-assisted MRHIFU sonications only. In that case, this approach allowed the lowest number of sonication, while for 10 mm and above, the number of required sonications increased. Conclusion: The use of a combined motion-assisted/gated MRHIFU strategy may accelerate tumor ablation in the pancreas when respiratory-induced displacement amplitudes are between 5 and 10 mm

A planning strategy for combined motion-assisted/gated MR guided focused ultrasound treatment of the pancreas

Objective: To develop and evaluate a combined motion-assisted/gated MRHIFU heating strategy designed to accelerate the treatment procedure by reducing the required number of sonications to ablate a target volume in the pancreas. Methods: A planning method for combined motion-assisted/gated MRHIFU using 4D-MRI and motion characterization is introduced. Six healthy volunteers underwent 4D-MRI for target motion characterization on a 3.0-T clinical scanner. Using displacement patterns, simulations were performed for all volunteers for three sonication approaches: gated, combined motion-assisted/gated, and static. The number of sonications needed to ablate the pancreas head was compared. The influence of displacement amplitude and target volume size was investigated. Spherical target volumes (8, 15, 20 and 34 mL) and displacement amplitudes ranging from 5 to 25 mm were evaluated. For this case, the number of sonications required to ablate the whole target was determined. Results: The number of required sonications was lowest for a static target, 62 on average (range 49-78). The gated approach required most sonications, 126 (range 97-159). The combined approach was almost as efficient as the hypothetical static case, with an average of 78 (range 53-123). Simulations showed that with a 5-mm displacement amplitude, the target could be treated by making use of motion-assisted MRHIFU sonications only. In that case, this approach allowed the lowest number of sonication, while for 10 mm and above, the number of required sonications increased. Conclusion: The use of a combined motion-assisted/gated MRHIFU strategy may accelerate tumor ablation in the pancreas when respiratory-induced displacement amplitudes are between 5 and 10 mm

A planning strategy for combined self-scanned/gated MR- HIFU treatment in the pancreas

International audienc

A planning strategy for combined self-scanned/gated MR- HIFU treatment in the pancreas

International audienc

Particle Filter–Based Target Tracking Algorithm for Magnetic Resonance–Guided Respiratory Compensation : Robustness and Accuracy Assessment

Purpose: To assess overall robustness and accuracy of a modified particle filter–based tracking algorithm for magnetic resonance (MR)-guided radiation therapy treatments. Methods and Materials: An improved particle filter–based tracking algorithm was implemented, which used a normalized cross-correlation function as the likelihood calculation. With a total of 5 healthy volunteers and 8 patients, the robustness of the algorithm was tested on 24 dynamic magnetic resonance imaging (MRI) time series with varying resolution, contrast, and signal-to-noise ratio. The complete data set included data acquired with different scan parameters on a number of MRI scanners with varying field strengths, including the 1.5T MR linear accelerator. Tracking errors were computed by comparing the results obtained by the particle filter algorithm with experts' delineations. Results: The ameliorated tracking algorithm was able to accurately track abdominal as well as thoracic tumors, whereas the previous Bhattacharyya distance-based implementation failed in more than 50% of the cases. The tracking error, combined over all MRI acquisitions, is 1.1 ± 0.4 mm, which demonstrated high robustness against variations in contrast, noise, and image resolution. Finally, the effect of the input/control parameters of the model was very similar across all cases, suggesting a class-based optimization is possible. Conclusions: The modified particle filter tracking algorithm is highly accurate and robust against varying image quality. This makes the algorithm a promising candidate for automated tracking on the MR linear accelerator