Viscoelasticity Modeling of the Prostate Region Using Vibro-elastography

Quantifying the Impact of Cancer on the Viscoelastic Properties of the Prostate Gland using a Quasi-Linear Viscoelastic Model

Pathological disorders can alter the mechanical properties of biological tissues, and studying such changes can help to better understand the disease progression. The prostate gland is no exception, as previous studies have shown that cancer can affect its mechanical properties. However, most of these studies have focused on the elastic properties of the tissue and have overlooked the impact of cancer on its viscous response. To address this gap, we used a quasi-linear viscoelastic model to investigate the impact of cancer on both the elastic and viscous characteristics of the prostate gland. By comparing the viscoelastic properties of segments influenced by cancer and those unaffected by cancer in 49 fresh prostates, removed within two hours after prostatectomy surgery, we were able to determine the influence of cancer grade and tumor volume on the tissue. Our findings suggest that tumor volume significantly affects both the elastic modulus and viscosity of the prostate (p-value less than 2%). Specifically, we showed that cancer increases Young's modulus and shear relaxation modulus by 20%. These results have implications for using mechanical properties of the prostate as a potential biomarker for cancer. However, developing an in vivo apparatus to measure these properties remains a challenge that needs to be addressed in future research. STATEMENT OF SIGNIFICANCE: This study is the first to explore how cancer impacts the mechanical properties of prostate tissues using a quasi-linear viscoelastic model. We examined 49 fresh prostate samples collected immediately after surgery and correlated their properties with cancer presence identified in pathology reports. Our results demonstrate a 20% change in the viscoelastic properties of the prostate due to cancer. We initially validated our approach using tissue-mimicking phantoms and then applied it to differentiate between cancerous and normal prostate tissues. These findings offer potential for early cancer detection by assessing these properties. However, conducting these tests in vivo remains a challenge for future research.

Experimental evidence of shear waves in fractional viscoelastic rheological models

Fractional viscoelastic rheological models, such as the Kelvin Voigt Fractional Derivative model, have been proposed in the literature for modelling shear wave propagation in soft tissue. In this article, our previously developed wave propagation model for transluminal propagation based on a Kelvin Voigt Fractional Derivative wave equation is experimentally validated. The transluminal procedure uses the transmission and detection of shear waves through the luminal wall. The model was compared against high-speed camera observations in translucent elastography phantoms with similar viscoelastic properties to prostate tissue. An ad hoc cross-correlation procedure was used to reconstruct the angular displacement from the high-speed camera observations. Rheometry and shear wave elastography were used for characterising the shear wave velocity dispersion curve for the phantoms. Fractional viscoelastic properties were derived after fitting the dispersion curve to its analytical expression. Propagation features and amplitude spectra from simulations and high-speed camera observations were compared. The obtained results indicate that the model replicates the experimental observations with acceptable accuracy. The model presented here provides a useful tool to model transluminal procedures based on wave propagation and its interaction with the mechanical properties of the tissue outside the lumen.

Application of the novel estimation method by shear wave elastography using vibrator to human skeletal muscle

In recent years, non-invasive measurement of tissue stiffness (hardness) using ultrasound elastography has attracted considerable attention. It has been used to evaluate muscle stiffness in the fields of rehabilitation, sports, and orthopedics. However, ultrasonic diagnostic devices with elastography systems are expensive and clinical use of such devices has been limited. In this study, we proposed a novel estimation method for vibration-based shear wave elastography measurement of human skeletal muscle, then determined its reproducibility and reliability. The coefficient of variation and correlation coefficient were used to determine reproducibility and reliability of the method by measuring the shear wave velocities in konjac phantom gels and agar phantom gels, as well as skeletal muscle. The intra-day, day-to-day, and inter-operator reliabilities were good when measuring the shear wave velocities in phantom gels. The intra-day and day-to-day reliabilities were good when measuring the shear wave velocities in skeletal muscle. The findings confirmed adequate reproducibility and reliability of the novel estimation method for vibration-based shear wave elastography. Therefore, the proposed measurement method may be a useful tool for evaluation of muscle stiffness.

Deep Recurrent Neural Networks for Prostate Cancer Detection: Analysis of Temporal Enhanced Ultrasound

Temporal enhanced ultrasound (TeUS), comprising the analysis of variations in backscattered signals from a tissue over a sequence of ultrasound frames, has been previously proposed as a new paradigm for tissue characterization. In this paper, we propose to use deep recurrent neural networks (RNN) to explicitly model the temporal information in TeUS. By investigating several RNN models, we demonstrate that long short-term memory (LSTM) networks achieve the highest accuracy in separating cancer from benign tissue in the prostate. We also present algorithms for in-depth analysis of LSTM networks. Our in vivo study includes data from 255 prostate biopsy cores of 157 patients. We achieve area under the curve, sensitivity, specificity, and accuracy of 0.96, 0.76, 0.98, and 0.93, respectively. Our result suggests that temporal modeling of TeUS using RNN can significantly improve cancer detection accuracy over previously presented works.

Toward a real-time system for temporal enhanced ultrasound-guided prostate biopsy

PURPOSE

We have previously proposed temporal enhanced ultrasound (TeUS) as a new paradigm for tissue characterization. TeUS is based on analyzing a sequence of ultrasound data with deep learning and has been demonstrated to be successful for detection of cancer in ultrasound-guided prostate biopsy. Our aim is to enable the dissemination of this technology to the community for large-scale clinical validation.

METHODS

In this paper, we present a unified software framework demonstrating near-real-time analysis of ultrasound data stream using a deep learning solution. The system integrates ultrasound imaging hardware, visualization and a deep learning back-end to build an accessible, flexible and robust platform. A client-server approach is used in order to run computationally expensive algorithms in parallel. We demonstrate the efficacy of the framework using two applications as case studies. First, we show that prostate cancer detection using near-real-time analysis of RF and B-mode TeUS data and deep learning is feasible. Second, we present real-time segmentation of ultrasound prostate data using an integrated deep learning solution.

RESULTS

The system is evaluated for cancer detection accuracy on ultrasound data obtained from a large clinical study with 255 biopsy cores from 157 subjects. It is further assessed with an independent dataset with 21 biopsy targets from six subjects. In the first study, we achieve area under the curve, sensitivity, specificity and accuracy of 0.94, 0.77, 0.94 and 0.92, respectively, for the detection of prostate cancer. In the second study, we achieve an AUC of 0.85.

CONCLUSION

Our results suggest that TeUS-guided biopsy can be potentially effective for the detection of prostate cancer.

Investigation of Physical Phenomena Underlying Temporal-Enhanced Ultrasound as a New Diagnostic Imaging Technique: Theory and Simulations

Temporal-enhanced ultrasound (TeUS) is a novel noninvasive imaging paradigm that captures information from a temporal sequence of backscattered US radio frequency data obtained from a fixed tissue location. This technology has been shown to be effective for classification of various in vivo and ex vivo tissue types including prostate cancer from benign tissue. Our previous studies have indicated two primary phenomena that influence TeUS: 1) changes in tissue temperature due to acoustic absorption and 2) micro vibrations of tissue due to physiological vibration. In this paper, first, a theoretical formulation for TeUS is presented. Next, a series of simulations are carried out to investigate micro vibration as a source of tissue characterizing information in TeUS. The simulations include finite element modeling of micro vibration in synthetic phantoms, followed by US image generation during TeUS imaging. The simulations are performed on two media, a sparse array of scatterers and a medium with pathology mimicking scatterers that match nuclei distribution extracted from a prostate digital pathology data set. Statistical analysis of the simulated TeUS data shows its ability to accurately classify tissue types. Our experiments suggest that TeUS can capture the microstructural differences, including scatterer density, in tissues as they react to micro vibrations.

Computer-Aided Prostate Cancer Detection Using Ultrasound RF Time Series: In Vivo Feasibility Study

This paper presents the results of a computer-aided intervention solution to demonstrate the application of RF time series for characterization of prostate cancer, in vivo.

METHODS

We pre-process RF time series features extracted from 14 patients using hierarchical clustering to remove possible outliers. Then, we demonstrate that the mean central frequency and wavelet features extracted from a group of patients can be used to build a nonlinear classifier which can be applied successfully to differentiate between cancerous and normal tissue regions of an unseen patient.

RESULTS

In a cross-validation strategy, we show an average area under receiver operating characteristic curve (AUC) of 0.93 and classification accuracy of 80%. To validate our results, we present a detailed ultrasound to histology registration framework.

CONCLUSION

Ultrasound RF time series results in differentiation of cancerous and normal tissue with high AUC.

Quantitative shear elasticity imaging from a complex elastic wavefield in soft solids with application to passive elastography

In passive elastography, the complex physiological noise present in the human body is used to conduct an elastography experiment. In the present work, quantitative shear elasticity imaging from a complex elastic wavefield is demonstrated in soft solids. By correlating the elastic field at different positions, which can be interpreted as a time-reversal experiment, shear waves are virtually focused on any point of the imaging plane. According to the Rayleigh criterion, the focus size is directly related to the shear wave speed and thus to the shear elasticity. To locally retrieve a shear wave speed estimation, analytical and empirical expressions that relate the focus size with the shear wave speed and the frequency band used in the correlation computation are derived. The validity of such expressions is demonstrated numerically and experimentally on a tissue-mimicking phantom consisting of two different elastic layers. The obtained results were in complete agreement with a prior shear wave speed estimation demonstrating the potential of the technique to quantitative shear elasticity assessment using a complex elastic wavefield. Finally, an ultraslow experiment at an imaging rate of 10 Hz shows the technique to be compatible with slow imaging devices such as standard echographs or MRI scanners.

Efficient and optimized identification of generalized Maxwell viscoelastic relaxation spectra

Viscoelastic relaxation spectra are essential for predicting and interpreting the mechanical responses of materials and structures. For biological tissues, these spectra must usually be estimated from viscoelastic relaxation tests. Interpreting viscoelastic relaxation tests is challenging because the inverse problem is expensive computationally. We present here an efficient algorithm that enables rapid identification of viscoelastic relaxation spectra. The algorithm was tested against trial data to characterize its robustness and identify its limitations and strengths. The algorithm was then applied to identify the viscoelastic response of reconstituted collagen, revealing an extensive distribution of viscoelastic time constants.

Ultrasound-based characterization of prostate cancer: an in vivo clinical feasibility study

This paper presents the results of an in vivo clinical study to accurately characterize prostate cancer using new features of ultrasound RF time series.

METHODS

The mean central frequency and wavelet features of ultrasound RF time series from seven patients are used along with an elaborate framework of ultrasound to histology registration to identify and verify cancer in prostate tissue regions as small as 1.7 mm x 1.7 mm.

RESULTS

In a leave-one-patient-out cross-validation strategy, an average classification accuracy of 76% and the area under ROC curve of 0.83 are achieved using two proposed RF time series features. The results statistically significantly outperform those achieved by previously reported features in the literature. The proposed features show the clinical relevance of RF time series for in vivo characterization of cancer.

Simulation-based joint estimation of body deformation and elasticity parameters for medical image analysis

Estimation of tissue stiffness is an important means of noninvasive cancer detection. Existing elasticity reconstruction methods usually depend on a dense displacement field (inferred from ultrasound orMR images) and known external forces.Many imaging modalities, however, cannot provide details within an organ and therefore cannot provide such a displacement field. Furthermore, force exertion and measurement can be difficult for some internal organs, making boundary forces another missing parameter. We propose a general method for estimating elasticity and boundary forces automatically using an iterative optimization framework, given the desired (target) output surface. During the optimization, the input model is deformed by the simulator, and an objective function based on the distance between the deformed surface and the target surface is minimized numerically. The optimization framework does not depend on a particular simulation method and is therefore suitable for different physical models. We show a positive correlation between clinical prostate cancer stage (a clinical measure of severity) and the recovered elasticity of the organ. Since the surface correspondence is established, our method also provides a non-rigid image registration, where the quality of the deformation fields is guaranteed, as they are computed using a physics-based simulation.

Subzone based magnetic resonance elastography using a Rayleigh damped material model

PURPOSE

Recently, the attenuating behavior of soft tissue has been addressed in magnetic resonance elastography by the inclusion of a damping mechanism in the methods used to reconstruct the resulting mechanical property image. To date, this mechanism has been based on a viscoelastic model for material behavior. Rayleigh, or proportional, damping provides a more generalized model for elastic energy attenuation that uses two parameters to characterize contributions proportional to elastic and inertial forces. In the case of time-harmonic vibration, these two parameters lead to both the elastic modulus and the density being complex valued (as opposed to the case of pure viscoelasticity, where only the elastic modulus is complex valued).

METHODS

This article presents a description of Rayleigh damping in the time-harmonic case, discussing the differences between this model and the viscoelastic damping models. In addition, the results from a subzone based Rayleigh damped elastography study of gelatin and tofu phantoms are discussed, along with preliminary results from in vivo breast data.

RESULTS

Both the phantom and the tissue studies presented here indicate a change in the Rayleigh damping structure, described as Rayleigh composition, between different material types, with tofu and healthy tissue showing lower Rayleigh composition values than gelatin or cancerous tissue.

CONCLUSIONS

It is possible that Rayleigh damping elastography and the concomitant Rayleigh composition images provide a mechanism for differentiating tissue structure in addition to measuring elastic stiffness and attenuation. Such information could be valuable in the use of Rayleigh damped magnetic resonance elastography as a diagnostic imaging tool.

Tissue typing using ultrasound RF time series: experiments with animal tissue samples

PURPOSE

This article provides experimental evidence to show that the time series of radiofrequency (RF) ultrasound data can be used for tissue typing. It also explores the tissue typing information in RF time series. Clinical and high-frequency ultrasound are studied.

METHODS

Bovine liver, pig liver, bovine muscle, and chicken breast were used in the experiments as the animal tissue types. In the proposed approach, the authors record RF echo signals backscattered from tissue, while the imaging probe and the tissue are stationary. This sequence of recorded RF data generates a time series of RF echoes for each spatial sample of the RF signal. The authors use spectral and fractal features of ultrasound RF time series averaged over a region of interest, along with feedforward neural networks for tissue typing. The experiments are repeated at ultrasound frequency of 6.6 and also 55 MHz. The effects of increasing power and frame rate are studied.

RESULTS

The methodology yielded an average two-class classification accuracy of 95.1% when ultrasound data were acquired at 6.6 MHz and 98.1% when data were collected with a high-frequency probe operating at 55 MHz. In four-class classification experiments, the recorded accuracies were 78.6% and 86.5% for low and high-frequency ultrasound data, respectively. A set of 12 texture features extracted from the B-mode image equivalents of the RF data yields an accuracy of only 77.5% in typing the analyzed tissues. An increase in acoustic power and the frame rate of ultrasound results in an improvement in classification results.

CONCLUSIONS

The results of this study demonstrate that RF time series can be used for ultrasound-based tissue typing. Further investigation of the underlying physical mechanisms is necessary.