3-D Single Breath-Hold Shear Strain Estimation for Improved Breast Lesion Detection and Classification in Automated Volumetric Ultrasound Scanners

Automated 3-D Ultrasound Elastography of the Breast: An In Vivo Validation Study

OBJECTIVE

Studies have indicated that adding 2-D quasi-static elastography to B-mode ultrasound imaging improved the specificity for malignant lesion detection, as malignant lesions are often stiffer (increased strain ratio) compared with benign lesions. This method is limited by its user dependency and so unsuitable for breast screening. To overcome this limitation, we implemented quasi-static elastography in an automated breast volume scanner (ABVS), which is an operator-independent 3-D ultrasound system and is especially useful for screening women with dense breasts. The study aim was to investigate if 3-D quasi-static elastography implemented in a clinically used ABVS can discriminate between benign and malignant breast lesions.

METHODS

Volumetric breast ultrasound radiofrequency data sets of 82 patients were acquired before and after automated transducer lifting. Lesions were annotated and strain was calculated using an in-house-developed strain algorithm. Two strain ratio types were calculated per lesion: using axial and maximal principal strain (i.e., strain in dominant direction).

RESULTS

Forty-four lesions were detected: 9 carcinomas, 23 cysts and 12 other benign lesions. A significant difference was found between malignant (median: 1.7, range: [1.0-3.2]) and benign (1.0, [0.6-1.9]) using maximal principal strain ratios. Axial strain ratio did not reveal a significant difference between benign (0.6, [-12.7 to 4.9]) and malignant lesions (0.8, [-3.5 to 5.1]).

CONCLUSION

Three-dimensional strain imaging was successfully implemented on a clinically used ABVS to obtain, visualize and analyze in vivo strain images in three dimensions. Results revealed that maximal principal strain ratios are significantly increased in malignant compared with benign lesions.

Ultrasound-guided breast biopsy using an adapted automated cone-based ultrasound scanner: a feasibility study

BACKGROUND

Among available breast biopsy techniques, ultrasound (US)-guided biopsy is preferable because it is relatively inexpensive and provides live imaging feedback. The availability of magnetic resonance imaging (MRI)-3D US image fusion would facilitate US-guided biopsy even for US occult lesions to reduce the need for expensive and time-consuming MRI-guided biopsy. In this paper, we propose a novel Automated Cone-based Breast Ultrasound Scanning and Biopsy System (ACBUS-BS) to scan and biopsy breasts of women in prone position. It is based on a previously developed system, called ACBUS, that facilitates MRI-3D US image fusion imaging of the breast employing a conical container filled with coupling medium.

PURPOSE

The purpose of this study was to introduce the ABCUS-BS system and demonstrate its feasibility for biopsy of US occult lesions.

METHOD

The biopsy procedure with the ACBUS-BS comprises four steps: target localization, positioning, preparation, and biopsy. The biopsy outcome can be impacted by 5 types of errors: due to lesion segmentation, MRI-3D US registration, navigation, lesion tracking during repositioning, and US inaccuracy (due to sound speed difference between the sample and the one used for image reconstruction). For the quantification, we use a soft custom-made polyvinyl alcohol phantom (PVA) containing eight lesions (three US-occult and five US-visible lesions of 10 mm in diameter) and a commercial breast mimicking phantom with a median stiffness of 7.6 and 28 kPa, respectively. Errors of all types were quantified using the custom-made phantom. The error due to lesion tracking was also quantified with the commercial phantom. Finally, the technology was validated by biopsying the custom-made phantom and comparing the size of the biopsied material to the original lesion size. The average size of the 10-mm-sized lesions in the biopsy specimen was 7.00 ± 0.92 mm (6.33 ± 1.16 mm for US occult lesions, and 7.40 ± 0.55 mm for US-visible lesions).

RESULTS

For the PVA phantom, the errors due to registration, navigation, lesion tracking during repositioning, and US inaccuracy were 1.33, 0.30, 2.12, and 0.55 mm. The total error was 4.01 mm. For the commercial phantom, the error due to lesion tracking was estimated at 1.10 mm, and the total error was 4.11 mm. Given these results, the system is expected to successfully biopsy lesions larger than 8.22 mm in diameter. Patient studies will have to be carried out to confirm this in vivo.

CONCLUSION

The ACBUS-BS facilitates US-guided biopsy of lesions detected in pre-MRI and therefore might offer a low-cost alternative to MRI-guided biopsy. We demonstrated the feasibility of the approach by successfully taking biopsies of five US-visible and three US-occult lesions embedded in a soft breast-shaped phantom.

Machine Learning Approach to Quadratic Programming-Based Microwave Imaging for Breast Cancer Detection

In this work, a novel technique is proposed that combines the Born iterative method, based on a quadratic programming approach, with convolutional neural networks to solve the ill-framed inverse problem coming from microwave imaging formulation in breast cancer detection. The aim is to accurately recover the permittivity of breast phantoms, these typically being strong dielectric scatterers, from the measured scattering data. Several tests were carried out, using a circular imaging configuration and breast models, to evaluate the performance of the proposed scheme, showing that the application of convolutional neural networks allows clinicians to considerably reduce the reconstruction time with an accuracy that exceeds 90% in all the performed validations.

2D tissue strain tensor imaging in quasi-static ultrasound elastography. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:2847-2851. [PMID: 34891841 DOI: 10.1109/embc46164.2021.9630570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Accurately estimating all strain components in quasi-static ultrasound elastography is crucial for the full analysis of biological media. In this paper, 2D strain tensor imaging is investigated, using a partial differential equation (PDE)-based regularization method. More specifically, this method employs the tissue property of incompressibility to smooth the displacement fields and reduce the noise in the strain components. The performance of the method is assessed with phantoms and in vivo breast tissues. For all the media examined, the results showed a significant improvement in both lateral displacement and strain but also, to a lesser extent, in the shear strain. Moreover, axial displacement and strain were only slightly modified by the regularization, as expected. Finally, the easier detectability of the inclusion/lesion in the final lateral strain images is associated with higher elastographic contrast-to-noise ratios (CNRs), with values in the range [0.68 - 9.40] vs [0.09 - 0.38] before regularization.

The Viability of 3-D Power Doppler Imaging Using Continuous Mechanical Translation: Simulation and Theoretical Analysis

Although conventional Doppler ultrasound is widely used for quantifying blood flow, it is restricted by its low sensitivity to detect slow flow. The incorporation of ultrafast ultrasound and spatial-temporal clutter filters can not only extensively boost the Doppler sensitivity to low-velocity slow flow but also facilitate the development of advanced 3-D Doppler techniques. In this work, we propose a novel 3-D Doppler method which extends 2-D imaging to 3-D through the continuous mechanical translation of a linear transducer. The viability of this method is assessed by simulations with the aids of a theoretical model. The combination of simulations and the theoretical model provides unique insights into the inherent mechanisms involved in the performance of this 3-D Doppler method and the roles of factors, such as tissue vibration characteristics, blood flow velocity, elevational point-spread-function profile, probe translating speed, and signal energy ratios.

In vivo 3D Power Doppler Imaging Using Continuous Translation and Ultrafast Ultrasound

The introduction of ultrafast ultrasound and spatiotemporal filtering has significantly improved the sensitivity of Doppler ultrasound imaging. This work describes the development of a novel 3D power Doppler imaging technique which uses a 1D-array ultrasound probe that mechanically translates at a constant speed. The continuous translation allows for a fast scan of a large 3D volume without requiring complex hardware. The technique was realized in a prototype and its feasibility illustrated using phantom and in-vivo kidney and breast lesion experiments. Although this 3D Doppler imaging technique is limited in some aspects, it enables power Doppler imaging of a large volume in a short acquisition time with less computational costs.

Optimization of transmission and reconstruction parameters in angular displacement compounding using plane wave ultrasound

In ultrasound elastography, plane-wave acquisitions and angular displacement compounding (ADC) are often used and combined to allow high frame rates and to improve accuracy of lateral displacement estimates, respectively. This study investigates the performance of displacement and strain estimation for ADC as a function of; the main-to-grating-lobe-amplitude ratio which decreases as a function of steering angle; plane-wave acquisition and Delay-and-Sum (DaS)-related parameters; and grating-lobe filter cut-off frequency. Three experiments were conducted with a block phantom to test ADC performance for displacement fields of varying complexity: a lateral transducer shift, phantom rotation and phantom deformation. Experiments were repeated for four linear array transducers (pitch-to-lambda ratios between 0.6 and 1.4). Best ADC performance was found for steering angles that resulted in a theoretically derived main-to-grating-lobe-amplitude ratio of 1.7 dB for pure lateral translation and 6 dB for predominately lateral strain or rotation. Temporal filtering to reduce grating lobe signal or shifting of the receive aperture to receive angles below or above the optimal angle, as dictated by the main-to-grating-lobe-amplitude ratio, did not improve results. The accuracy of lateral displacement and strain estimates was improved by apodization in transmission and a dedicated F-number in DaS (0.75) allowing incidence angles within ± 33° in the active aperture. ADC with the optimized settings as found in this study improves the accuracy of displacements and strain estimates up to 80.7% compared to non-ADC. Compared to ADC settings described in current literature, our optimization improved the accuracy by 11.9% to 75.3% for lateral displacement and strain, and by 89.3% to 96.2% for rotation. The accuracy of ADC in rotation seemed to depend highly on plane-wave and DaS-related parameters which may explain the major improvement compared to settings in current literature. The overall improvement by optimized ADC was statistically significant compared to non-ADC (p = 0.003) and literature (p = 0.002).

Physics-guided machine learning for 3-D quantitative quasi-static elasticity imaging

We present a 3D extension of the Autoprogressive Method (AutoP) for quantitative quasi-static ultrasonic elastography (QUSE) based on sparse sampling of force-displacement measurements. Compared to current model-based inverse methods, our approach requires neither geometric nor constitutive model assumptions. We build upon our previous report for 2D QUSE and demonstrate the feasibility of recovering the 3D linear-elastic material property distribution of gelatin phantoms under compressive loads. Measurements of boundary geometry, applied surface forces, and axial displacements enter into AutoP where a Cartesian neural network constitutive model (CaNNCM) interacts with finite element analyses to learn physically consistent material properties with no prior constitutive model assumption. We introduce a new regularization term uniquely suited to AutoP that improves the ability of CaNNCMs to extract information about spatial stress distributions from measurement data. Results of our study demonstrate that acquiring multiple sets of force-displacement measurements by moving the US probe to different locations on the phantom surface not only provides AutoP with the necessary information for a CaNNCM to learn the 3D material property distribution, but may significantly improve the accuracy of the Young's modulus estimates. Furthermore, we investigate the trade-offs of decreasing the contact area between the US transducer and phantom surface in an effort to increase sensitivity to surface force variations without additional instrumentation. Each of these modifications improves the ability of CaNNCMs trained in AutoP to learn the spatial distribution of Young's modulus from force-displacement measurements.

Multi-frequency Integration Algorithm of Contrast Source Inversion Method for Microwave Breast Tumor Detection

Microwave mammography is one of the most promising alternatives to X-ray-based breast cancer detection techniques, where a malignant tumor has a certain level of dielectric property contrast compared with those in normal tissues. However, the inverse problem of reconstructing complex permittivity is a non-linear and ill-posed problem, and the appropriate selection of such algorithms is the key to the success of microwave mammography. The contrast source inversion (CSI) method is the most promising solution to the above problem, where the iterative procedure does not require a computationally expensive forward solver, like the finite difference time domain (FDTD) method. However, the conventional CSI method assumes a non-dispersive dielectric model, while breast or other human tissues have a nonnegligible dispersive property. To address this problem, this paper introduces an extended CSI method, which is suitable for dispersive medium and in which multi-frequency integration is introduced to enhance the reconstruction accuracy. The FDTD numerical test, which uses a realistic breast phantom via magnetic resonance imaging (MRI), demonstrates that our proposed method efficiently enhances the reconstruction accuracy even in dispersive medium.