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Multi-perspective Ultrasound Strain Imaging & Elastography

Periodic Reporting for period 2 - MUSE (Multi-perspective Ultrasound Strain Imaging & Elastography)

Reporting period: 2019-08-01 to 2021-01-31

Cardiovascular disease is still the leading cause of death worldwide. In this project we focus on the heart and the aorta. Current clinical decision making is based on several biomedical measurements and imaging, yielding geometrical and functional parameters that are compared to large population statistics (evidence-based medicine). In our group, we strive to assist clinical decision making based on accurate, functional measurements, and patients-specific models of the diseased organ.

Ultrasound imaging is a widely used imaging technique, one of the workhorses in the clinic, that is frequently used for imaging of the cardiovascular system. Ultrasound (US) has several advantages, it is non-invasive, relatively cheap, can be used at the patient’s bedside, and most importantly: is known for its high temporal and spatial resolution. Unfortunately, US is also known for its drawbacks, which are a limited field-of-view and contrast. The latter is anisotropic, as well as the resolution. In this project, we aim to tackle these physical limitations by developing so-called multiperspective US imaging techniques.

The project has four objectives:
1) developing a novel imaging platform, including
2) new methods for functional measurements on organs;
3) create patient-specific models based on these new functional imaging techniques and
4) perform extensive experimental verification and in vivo validation of the methods proposed.

In multiperspective ultrasound imaging (MPUS), we use multiple transducers rather than a single one as is the case in conventional US imaging. Systems are developed that can perform MPUS in 2-D and 3D. Algorithms are developed that can fuse the image data obtained with both transducers and/or reconstruct high quality images (higher contrast, improved resolution), with a larger field-of-view. With these new systems it is crucial to know where the probes are, a problem that is tackled using both dedicated US acquisition and image analysis techniques.

Based on the improved MPUS images, new segmentation and elastography techniques have been developed. Thanks to the availability of MPUS data, segmentation and motion estimation have improved significantly or has become feasible at all (in case of the aorta). Moreover, the data can be used to improve patient-specific models of the aorta that are currently developed to assess the mechanical state of the vessel, parameters that cannot be assessed via measurements.

To translate these new methods from bench (design/development phase) to bedside (clinical pilots), simulations and setups were introduced to test and verify the performance of the imaging techniques proposed, and in vivo pilot studies were set up. A simulation framework that can produce realistic MPUS images with a known ground truth on geometry or motion was developed, as well as experimental setups that allow testing of the new MPUS systems under more realistic conditions (i.e. biological tissue). Finally, volunteer and patient pilot studies have been organized, that allow for in vivo validation.
Systems have been developed to perform multiperspective US imaging using both high-end clinical scanners and so-called open source US systems. The latter were used to develop new US acquisition schemes that allow for multi-perspective imaging at high frame rates. Experimental imaging was tested using two-dimensional and three-dimensional transducers, for imaging dynamic porcine ex-vivo aortas and hearts. Moreover, first multi-perspective ultrafast 3D US acquisitions have been performed using two matrix array transducers. Image reconstruction and smart image fusion of these high-frame-rate images were performed after acquisition in a custom developed framework.

An algorithm for fusion of 2-D and 3D images applicable for the cardiovascular system has been developed. The fusion algorithm preserves the anatomical structures, expands the field-of-view, and mitigates noise, whilst preserving US speckles required for motion estimation. In the fusion algorithm, a novel near-field clutter reduction filter was implemented, which reduces the aberration and reverberation artefacts, leading to improved image quality and motion estimation results. The fusion algorithm is being/ has been validated in ex-vivo beating hearts, healthy volunteers (aorta & cardiac), and in the clinic (abdominal aortic aneurysm (AAA) patients and in the future aortic stenosis (AoS) patients).

Methods for geometry assessment and mechanical characterization of AAAs have been developed. Methods for automated MP image registration, both in time and space, and AAA segmentation have been developed and are currently verified. A fully automated modeling pipeline for global assessment of mechanical properties of AAAs has been developed, tested on simulations, and is ready for MPUS imaging. Pilot in vivo studies in volunteers and patients have been initiated and are currently ongoing for clinical introduction and validation of the methods proposed.
For WP-1, in the upcoming period, a major action will be the implementation of a (bistatic) 3D ultrafast MPUS image acquisition scheme. Furthermore, the registration of the MPUS images will be fully automated (using conventional image processing as well as deep learning approaches) and validated using rotation and translation sensors attached to the probe to perform (semi-)free-hand MPUS in both 2-D and 3D. Besides image based registration, receive data based probe localization will be translated from 2D to 3D ultrasound, allowing for a robust registration even for angles, where common image features have fully decorrelated. Near results also include the optimization of acquisition and reconstruction of ultrafast multi-perspective 3D US, using two matrix array transducers connected to multiple US systems.

For WP-2, the 3D motion estimation will be optimized using improved motion estimation algorithms and smart acquisitions schemes to improve the 3D frame rate. 3D MPUS strain estimation will be compared to the gold standard (MRI) in healthy volunteers and AoS patients. Furthermore, currently, hands-free multi-perspective motion estimation is also being tested in upright, exercise stress echocardiography in healthy volunteers. Ultrafast 3D RF data obtained with multiple probes can be reconstructed at a fast pace to perform 3D RF speckle tracking.

For WP3, the expected progress in the upcoming period will be the execution of the clinical study, yielding the first MPUS imaging data in AAA patients using clinical systems, but also first-in-man ultrafast MPUS data of aortas. The improvements in the MPUS-based strain imaging and mechanical characterization of AAAs (compared to conventional 3D US and CT) will be assessed and verified using simulations of the abdomen and our experimental infrastructure. The expected outcome is a fully automated platform for the assessment of the vascular state using free-hand, multiperspective, 3D US imaging, assessing local stresses, strains and stiffness in a non-invasive manner.

For WP-4, the clinical validation of MPUS and MPUS-based elastography in AAAs and AoS patients will be performed in the near future. The MPUS data will be compared to single perspective US, and to the gold standard, tagged MRI and contrast-enhanced CT imaging, respectively. Also, US simulations will be extended to verify the outcome of experimentally obtained results of MPUS acquisitions and fusion strain imaging.