Hassan Rivaz

Real-Time Regularized Ultrasound Elastography

This paper introduces two real-time elastography techniques based on analytic minimization (AM) of regularized cost functions. The first method (1D AM) produces axial strain and integer lateral displacement, while the second method (2D AM) produces both axial and lateral strains. The cost functions incorporate similarity of radio-frequency (RF) data intensity and displacement continuity, making both AM methods robust to small decorrelations present throughout the image. We also exploit techniques from robust statistics to make the methods resistant to large local decorrelations. We further introduce Kalman filtering for calculating the strain field from the displacement field given by the AM methods. Simulation and phantom experiments show that both methods generate strain images with high SNR, CNR and resolution. Both methods work for strains as high as 10% and run in real-time. We also present in vivo patient trials of ablation monitoring. An implementation of the 2D AM method as well as phantom and clinical RF-data can be downloaded.

Ultrasound Elastography: A Dynamic Programming Approach

This paper introduces a 2D strain imaging technique based on minimizing a cost function using dynamic programming (DP). The cost function incorporates similarity of echo amplitudes and displacement continuity. Since tissue deformations are smooth, the incorporation of the smoothness into the cost function results in reduced decorrelation noise. As a result, the method generates high-quality strain images of freehand palpation elastography with up to 10% compression, showing that the method is more robust to signal decorrelation (caused by scatterer motion in high axial compression and nonaxial motions of the probe) in comparison to the standard correlation techniques. The method operates in less than 1 s and is thus also potentially suitable for real time elastography.

Ablation Monitoring with Elastography: 2D In-vivo and 3D Ex-vivo Studies

The clinical feasibility of 2D elastography methods is hindered by the requirement that the operator avoid out-of-plane motion of the ultrasound image during palpation, and also by the lack of volumetric elastography measurements. In this paper, we develop and evaluate a 3D elastography method operating on volumetric data acquired from a 3D probe. Our method is based on minimizing a cost function using dynamic programming (DP). The cost function incorporates similarity of echo amplitudes and displacement continuity. We present, to the best of our knowledge, the first in-vivo patient studies of monitoring liver ablation with freehand DP elastography. The thermal lesion was not discernable in the B-mode image but it was clearly visible in the strain image as well as in validation CT. We also present 3D strain images from thermal lesions in ex-vivo ablation. Good agreement was observed between strain images, CT and gross pathology.

Intra-operative ultrasound elasticity imaging for monitoring of hepatic tumour thermal ablation

BACKGROUND Thermal ablation is an accepted therapy for selected hepatic malignancies. However, the reliability of thermal ablation is limited by the inability to accurately monitor and confirm completeness of tumour destruction in real time. We investigated the ability of ultrasound elasticity imaging (USEI) to monitor thermal ablation. OBJECTIVES Capitalizing on the known increased stiffness that occurs with protein denaturation and dehydration during thermal therapy, we sought to investigate the feasibility and accuracy of USEI for monitoring of liver tumour ablation. METHODS A model for hepatic tumours was developed and elasticity images of liver ablation were acquired in in vivo animal studies, comparing the elasticity images to gross specimens. A clinical pilot study was conducted using USEI in nine patients undergoing open radiofrequency ablation for hepatic malignancies. The size and shape of thermal lesions on USEI were compared to B-mode ultrasound and post-ablation computed tomography (CT). RESULTS In both in vivo animal studies and in the clinical trial, the boundary of thermal lesions was significantly more conspicuous on USEI when compared with B-mode imaging. Animal studies demonstrated good correlation between the diameter of ablated lesions on USEI and the gross specimen (r = 0.81). Moreover, high-quality strain images were generated in real time during therapy. In patients undergoing tumour ablation, a good size correlation was observed between USEI and post-operative CT (r = 0.80). CONCLUSION USEI can be a valuable tool for the accurate monitoring and real-time verification of successful thermal ablation of liver tumours.

Automatic Deformable MR-Ultrasound Registration for Image-Guided Neurosurgery

In this work, we present a novel algorithm for registration of 3-D volumetric ultrasound (US) and MR using Robust PaTch-based cOrrelation Ratio (RaPTOR). RaPTOR computes local correlation ratio (CR) values on small patches and adds the CR values to form a global cost function. It is therefore invariant to large amounts of spatial intensity inhomogeneity. We also propose a novel outlier suppression technique based on the orientations of the RaPTOR gradients. Our deformation is modeled with free-form cubic B-splines. We analytically derive the derivatives of RaPTOR with respect to the transformation, i.e., the displacement of the B-spline nodes, and optimize RaPTOR using a stochastic gradient descent approach. RaPTOR is validated on MR and tracked US images of neurosurgery. Deformable registration of the US and MR images acquired, respectively, preoperation and postresection is of significant clinical significance, but challenging due to, among others, the large amount of missing correspondences between the two images. This work is also novel in that it performs automatic registration of this challenging dataset. To validate the results, we manually locate corresponding anatomical landmarks in the US and MR images of tumor resection in brain surgery. Compared to rigid registration based on the tracking system alone, RaPTOR reduces the mean initial mTRE over 13 patients from 5.9 to 2.9 mm, and the maximum initial TRE from 17.0 to 5.9 mm. Each volumetric registration using RaPTOR takes about 30 sec on a single CPU core. An important challenge in the field of medical image analysis is the shortage of publicly available dataset, which can both facilitate the advancement of new algorithms to clinical settings and provide a benchmark for comparison. To address this problem, we will make our manually located landmarks available online.

Nonrigid Registration of Ultrasound and MRI Using Contextual Conditioned Mutual Information

Mutual information (MI) quantifies the information that is shared between two random variables and has been widely used as a similarity metric for multi-modal and uni-modal image registration. A drawback of MI is that it only takes into account the intensity values of corresponding pixels and not of neighborhoods. Therefore, it treats images as “bag of words” and the contextual information is lost. In this work, we present Contextual Conditioned Mutual Information (CoCoMI), which conditions MI estimation on similar structures. Our rationale is that it is more likely for similar structures to undergo similar intensity transformations. The contextual analysis is performed on one of the images offline. Therefore, CoCoMI does not significantly change the registration time. We use CoCoMI as the similarity measure in a regularized cost function with a B-spline deformation field and efficiently optimize the cost function using a stochastic gradient descent method. We show that compared to the state of the art local MI based similarity metrics, CoCoMI does not distort images to enforce erroneous identical intensity transformations for different image structures. We further present the results on nonrigid registration of ultrasound (US) and magnetic resonance (MR) patient data from image-guided neurosurgery trials performed in our institute and publicly available in the BITE dataset. We show that CoCoMI performs significantly better than the state of the art similarity metrics in US to MR registration. It reduces the average mTRE over 13 patients from 4.12 mm to 2.35 mm, and the maximum mTRE from 9.38 mm to 3.22 mm.

Ultrasound elastography using multiple images

Displacement estimation is an essential step for ultrasound elastography and numerous techniques have been proposed to improve its quality using two frames of ultrasound RF data. This paper introduces a technique for calculating a displacement field from three (or multiple) frames of ultrasound RF data. To calculate a displacement field using three images, we first derive constraints on variations of the displacement field with time using mechanics of materials. These constraints are then used to generate a regularized cost function that incorporates amplitude similarity of three ultrasound images and displacement continuity. We optimize the cost function in an expectation maximization (EM) framework. Iteratively reweighted least squares (IRLS) is used to minimize the effect of outliers. An alternative approach for utilizing multiple images is to only consider two frames at any time and sequentially calculate the strains, which are then accumulated. We formally show that, compared to using two images or accumulating strains, the new algorithm reduces the noise and eliminates ambiguities in displacement estimation. The displacement field is used to generate strain images for quasi-static elastography. Simulation, phantom experiments and in vivo patient trials of imaging liver tumors and monitoring ablation therapy of liver cancer are presented for validation. We show that even with the challenging patient data, where it is likely to have one frame among the three that is not optimal for strain estimation, the introduction of physics-based prior as well as the simultaneous consideration of three images significantly improves the quality of strain images. Average values for strain images of two frames versus ElastMI are: 43 versus 73 for SNR (signal to noise ratio) in simulation data, 11 versus 15 for CNR (contrast to noise ratio) in phantom data, and 5.7 versus 7.3 for CNR in patient data. In addition, the improvement of ElastMI over both utilizing two images and accumulating strains is statistically significant in the patient data, with p-values of respectively 0.006 and 0.012.

An Active Dynamic Vibration Absorber for a Hand-Held Vibro-Elastography Probe

Vibro-elastography is a new medical imaging method that identifies the mechanical properties of tissue by measuring tissue motion in response to a multi-frequency external vibration source. Previous research on vibro-elastography used ultrasound to measure the tissue motion and system identification techniques to identify the tissue properties. This paper describes a hand-held probe with a combined vibration source and ultrasound transducer to implement the new method as a practical device. The device uses a proportional integral active dynamic vibration absorber with an electromagnetic actuator to counterbalance the reaction forces from contact with the tissue. Experiments show an operational frequency range of 5–2 0 Hz, with at least 15 dB vibration absorption in 0.4 s for single frequency excitation. Experiments with variable frequency and amplitude excitation also show a high level of vibration absorption. DOI: 10.1115/1.2424982

Self-similarity weighted mutual information: A new nonrigid image registration metric

Extending mutual information (MI), which has been widely used as a similarity measure for rigid registration of multi-modal images, to deformable registration is an active field of research. We propose a self-similarity weighted graph-based implementation of alpha-mutual information (alpha-MI) for nonrigid image registration. The new Self Similarity alpha-MI (SeSaMI) metric takes local structures into account and is robust against signal non-stationarity and intensity distortions. We have used SeSaMI as the similarity measure in a regularized cost function with B-spline deformation field. Since the gradient of SeSaMI can be derived analytically, the cost function can be efficiently optimized using stochastic gradient descent. We show that SeSaMI produces a robust and smooth cost function and outperforms the state of the art statistical based similarity metrics in simulation and using data from image-guided neurosurgery.

9C-1 Beam Steering Approach for Speckle Characterization and Out-of-Plane Motion Estimation in Real Tissue

Out-of-plane motion in freehand 3D ultrasound can be estimated using the correlation of corresponding patches, leading to sensorless freehand 3D ultrasound systems. Previous work has shown that the motion estimation in a beef tissue is systematically underestimated by approximately 33% and that it can be improved to approximately 25% by limiting the correlation calculation to the fully developed speckle (FDS) patches [1]. The improvement in the accuracy is limited because FDS patches are rare in real tissue. Here, we propose beam steering for detecting FDS patches and we show that it significantly improves speckle detection. We further experiment the effect of beam steering on out-of-plane motion estimation using ex-vivo beef liver and steak experiment. Without steered images, we find a 17% error in the liver experiment. Beam steering reduces the error to 9%, a significant improvement which is mainly due to enhanced FDS detection. Beef steak results are even more promising: 14.8% error without beam steering is reduced to 3.2% error.