A framework for the correction of slow physiological drifts during MR‐guided HIFU therapies: Proof of concept

BackgroundDuring lengthy magnetic resonance-guided high intensity focused ultrasound (MRg-HIFU) thermal ablations in abdominal organs, the therapeutic work-flow is frequently hampered by various types of physiological motion occurring at different time-scales. If left un-addressed this can lead to an incomplete therapy and/or to tissue damage of organs-at-risk. While previous studies focus on correction schemes for displacements occurring at a particular time-scale within the work-flow of an MRg-HIFU therapy, in the current work we propose a motion correction strategy encompassing the entire work-flow.MethodsThe proposed motion compensation framework consists of several linked components, each being adapted to motion occurring at a particular time-scale. While respiration was addressed through a fast correction scheme, long term organ drifts were compensated using a strategy operating on time-scales of several minutes. The framework relies on a periodic examination of the treated area via MR scans which are then registered to a reference scan acquired at the beginning of the therapy. The resulting displacements were used for both on-the-fly re-optimization of the interventional plan and to ensure the spatial fidelity between the different steps of the therapeutic work-flow. The approach was validated in three complementary studies: an experiment conducted on a phantom undergoing a known motion pattern, a study performed on the abdomen of 10 healthy volunteers and during 3 in-vivo MRg-HIFU ablations on porcine liver.ResultsResults have shown that, during lengthy MRg-HIFU thermal therapies, the human liver and kidney can manifest displacements that exceed acceptable therapeutic margins. Also, it was demonstrated that the proposed framework is capable of providing motion estimates with sub-voxel precision and accuracy. Finally, the 3 successful animal studies demonstrate the compatibility of the proposed approach with the work-flow of an MRg-HIFU intervention under clinical conditions.ConclusionsIn the current study we proposed an image-based motion compensation framework dedicated to MRg-HIFU thermal ablations in the abdomen, providing the possibility to re-optimize the therapy plan on-the-fly with the patient on the interventional table. Moreover, we have demonstrated that even under clinical conditions, the proposed approach is fully capable of continuously ensuring the spatial fidelity between the different phases of the therapeutic work-flow.

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“…This is in good correspondence with previous reportings [13, 45, 46]. Additionally, a rather large inter-subject variability was observed.…”

Section: Discussionsupporting

confidence: 93%

A framework for continuous target tracking during MR-guided high intensity focused ultrasound thermal ablations in the abdomen

et al. 2017

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“…The main limit of the respiratory gating used in this work is that it does not correct for other motion sources such as heart beats and physiologic motion. A solution developed in MR-guided HIFU treatment is to use electronic beam steering for 3D motion compensation to remain within the targeted site whatever the motion is (Ries et al 2010; Zachiu et al 2015). This would require capability of tracking the target in real-time and in 3D, as well as adapting the focal position at a high temporal and spatial resolution with a low tracking latency to limit errors.…”

Section: Discussionmentioning

confidence: 99%

Elasticity mapping of murine abdominal organsin vivousing harmonic motion imaging (HMI)

et al. 2016

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Recently, ultrasonic imaging of soft tissue mechanics has been increasingly studied to image otherwise undetectable pathologies. However, many underlying mechanisms of tissue stiffening remain unknown, requiring small animal studies and adapted elasticity mapping techniques. Harmonic motion imaging (HMI) assesses tissue viscoelasticity by inducing localized oscillation from a periodic acoustic radiation force. The objective of this study was to evaluate the feasibility of HMI for in vivo elasticity mapping of abdominal organs in small animals. Pathological cases, i.e. chronic pancreatitis and pancreatic cancer, were also studied in vivo to assess the capability of HMI for detection of the change in mechanical properties. A 4.5-MHz focused ultrasound transducer (FUS) generated an amplitude-modulated beam resulting in 50-Hz harmonic tissue oscillations at its focus. Axial tissue displacement was estimated using 1D-cross-correlation of RF signals acquired with a 7.8-MHz diagnostic transducer confocally aligned with the FUS. In vitro results in canine liver and kidney showed the correlation between HMI displacement and Young’s moduli measured by rheometry compression tests. HMI was able to provide reproducible elasticity maps of the mouse abdominal region in vivo allowing the identification of, from stiffest to softest, the murine kidney, pancreas, liver, and spleen. Finally, pancreata affected by pancreatitis and pancreatic cancer showed HMI displacements 1.7 and 2.2 times lower than in the control case, respectively, indicating higher stiffness. HMI displacement was correlated with the extent of fibrosis as well as detecting the very onset of stiffening even before fibrosis could be detected on H&E. This work shows that HMI can produce reliable elasticity maps of mouse abdominal region in vivo providing a crucial tool to understand pathologies affecting organ elasticity.

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“…Thus, we used a method specifically designed for MR images with improved robustness to image intensity fluctuations as described previously . The method has previously been validated in MRI for high‐frequency ultrasound . Unlike classical optical flow methods, it uses an L1 optical flow data fit term and an L2 regularizer (ie, an L1–L2 model):

∭ ∣ I_{x} u + I_{y} v + I_{z} w + I_{t} ∣ + β^{2} ((), {‖\nabla u‖}_{2}^{2} + {‖\nabla v‖}_{2}^{2} + {‖\nabla w‖}_{2}^{2}) italicdxdydz

…”

Section: Methodsmentioning

confidence: 99%

“…28 The method has previously been validated in MRI for high-frequency ultrasound. 29 Unlike classical optical flow methods, it uses an L1 optical flow data fit term and an L2 regularizer (ie, an L1-L2 model) 28 :…”

Section: Estimation Of Breathing-induced Deformation Fieldmentioning

confidence: 99%

Pancreas deformation in the presence of tumors using feature tracking from free‐breathing XD‐GRASP MRI

Chițiboi

Muckley

Dane

et al. 2019

Magnetic Resonance Imaging

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Background: Quantifying the biomechanical properties of pancreatic tumors could potentially help with assessment of tumor aggressiveness, prognosis, and prediction of therapy response. Purpose: To quantify respiratory-induced deformation in the pancreas and pancreatic lesions using XD-GRASP (eXtra-Dimensional Golden-angle RAdial Sparse Parallel), MRI. Study Type: Retrospective study where patients undergoing clinically indicated abdominal MRI which included free-breathing radial T1-weighted (T1W) imaging were studied. Subjects: Thirty-two patients (12 male and 20 female) including nine with pancreatic lesions constituted our study cohort. Field Strength/Sequence: 3.0 T with T1WI contrast-enhanced gradient echo radial free-breathing acquisition. Assessment: Using the XD-GRASP imaging technique, the acquired free-breathing radial data were sorted and binned into 10 consecutive respiratory motion states that were jointly reconstructed. 3D deformation fields along the respiratory dimension were computed using an optical flow method and were analyzed in the pancreas. Statistical Tests: The Wilcoxon signed-rank test was used to assess the difference in average displacement across pancreatic regions, while the Wilcoxon rank-sum test was used for displacement differences between patients with and without tumors. The interclass correlation coefficient (ICC) was computed to assess consistency between observers for each image quality measure. Results: There was a significantly larger displacement in the pancreatic tail compared with the head (8.2 ± 3.7 mm > 5.8 ± 2.4 mm; P < 0.001) and body regions (8.2 ± 3.7 mm > 6.6 ± 2.9 mm; P < 0.001). Furthermore, there was reduced normalized average displacement in patients with pancreatic lesions compared with subjects without lesions (0.33 ± 0.1 < 0.69 ± 0.26, P < 0.001 for the head; 0.30 ± 0.1 < 0.84 ± 0.31, P < 0.001 for the body; and 0.44 ± 0.31 < 1.08 ± 0.53, P < 0.001 for the tail, respectively). Data Conclusion: Free-breathing respiratory motion-sorted XD-GRASP MRI has the potential to noninvasively characterize the biomechanical properties of the pancreas by quantifying breathing-induced mechanical displacement.

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A framework for the correction of slow physiological drifts during MR‐guided HIFU therapies: Proof of concept

Cited by 43 publications

References 64 publications

A framework for continuous target tracking during MR-guided high intensity focused ultrasound thermal ablations in the abdomen

A framework for continuous target tracking during MR-guided high intensity focused ultrasound thermal ablations in the abdomen

Elasticity mapping of murine abdominal organsin vivousing harmonic motion imaging (HMI)

Pancreas deformation in the presence of tumors using feature tracking from free‐breathing XD‐GRASP MRI

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