Wall shear stress estimation in the aorta: Impact of wall motion, spatiotemporal resolution, and phase noise.

BACKGROUND: Wall shear stress (WSS) presents an important parameter for assessing blood flow characteristics and evaluating flow-mediated lesions in the aorta. PURPOSE: To investigate the robustness of WSS and oscillatory shear index (OSI) estimation based on 4D flow MRI against vessel wall motion, spatiotemporal resolution, and velocity encoding (VENC). STUDY TYPE: Simulated and prospective. POPULATION: Synthetic 4D flow MRI data of the aorta, simulated using the Lattice-Boltzmann method; in vivo 4D flow MRI data of the aorta from healthy volunteers (n = 11) and patients with congenital heart defects (n = 17). FIELD STRENGTH/SEQUENCE: 1.5T; 4D flow MRI with PEAK-GRAPPA acceleration and prospective electrocardiogram triggering. ASSESSMENT: Predicated upon 3D cubic B-splines interpolation of the image velocity field, WSS was estimated in mid-systole, early-diastole, and late-diastole and OSI was derived. We assessed the impact of spatiotemporal resolution and phase noise, and compared results based on tracked-using deformable registration-and static vessel wall location. STATISTICAL TESTS: Bland-Altman analysis to assess WSS/OSI differences; Hausdorff distance (HD) to assess wall motion; and Pearson's correlation coefficient (PCC) to assess correlation of HD with WSS. RESULTS: Synthetic data results show systematic over-/underestimation of WSS when different spatial resolution (mean ± 1.96 SD up to -0.24 ± 0.40 N/m2 and 0.5 ± 1.38 N/m2 for 8-fold and 27-fold voxel size, respectively) and VENC-depending phase noise (mean ± 1.96 SD up to 0.31 ± 0.12 N/m2 and 0.94 ± 0.28 N/m2 for 2-fold and 4-fold VENC increase, respectively) are given. Neglecting wall motion when defining the vessel wall perturbs WSS estimates to a considerable extent (1.96 SD up to 1.21 N/m2 ) without systematic over-/underestimation (Bland-Altman mean range -0.06 to 0.05). DATA CONCLUSION: In addition to sufficient spatial resolution and velocity to noise ratio, accurate tracking of the vessel wall is essential for reliable image-based WSS estimation and should not be neglected if wall motion is present. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Evidence-based cross validation for acoustic power transmission for a novel treatment system.

INTRODUCTION: The novel Trans-Fusimo Treatment System (TTS) is designed to control Magnetic Resonance guided Focused Ultrasound (MRgFUS) therapy to ablate liver tumours under respiratory motion. It is crucial to deliver the acoustic power within tolerance limits for effective liver tumour treatment via MRgFUS. Before application in a clinical setting, evidence of reproducibility and reliability is a must for safe practice. MATERIALS AND METHODS: The TTS software delivers the acoustic power via ExAblate-2100 Conformal Bone System (CBS) transducer. A built-in quality assurance application was developed to measure the force values, using a novel protocol to measure the efficiency for the electrical power values of 100 and 150W for 6s of sonication. This procedure was repeated 30 times by two independent users against the clinically approved ExAblate-2100 CBS for cross-validation. RESULTS: Both systems proved to deliver the power within the accepted efficiency levels (70-90%). Two sample t-tests were used to assess the differences in force values between the ExAblate-2100 CBS and the TTS (p > 0.05). Bland-Altman plots were used to demonstrate the limits of agreement between the two systems falling within the 10% limits of agreement. Two sample t-tests indicated that TTS does not have user dependency (p > 0.05). CONCLUSIONS: The TTS software proved to deliver the acoustic power without exceeding the safety levels. Results provide evidence as a part of ISO13485 regulations for CE marking purposes. The developed methodology could be utilised as a part of quality assurance system in clinical settings; when the TTS is used in clinical practice.

Methodology on quantification of sonication duration for safe application of MR guided focused ultrasound for liver tumour ablation.

BACKGROUND AND OBJECTIVE: Magnetic Resonance Guided Focused Ultrasound (MRgFUS) for liver tumour ablation is a challenging task due to motion caused by breathing and occlusion due the ribcage between the transducer and the tumour. To overcome these challenges, a novel system for liver tumour ablation during free breathing has been designed. METHODS: The novel TRANS-FUSIMO Treatment System (TTS, EUFP7) interacts with a Magnetic Resonance (MR) scanner and a focused ultrasound transducer to sonicate to a moving target in liver. To meet the requirements of ISO 13485; a quality management system for medical device design, the system needs to be tested for certain process parameters. The duration of sonication and, the delay after the sonication button is activated, are among the parameters that need to be quantified for efficient and safe ablation of tumour tissue. A novel methodology is developed to quantify these process parameters. A computerised scope is programmed in LabVIEW to collect data via hydrophone; where the coordinates of fiber-optic sensor assembly was fed into the TRANS-FUSIMO treatment software via Magnetic Resonance Imaging (MRI) to sonicate to the tip of the sensor, which is synchronised with the clock of the scope, embedded in a degassed water tank via sensor assembly holder. The sonications were executed for 50 W, 100 W, 150 W for 10 s to quantify the actual sonication duration and the delay after the emergency stop by two independent operators for thirty times. The deviation of the system from the predefined specs was calculated. Student's-T test was used to investigate the user dependency. RESULTS: The duration of sonication and the delay after the sonication were quantified successfully with the developed method. TTS can sonicate with a maximum deviation of 0.16 s (Std 0.32) from the planned duration and with a delay of 14 ms (Std 0.14) for the emergency stop. Student's T tests indicate that the results do not depend on operators (p > .05). CONCLUSION: The evidence obtained via this protocol is crucial for translation- of-research into the clinics for safe application of MRgFUS. The developed protocol could be used for system maintenance in compliance with quality systems in clinics for daily quality assurance routines.

A focused ultrasound treatment system for moving targets (part I): generic system design and in-silico first-stage evaluation.

BACKGROUND: Focused ultrasound (FUS) is entering clinical routine as a treatment option. Currently, no clinically available FUS treatment system features automated respiratory motion compensation. The required quality standards make developing such a system challenging. METHODS: A novel FUS treatment system with motion compensation is described, developed with the goal of clinical use. The system comprises a clinically available MR device and FUS transducer system. The controller is very generic and could use any suitable MR or FUS device. MR image sequences (echo planar imaging) are acquired for both motion observation and thermometry. Based on anatomical feature tracking, motion predictions are estimated to compensate for processing delays. FUS control parameters are computed repeatedly and sent to the hardware to steer the focus to the (estimated) target position. All involved calculations produce individually known errors, yet their impact on therapy outcome is unclear. This is solved by defining an intuitive quality measure that compares the achieved temperature to the static scenario, resulting in an overall efficiency with respect to temperature rise. To allow for extensive testing of the system over wide ranges of parameters and algorithmic choices, we replace the actual MR and FUS devices by a virtual system. It emulates the hardware and, using numerical simulations of FUS during motion, predicts the local temperature rise in the tissue resulting from the controls it receives. RESULTS: With a clinically available monitoring image rate of 6.67 Hz and 20 FUS control updates per second, normal respiratory motion is estimated to be compensable with an estimated efficiency of 80%. This reduces to about 70% for motion scaled by 1.5. Extensive testing (6347 simulated sonications) over wide ranges of parameters shows that the main source of error is the temporal motion prediction. A history-based motion prediction method performs better than a simple linear extrapolator. CONCLUSIONS: The estimated efficiency of the new treatment system is already suited for clinical applications. The simulation-based in-silico testing as a first-stage validation reduces the efforts of real-world testing. Due to the extensible modular design, the described approach might lead to faster translations from research to clinical practice.