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Eshaghinia SS, Taghvaeipour A, Aghdam MM, Rivaz H. On the soft tissue ultrasound elastography using FEM based inversion approach. Proc Inst Mech Eng H 2024; 238:271-287. [PMID: 38240143 DOI: 10.1177/09544119231224674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2024]
Abstract
Elastography is a medical imaging modality that enables visualization of tissue stiffness. It involves quasi-static or harmonic mechanical stimulation of the tissue to generate a displacement field which is used as input in an inversion algorithm to reconstruct tissue elastic modulus. This paper considers quasi-static stimulation and presents a novel inversion technique for elastic modulus reconstruction. The technique follows an inverse finite element framework. Reconstructed elastic modulus maps produced in this technique do not depend on the initial guess, while it is computationally less involved than iterative reconstruction approaches. The method was first evaluated using simulated data (in-silico) where modulus reconstruction's sensitivity to displacement noise and elastic modulus was assessed. To demonstrate the method's performance, displacement fields of two tissue mimicking phantoms determined using three different motion tracking techniques were used as input to the developed elastography method to reconstruct the distribution of relative elastic modulus of the inclusion to background tissue. In the next stage, the relative elastic modulus of three clinical cases pertaining to liver cancer patient were determined. The obtained results demonstrate reasonably high elastic modulus reconstruction accuracy in comparison with similar direct methods. Also it is associated with reduced computational cost in comparison with iterative techniques, which suffer from convergence and uniqueness issues, following the same formulation concept. Moreover, in comparison with other methods which need initial guess, the presented method does not require initial guess while it is easy to understand and implement.
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Affiliation(s)
- Seyed Shahab Eshaghinia
- Mechanical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
| | - Afshin Taghvaeipour
- Mechanical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
| | - Mohammad Mohammadi Aghdam
- Mechanical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
| | - Hassan Rivaz
- Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada
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2
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Boquet-Pujadas A, Olivo-Marin JC. Reformulating Optical Flow to Solve Image-Based Inverse Problems and Quantify Uncertainty. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023; 45:6125-6141. [PMID: 36040935 DOI: 10.1109/tpami.2022.3202855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
From meteorology to medical imaging and cell mechanics, many scientific domains use inverse problems (IPs) to extract physical measurements from image movement. To this end, motion estimation methods such as optical flow (OF) pre-process images into motion data to feed the IP, which then inverts for the measurements through a physical model. However, this combined OFIP pipeline exacerbates the ill-posedness inherent to each technique, propagating errors and preventing uncertainty quantification. We introduce a Bayesian PDE-constrained framework that transforms visual information directly into physical measurements in the context of probability distributions. The posterior mean is a constrained IP that tracks brightness while satisfying the physical model, thereby translating the aperture problem from the motion to the underlying physics; whereas the posterior covariance derives measurement error out of image noise. As we illustrate with traction force microscopy, our approach offers several advantages: more accurate reconstructions; unprecedented flexibility in experiment design (e.g., arbitrary boundary conditions); and the exclusivity of measurement error, central to empirical science, yet still unavailable under the OFIP strategy.
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Chen CT, Gu GX. Physics-Informed Deep-Learning For Elasticity: Forward, Inverse, and Mixed Problems. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023:e2300439. [PMID: 37092567 DOI: 10.1002/advs.202300439] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Indexed: 05/03/2023]
Abstract
Elastography is a medical imaging technique used to measure the elasticity of tissues by comparing ultrasound signals before and after a light compression. The lateral resolution of ultrasound is much inferior to the axial resolution. Current elastography methods generally require both axial and lateral displacement components, making them less effective for clinical applications. Additionally, these methods often rely on the assumption of material incompressibility, which can lead to inaccurate elasticity reconstruction as no materials are truly incompressible. To address these challenges, a new physics-informed deep-learning method for elastography is proposed. This new method integrates a displacement network and an elasticity network to reconstruct the Young's modulus field of a heterogeneous object based on only a measured axial displacement field. It also allows for the removal of the assumption of material incompressibility, enabling the reconstruction of both Young's modulus and Poisson's ratio fields simultaneously. The authors demonstrate that using multiple measurements can mitigate the potential error introduced by the "eggshell" effect, in which the presence of stiff material prevents the generation of strain in soft material. These improvements make this new method a valuable tool for a wide range of applications in medical imaging, materials characterization, and beyond.
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Affiliation(s)
- Chun-Teh Chen
- Department of Materials Science and Engineering, University of California, Berkeley, CA, 94720, USA
| | - Grace X Gu
- Department of Mechanical Engineering, University of California, Berkeley, CA, 94720, USA
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Seppecher L, Bretin E, Millien P, Petrusca L, Brusseau E. Reconstructing the Spatial Distribution of the Relative Shear Modulus in Quasi-static Ultrasound Elastography: Plane Stress Analysis. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:710-722. [PMID: 36639283 DOI: 10.1016/j.ultrasmedbio.2022.09.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 09/28/2022] [Accepted: 09/30/2022] [Indexed: 06/17/2023]
Abstract
Quasi-static ultrasound elastography (QSUE) is an imaging technique that mainly provides axial strain maps of tissues when the latter are subjected to compression. In this article, a method for reconstructing the relative shear modulus distribution within a linear elastic and isotropic medium, in QSUE, is introduced. More specifically, the plane stress inverse problem is considered. The proposed method is based on the variational formulation of the equilibrium equations and on the choice of adapted discretization spaces, and only requires displacement fields in the analyzed media to be determined. Results from plane stress and 3-D numerical simulations, as well as from phantom experiments, showed that the method is able to reconstruct the different regions within a medium, with shear modulus contrasts that unambiguously reveal whether inclusions are stiffer or softer than the surrounding material. More specifically, for the plane stress simulations, inclusion-to-background modulus ratios were found to be very accurately estimated, with an error lower than 3%. For the 3-D simulations, for which the plane stress conditions are no longer satisfied, these ratios were, as expected, less accurate, with an error that remained lower than 10% for two of the three cases analyzed but was around 34% for the last case. Concerning the phantom experiments, a comparison with a shear wave elastography technique from a clinical ultrasound scanner was also made. Overall, the inclusion-to-background shear modulus ratios obtained with our approach were found to be closer to those given by the phantom manufacturer than the ratios provided by the clinical system.
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Affiliation(s)
- Laurent Seppecher
- Institut Camille Jordan, Ecole Centrale de Lyon & UCBL, Lyon, France
| | - Elie Bretin
- Institut Camille Jordan, INSA de Lyon & UCBL, Lyon, France
| | - Pierre Millien
- Institut Langevin, CNRS UMR 7587, ESPCI Paris, PSL Research University, Paris, France
| | - Lorena Petrusca
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM Saint-Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, France
| | - Elisabeth Brusseau
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM Saint-Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, France.
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5
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Kabir IE, Caban-Rivera DA, Ormachea J, Parker KJ, Johnson CL, Doyley MM. Reverberant magnetic resonance elastographic imaging using a single mechanical driver. Phys Med Biol 2023; 68:055015. [PMID: 36780698 PMCID: PMC9969521 DOI: 10.1088/1361-6560/acbbb7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 02/13/2023] [Indexed: 02/15/2023]
Abstract
Reverberant elastography provides fast and robust estimates of shear modulus; however, its reliance on multiple mechanical drivers hampers clinical utility. In this work, we hypothesize that for constrained organs such as the brain, reverberant elastography can produce accurate magnetic resonance elastograms with a single mechanical driver. To corroborate this hypothesis, we performed studies on healthy volunteers (n= 3); and a constrained calibrated brain phantom containing spherical inclusions with diameters ranging from 4-18 mm. In both studies (i.e. phantom and clinical), imaging was performed at frequencies of 50 and 70 Hz. We used the accuracy and contrast-to-noise ratio performance metrics to evaluate reverberant elastograms relative to those computed using the established subzone inversion method. Errors incurred in reverberant elastograms varied from 1.3% to 16.6% when imaging at 50 Hz and 3.1% and 16.8% when imaging at 70 Hz. In contrast, errors incurred in subzone elastograms ranged from 1.9% to 13% at 50 Hz and 3.6% to 14.9% at 70 Hz. The contrast-to-noise ratio of reverberant elastograms ranged from 63.1 to 73 dB compared to 65 to 66.2 dB for subzone elastograms. The average global brain shear modulus estimated from reverberant and subzone elastograms was 2.36 ± 0.07 kPa and 2.38 ± 0.11 kPa, respectively, when imaging at 50 Hz and 2.70 ± 0.20 kPa and 2.89 ± 0.60 kPa respectively, when imaging at 70 Hz. The results of this investigation demonstrate that reverberant elastography can produce accurate, high-quality elastograms of the brain with a single mechanical driver.
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Affiliation(s)
- Irteza Enan Kabir
- University of Rochester, Hajim School of Engineering and Applied Sciences 1467, Rochester, NY, United States of America
| | - Diego A Caban-Rivera
- University of Delaware, Department of Biomedical Engineering 19716, Newark, DE, United States of America
| | - Juvenal Ormachea
- Verasonics, Inc., 11335 NE 122nd Way, Suite 100 98034 Kirkland, WA, United States of America
| | - Kevin J Parker
- University of Rochester, Hajim School of Engineering and Applied Sciences 1467, Rochester, NY, United States of America
| | - Curtis L Johnson
- University of Delaware, Department of Biomedical Engineering 19716, Newark, DE, United States of America
| | - Marvin M Doyley
- University of Rochester, Hajim School of Engineering and Applied Sciences 1467, Rochester, NY, United States of America
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Ghafarinatanzi M, Perie D. Estimation of anisotropic properties of CMR patient-specific left ventricle using the virtual field method. Biomech Model Mechanobiol 2023; 22:695-710. [PMID: 36692846 DOI: 10.1007/s10237-022-01675-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 12/08/2022] [Indexed: 01/25/2023]
Abstract
Left ventricle (LV) myocardial dysfunction has been recently investigated using the estimation of isotropic myocardial stiffness from magnetic resonance imaging (MRI). However, Myocardium is known to have a 3D complex geometry with anisotropic stiffness. The assessment of the anisotropy properties characterizes structural changes in myocardium as a consequence of heart failure (HF). From image data, the virtual field method (VFM) can determine material stiffness in a non-invasive manner. In the present work, the objective is to compare two inverse identification methods, given the isotropic and anisotropic models in the characterization of properties of myocardium in acute lymphoblastic leukemia (ALL) survivors using VFM and MRI. Two types of VFM approach are presented. Using the first, the virtual displacements (VFs) allow whole-field LV to be imposed into VFM formulation and caused to directly estimate two independent parameters from isotropic constitutive relation. With the second, anisotropic parameters are estimated using piece-wise (Finite element-based) VFM. The resulting values showed significant differences between the subjects in comparative study of leukemia survivors, and variance in estimated parameters by two different VFM approach. This approach would be an efficient tool to characterize early cardiac dysfunction. This work elucidates the benefits and shortcomings of using VFM to determine anisotropic parameters of LV myocardium in linear elastic and of using the FEM application to generate meshes of patient-specific LVs from MRI images.
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Affiliation(s)
- Mehdi Ghafarinatanzi
- Department of Mechanical Engineering, Polytechnique Montreal, Station Centre-Ville, P.O. Box 6079, Montréal, QC, H3C 3A7, Canada. .,Sainte-Justine University Health Center, Research Center, Montreal, Canada.
| | - Delphine Perie
- Department of Mechanical Engineering, Polytechnique Montreal, Station Centre-Ville, P.O. Box 6079, Montréal, QC, H3C 3A7, Canada.,Sainte-Justine University Health Center, Research Center, Montreal, Canada
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Lamprecht R, Scheible F, Veltrup R, Schaan C, Semmler M, Henningson JO, Sutor A. Quasi-static ultrasound elastography of ex-vivo porcine vocal folds during passive elongation and adduction. J Voice 2022:S0892-1997(22)00386-1. [PMID: 36529564 DOI: 10.1016/j.jvoice.2022.11.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 11/24/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022]
Abstract
OBJECTIVES The elastic properties of the vocal folds have great influence on the primary sound and thus on the entire subsequent phonation process. Muscle contractions in the larynx can alter the elastic properties of the vocal fold tissue. Quasi-static ultrasound elastography is a non-destructive examination method that can be applied to ex-vivo vocal folds. In this work, porcine vocal folds were passively elongated and adducted and the changes of the elastic properties due to that manipulations were measured. METHODS Manipulations were performed by applying force to sewn-in sutures. Elongation was achieved by a suture attached to the thyroid cartilage, which was pulled forward by defined weights. Adduction was effected by two sutures exerting torque on the arytenoid cartilage. A series of ten specimens was examined and evaluated using a quasi-static elastography algorithm. In addition, the surface stretch was measured optically using tattooed reference points. RESULTS This study showed that the expected stiffening of the tissue during the manipulations can be measured using quasi-static ultrasound elastography. The measured effect of elongation and adduction, both of which result in stretching of the tissue, is stiffening. However, the relative change of specific manipulations is not the same for the same load on different larynges, but is rather related to stretch caused and other uninvestigated factors. CONCLUSION The passive elongation and adduction of vocal folds stiffen the tissue of the vocal folds and can be measured using ultrasound elastography.
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Affiliation(s)
- Raphael Lamprecht
- Institute of Measurement and Sensor Technology, UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tirol, Austria.
| | - Florian Scheible
- Institute of Measurement and Sensor Technology, UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tirol, Austria.
| | - Reinhard Veltrup
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head- and Neck surgery, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany.
| | - Casey Schaan
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head- and Neck surgery, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany.
| | - Marion Semmler
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head- and Neck surgery, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany.
| | - Jann-Ole Henningson
- Chair of Visual Computing, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany.
| | - Alexander Sutor
- Institute of Measurement and Sensor Technology, UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tirol, Austria.
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Du Q, Bel-Brunon A, Lambert SA, Hamila N. Numerical simulation of wave propagation through interfaces using the extended finite element method for magnetic resonance elastography. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2022; 151:3481. [PMID: 35649898 PMCID: PMC9381142 DOI: 10.1121/10.0011392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Magnetic resonance elastography (MRE) is an elasticity imaging technique for quantitatively assessing the stiffness of human tissues. In MRE, finite element method (FEM) is widely used for modeling wave propagation and stiffness reconstruction. However, in front of inclusions with complex interfaces, FEM can become burdensome in terms of the model partition and computationally expensive. In this work, we implement a formulation of FEM, known as the eXtended finite element method (XFEM), which is a method used for modeling discontinuity like crack and heterogeneity. Using a level-set method, it makes the interface independent of the mesh, thus relieving the meshing efforts. We investigate this method in two studies: wave propagation across an oblique linear interface and stiffness reconstruction of a random-shape inclusion. In the first study, numerical results by XFEM and FEM models revealing the wave conversion rules at linear interface are presented and successfully compared to the theoretical predictions. The second study, investigated in a pseudo-practical application, demonstrates further the applicability of XFEM in MRE and the convenience, accuracy, and speed of XFEM with respect to FEM. XFEM can be regarded as a promising alternative to FEM for inclusion modeling in MRE.
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Affiliation(s)
- Quanshangze Du
- Univ Lyon, INSA Lyon, CNRS, LaMCoS, UMR5259, 69621 Villeurbanne, France
| | - Aline Bel-Brunon
- Univ Lyon, INSA Lyon, CNRS, LaMCoS, UMR5259, 69621 Villeurbanne, France
- Electronic mail:
| | - Simon Auguste Lambert
- Université de Lyon, INSA Lyon, Université Claude Bernard Lyon 1, Ecole Centrale de Lyon, CNRS, Ampère UMR5005, Villeurbanne, France
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9
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Majumder S, Islam MT, Righetti R. Estimation of Mechanical and Transport Parameters in Cancers Using Short Time Poroelastography. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2022; 10:1900411. [PMID: 36147877 PMCID: PMC9484738 DOI: 10.1109/jtehm.2022.3198316] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 04/03/2022] [Accepted: 07/21/2022] [Indexed: 05/20/2023]
Abstract
Mechanical and transport properties of cancers such as Young's modulus (YM), Poisson's ratio (PR), and vascular permeability (VP) have great clinical importance in cancer diagnosis, prognosis, and treatment. However, non-invasive estimation of these parameters in vivo is challenged by many practical factors. Elasticity imaging methods, such as "poroelastography", require prolonged data acquisition, which can limit their clinical applicability. In this paper, we investigate a new method to perform poroelastography experiments, which results in shorter temporal acquisition windows. This method is referred to as "short-time poroelastography" (STPE). Finite element (FE) and ultrasound simulations demonstrate that, using STPE, it is possible to accurately estimate YM, PR (within 10% error) using windows of observation (WoOs) of length as short as 1 underlying strain Time Constant (TC). The error was found to be almost negligible (< 3%) when using WoOs longer than 2 strain TCs. In the case of VP estimation, WoOs of at least 2 strain TCs are required to obtain an error < 8% (in simulations). The stricter requirement for the estimation of VP with respect to YM and PR is due its reliance on the transient strain behavior while YM and PR depend on the steady state strain values only. In vivo experimental data are used as a proof-of-principle of the potential applicability of the proposed methodology in vivo. The use of STPE may provide a means to efficiently perform poroelastography experiments without compromising the accuracy of the estimated tissue properties.
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Affiliation(s)
- Sharmin Majumder
- Department of Electrical and Computer EngineeringTexas A&M University College Station TX 77843 USA
| | - Md Tauhidul Islam
- Department of Radiation OncologyStanford University Stanford CA 94305 USA
| | - Raffaella Righetti
- Department of Electrical and Computer EngineeringTexas A&M University College Station TX 77843 USA
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10
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Napoli ME, Goswami S, McAleavey SA, Doyley MM, Howard TM. Enabling quantitative robot-assisted compressional elastography via the extended Kalman filter. Phys Med Biol 2021; 66. [PMID: 34715685 DOI: 10.1088/1361-6560/ac34b0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 10/29/2021] [Indexed: 11/12/2022]
Abstract
Compressional or quasi-static elastography has demonstrated the capability to detect occult cancers in a variety of tissue types, however it has a serious limitation in that the resulting elastograms are generally qualitative whereas other forms of elastography, such as shear-wave, can produce absolute measures of elasticity for histopathological classification. We address this limitation by introducing a stochastic method using an extended Kalman filter and robot-assistance to obtain quantitative elastograms which are resilient to measurement noise and system uncertainty. In this paper, the probabilistic framework is described, which utilizes many ultrasound acquisitions obtained from multiple palpations, to fuse data and uncertainty from a robotic manipulator's joint encoders and force/torque sensor directly into the inverse reconstruction of the elastogram. Quantitative results are demonstrated over homogeneous and inclusion gelatin phantoms using a seven degree of freedom manipulator for a range of initial elasticity assumptions. Results imply resilience to poorly assumed initial conditions as all trials were within 5 kPa of the elasticity measured by a mechanical testing system. Moreover, the presence or absence of an inclusion is clear in all reconstructed elastograms even when artifacts are present in displacement fields, indicating further robustness to measurement noise. The proposed stochastic method allows fusion of data from a robot's sensors directly into compressional elastography image reconstruction which may stabilize optimization and improve accuracy. This approach provides a mathematical framework to readily incorporate measurements from additional sensors in future applications which may extend the capabilities of compressional elastography beyond that of producing quantitative elasticity measurements.
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Affiliation(s)
- Michael E Napoli
- University of Rochester, Rochester, NY, United States of America
| | - Soumya Goswami
- University of Rochester, Rochester, NY, United States of America
| | | | - Marvin M Doyley
- University of Rochester, Rochester, NY, United States of America
| | - Thomas M Howard
- University of Rochester, Rochester, NY, United States of America
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Brusseau E, Petrusca L, Bretin E, Millien P, Seppecher L. Reconstructing the shear modulus contrast of linear elastic and isotropic media 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; 2021:3177-3180. [PMID: 34891916 DOI: 10.1109/embc46164.2021.9630973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
This study focuses on the reconstruction of the shear modulus contrast in linear elastic and isotropic media, in quasi-static ultrasound elastography. The method proposed is based on the variational formulation of the equilibrium equations and on the choice of adapted discretization spaces to estimate the parameters of interest. Experimental results obtained with CIRS phantoms are presented, for which regions with different mechanical properties can be clearly identified in the stiffness contrast maps. Elastic modulus images collected with a shear-wave elastography technique from a clinical ultrasound scanner (Aixplorer) are also provided for comparison. Results show very similar values for the modulus ratios determined by the two elastography approaches.
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12
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Miller CE, Jordan JH, Thomas A, Weis JA. Developing a biomechanical model-based elasticity imaging method for assessing hormone receptor positive breast cancer treatment-related myocardial stiffness changes. J Med Imaging (Bellingham) 2021; 8:056002. [PMID: 34604442 DOI: 10.1117/1.jmi.8.5.056002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 09/16/2021] [Indexed: 11/14/2022] Open
Abstract
Purpose: Assessing cardiotoxicity as a result of breast cancer therapeutics is increasingly important as breast cancer diagnoses are trending younger and overall survival is increasing. With evidence showing that prevention of cardiotoxicity plays a significant role in increasing overall survival, there is an unmet need for accurate non-invasive methods to assess cardiac injury due to cancer therapies. Current clinical methods are too coarse and emerging research methods have not yet achieved clinical implementation. Approach: As a proof of concept, we examine myocardial elasticity imaging in the setting of premenopausal women diagnosed with hormone receptor positive (HR-positive) breast cancer undergoing severe estrogen depletion, as cardiovascular injury from early estrogen depletion is well-established. We evaluate the ability of our model-based cardiac elasticity imaging analysis method to indicate subclinical cancer therapy-related cardiac decline by examining differences in the change in cardiac elasticity over time in two cohorts of premenopausal women either undergoing severe estrogen depletion for HR-positive breast cancer or triple negative breast cancer patients as comparators. Results: Our method was capable of producing functional mechanical elasticity maps of the left ventricle (LV). Using these elasticity maps, we show significant differences in cardiac mechanical elasticity in the HR-positive breast cancer cohort compared to the comparator cohort. Conclusions: We present our methodology to assess the mechanical stiffness of the LV by interrogating cardiac magnetic resonance images within a computational biomechanical model. Our preliminary study suggests the potential of this method for examining cardiac tissue mechanical stiffness properties as an early indicator of cardiac decline.
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Affiliation(s)
- Caroline E Miller
- Wake Forest School of Medicine, Department of Biomedical Engineering, Winston-Salem, North Carolina, United States.,Virginia Tech-Wake Forest University, School of Biomedical Engineering and Sciences, Blacksburg, Virginia, United States
| | - Jennifer H Jordan
- Virginia Commonwealth University, Biomedical Engineering and Pauley Heart Center, Richmond, Virginia, United States
| | - Alexandra Thomas
- Wake Forest Baptist Medical Center, Comprehensive Cancer Center, Winston-Salem, North Carolina, United States.,Wake Forest Baptist Medical Center, Hematology and Oncology Cancer Center, Winston-Salem, North Carolina, United States
| | - Jared A Weis
- Wake Forest School of Medicine, Department of Biomedical Engineering, Winston-Salem, North Carolina, United States.,Virginia Tech-Wake Forest University, School of Biomedical Engineering and Sciences, Blacksburg, Virginia, United States.,Wake Forest Baptist Medical Center, Comprehensive Cancer Center, Winston-Salem, North Carolina, United States
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13
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Abstract
Elastography is an imaging technique to reconstruct elasticity distributions of heterogeneous objects. Since cancerous tissues are stiffer than healthy ones, for decades, elastography has been applied to medical imaging for noninvasive cancer diagnosis. Although the conventional strain-based elastography has been deployed on ultrasound diagnostic-imaging devices, the results are prone to inaccuracies. Model-based elastography, which reconstructs elasticity distributions by solving an inverse problem in elasticity, may provide more accurate results but is often unreliable in practice due to the ill-posed nature of the inverse problem. We introduce ElastNet, a de novo elastography method combining the theory of elasticity with a deep-learning approach. With prior knowledge from the laws of physics, ElastNet can escape the performance ceiling imposed by labeled data. ElastNet uses backpropagation to learn the hidden elasticity of objects, resulting in rapid and accurate predictions. We show that ElastNet is robust when dealing with noisy or missing measurements. Moreover, it can learn probable elasticity distributions for areas even without measurements and generate elasticity images of arbitrary resolution. When both strain and elasticity distributions are given, the hidden physics in elasticity-the conditions for equilibrium-can be learned by ElastNet.
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14
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A Quasi-Static Quantitative Ultrasound Elastography Algorithm Using Optical Flow. SENSORS 2021; 21:s21093010. [PMID: 33923001 PMCID: PMC8123352 DOI: 10.3390/s21093010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 04/19/2021] [Accepted: 04/22/2021] [Indexed: 11/17/2022]
Abstract
Ultrasound elastography is a constantly developing imaging technique which is capable of displaying the elastic properties of tissue. The measured characteristics could help to refine physiological tissue models, but also indicate pathological changes. Therefore, elastography data give valuable insights into tissue properties. This paper presents an algorithm that measures the spatially resolved Young’s modulus of inhomogeneous gelatin phantoms using a CINE sequence of a quasi-static compression and a load cell measuring the compressing force. An optical flow algorithm evaluates the resulting images, the stresses and strains are computed, and, conclusively, the Young’s modulus and the Poisson’s ratio are calculated. The whole algorithm and its results are evaluated by a performance descriptor, which determines the subsequent calculation and gives the user a trustability index of the modulus estimation. The algorithm shows a good match between the mechanically measured modulus and the elastography result—more precisely, the relative error of the Young’s modulus estimation with a maximum error 35%. Therefore, this study presents a new algorithm that is capable of measuring the elastic properties of gelatin specimens in a quantitative way using only the image data. Further, the computation is monitored and evaluated by a performance descriptor, which measures the trustability of the results.
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Santhanam AP, Stiehl B, Lauria M, Hasse K, Barjaktarevic I, Goldin J, Low DA. An adversarial machine learning framework and biomechanical model-guided approach for computing 3D lung tissue elasticity from end-expiration 3DCT. Med Phys 2020; 48:667-675. [PMID: 32449519 DOI: 10.1002/mp.14252] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 04/19/2020] [Accepted: 04/24/2020] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Lung elastography aims at measuring the lung parenchymal tissue elasticity for applications ranging from diagnostic purposes to biomechanically guided deformations. Characterizing the lung tissue elasticity requires four-dimensional (4D) lung motion as an input, which is currently estimated by deformably registering 4D computed tomography (4DCT) datasets. Since 4DCT imaging is widely used only in a radiotherapy treatment setup, there is a need to predict the elasticity distribution in the absence of 4D imaging for applications within and outside of radiotherapy domain. METHODS In this paper, we present a machine learning-based method that predicts the three-dimensional (3D) lung tissue elasticity distribution for a given end-expiration 3DCT. The method to predict the lung tissue elasticity from an end-expiration 3DCT employed a deep neural network that predicts the tissue elasticity for the given CT dataset. For training and validation purposes, we employed five-dimensional CT (5DCT) datasets and a finite element biomechanical lung model. The 5DCT model was first used to generate end-expiration lung geometry, which was taken as the source lung geometry for biomechanical modeling. The deformation vector field pointing from end expiration to end inhalation was computed from the 5DCT model and taken as input in order to solve for the lung tissue elasticity. An inverse elasticity estimation process was employed, where we iteratively solved for the lung elasticity distribution until the model reproduced the ground-truth deformation vector field. The machine learning process uses a specific type of learning process, namely a constrained generalized adversarial neural network (cGAN) that learned the lung tissue elasticity in a supervised manner. The biomechanically estimated tissue elasticity together with the end-exhalation CT was the input for the supervised learning. The trained cGAN generated the elasticity from a given breath-hold CT image. The elasticity estimated was validated in two approaches. In the first approach, a L2-norm-based direct comparison was employed between the estimated elasticity and the ground-truth elasticity. In the second approach, we generated a synthetic four-dimensional CT (4DCT0 using a lung biomechanical model and the estimated elasticity and compared the deformations with the ground-truth 4D deformations using three image similarity metrics: mutual Information (MI), structured similarity index (SSIM), and normalized cross correlation (NCC). RESULTS The results show that a cGAN-based machine learning approach was effective in computing the lung tissue elasticity given the end-expiration CT datasets. For the training data set, we obtained a learning accuracy of 0.44 ± 0.2 KPa. For the validation dataset, consisting of 13 4D datasets, we were able to obtain an accuracy of 0.87 ± 0.4 KPa. These results show that the cGAN-generated elasticity correlates well with that of the underlying ground-truth elasticity. We then integrated the estimated elasticity with the biomechanical model and applied the same boundary conditions in order to generate the end inhalation CT. The cGAN-generated images were very similar to that of the original end inhalation CT. The average value of the MI is 1.77 indicating the high local symmetricity between the ground truth and the cGAN elasticity-generated end inhalation CT data. The average value of the structural similarity for the 13 patients was observed to be 0.89 indicating the high structural integrity of the cGAN elasticity-generated end inhalation CT. Finally, the average NCC value of 0.97 indicates that potential variations in the contrast and brightness of the cGAN elasticity-generated end inhalation CT and the ground-truth end inhalation CT. CONCLUSION The cGAN-generated lung tissue elasticity given an end-expiration CT image can be computed in near real time. Using the lung tissue elasticity along with a biomechanical model, 4D lung deformations can be generated from a given end-expiration CT image within clinically acceptable numerical accuracy.
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Affiliation(s)
- Anand P Santhanam
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Brad Stiehl
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Michael Lauria
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Katelyn Hasse
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Igor Barjaktarevic
- Department of Pulmonary Critical Care, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Jonathan Goldin
- Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Daniel A Low
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
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Kheirkhah N, Dempsey SCH, Rivaz H, Samani A, Sadeghi-Naini A. A Tissue Mechanics Based Method to Improve Tissue Displacement Estimation in Ultrasound Elastography .. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2051-2054. [PMID: 33018408 DOI: 10.1109/embc44109.2020.9175869] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Cancer is known to induce significant structural changes to tissue. In most cancers, including breast cancer, such changes yield tissue stiffening. As such, imaging tissue stiffness can be used effectively for cancer diagnosis. One such imaging technique, ultrasound elastography, has emerged with the aim of providing a low-cost imaging modality for effective breast cancer diagnosis. In quasi-static breast ultrasound elastography, the breast is stimulated by ultrasound probe, leading to tissue deformation. The tissue displacement data can be estimated using a pair of acquired ultrasound radiofrequency (RF) data pertaining to pre- and post-deformation states. The data can then be used within a mathematical framework to construct an image of the tissue stiffness distribution. Ultrasound RF data is known to include significant noise which lead to corruption of estimated displacement fields, especially the lateral displacements. In this study, we propose a tissue mechanics-based method aiming at improving the quality of estimated displacement data. We applied the method to RF data acquired from a tissue-mimicking phantom. The results indicated that the method is effective in improving the quality of the displacement data.
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Cai L, Nauman EA, Pedersen CBW, Neu CP. Finite deformation elastography of articular cartilage and biomaterials based on imaging and topology optimization. Sci Rep 2020; 10:7980. [PMID: 32409711 PMCID: PMC7224212 DOI: 10.1038/s41598-020-64723-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2019] [Accepted: 04/17/2020] [Indexed: 01/17/2023] Open
Abstract
Tissues and engineered biomaterials exhibit exquisite local variation in stiffness that defines their function. Conventional elastography quantifies stiffness in soft (e.g. brain, liver) tissue, but robust quantification in stiff (e.g. musculoskeletal) tissues is challenging due to dissipation of high frequency shear waves. We describe new development of finite deformation elastography that utilizes magnetic resonance imaging of low frequency, physiological-level (large magnitude) displacements, coupled to an iterative topology optimization routine to investigate stiffness heterogeneity, including spatial gradients and inclusions. We reconstruct 2D and 3D stiffness distributions in bilayer agarose hydrogels and silicon materials that exhibit heterogeneous displacement/strain responses. We map stiffness in porcine and sheep articular cartilage deep within the bony articular joint space in situ for the first time. Elevated cartilage stiffness localized to the superficial zone is further related to collagen fiber compaction and loss of water content during cyclic loading, as assessed by independent T2 measurements. We additionally describe technical challenges needed to achieve in vivo elastography measurements. Our results introduce new functional imaging biomarkers, which can be assessed nondestructively, with clinical potential to diagnose and track progression of disease in early stages, including osteoarthritis or tissue degeneration.
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Affiliation(s)
- Luyao Cai
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, 47907, US
| | - Eric A Nauman
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, 47907, US
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, 47907, US
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN, 47907, US
| | | | - Corey P Neu
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, 47907, US.
- Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO, 80309, US.
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18
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Chen S, Xu W, Kim J, Nan H, Zheng Y, Sun B, Jiao Y. Novel inverse finite-element formulation for reconstruction of relative local stiffness in heterogeneous extra-cellular matrix and traction forces on active cells. Phys Biol 2019; 16:036002. [DOI: 10.1088/1478-3975/ab0463] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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19
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Otesteanu CF, Sanabria SJ, Goksel O. Robust Reconstruction of Elasticity Using Ultrasound Imaging and Multi-Frequency Excitations. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:2502-2513. [PMID: 29994521 DOI: 10.1109/tmi.2018.2837390] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Biomedical parameters of tissue can be important indicators for clinical diagnosis. One such parameter that reflects tissue stiffness is elasticity, the imaging of which is called elastography. In this paper, we use displacements from harmonic excitations to solve the inverse problem of elasticity based on a finite-element method (FEM) formulation. This leads to iterative solution of nonlinear and nonconvex problems. In this paper, we show the importance and selection of viable initializations in numerical simulation studies and propose techniques for the fusion of multiple initializations for ideal reconstructions of unknown tissue as well as combining information from excitations at multiple frequencies. Results show that our method leads up to 76% decrease in root-mean-squared error (RMSE) and 9.9 dB increase in contrast-to-noise ratio (CNR) in simulations with noise, when compared to conventional iterative FEM without multiple initializations and frequencies. As the wave patterns in individually selected frequencies may introduce artifacts, a joint inverse-problem solution of multi-frequency excitations is introduced as a robust solution, where CNR improvements of up to 11.9 dB are observed. We also present the methods on a tissue-mimicking gelatin phantom study using mechanical excitation and ultrafast plane-wave ultrasound imaging, where the RMSE was improved by up to 51%. An experiment of ablation via heating an ex-vivo bovine liver shows that reconstruction artifacts are reduced with our proposed method.
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Miller R, Kolipaka A, Nash MP, Young AA. Relative identifiability of anisotropic properties from magnetic resonance elastography. NMR IN BIOMEDICINE 2018; 31:e3848. [PMID: 29106765 PMCID: PMC5936684 DOI: 10.1002/nbm.3848] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Revised: 07/31/2017] [Accepted: 09/20/2017] [Indexed: 05/24/2023]
Abstract
Although magnetic resonance elastography (MRE) has been used to estimate isotropic stiffness in the heart, myocardium is known to have anisotropic properties. This study investigated the determinability of global transversely isotropic material parameters using MRE and finite-element modeling (FEM). A FEM-based material parameter identification method, using a displacement-matching objective function, was evaluated in a gel phantom and simulations of a left ventricular (LV) geometry with a histology-derived fiber field. Material parameter estimation was performed in the presence of Gaussian noise. Parameter sweeps were analyzed and characteristics of the Hessian matrix at the optimal solution were used to evaluate the determinability of each constitutive parameter. Four out of five material stiffness parameters (Young's modulii E1 and E3 , shear modulus G13 and damping coefficient s), which describe a transversely isotropic linear elastic material, were well determined from the MRE displacement field using an iterative FEM inversion method. However, the remaining parameter, Poisson's ratio, was less identifiable. In conclusion, Young's modulii, shear modulii and damping can theoretically be well determined from MRE data, but Poisson's ratio is not as well determined and could be set to a reasonable value for biological tissue (close to 0.5).
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Affiliation(s)
- Renee Miller
- Department of Anatomy and Medical Imaging, University of Auckland, New Zealand
- Auckland Bioengineering Institute, University of Auckland, New Zealand
| | - Arunark Kolipaka
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, USA
| | - Martyn P Nash
- Auckland Bioengineering Institute, University of Auckland, New Zealand
- Department of Engineering Science, University of Auckland, New Zealand
| | - Alistair A Young
- Department of Anatomy and Medical Imaging, University of Auckland, New Zealand
- Auckland Bioengineering Institute, University of Auckland, New Zealand
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Abstract
The mechanical properties of soft tissues are closely associated with a variety of diseases. This motivates the development of elastography techniques in which tissue mechanical properties are quantitatively estimated through imaging. Magnetic resonance elastography (MRE) is a noninvasive phase-contrast MR technique wherein shear modulus of soft tissue can be spatially and temporally estimated. MRE has recently received significant attention due to its capability in noninvasively estimating tissue mechanical properties, which can offer considerable diagnostic potential. In this work, recent technology advances of MRE, its future clinical applications, and the related limitations will be discussed.
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Affiliation(s)
- Huiming Dong
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA
- Department of Biomedical Engineering, The Ohio State University, Columbus, OH, 43210, USA
| | - Richard D. White
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA
- Department of Internal Medicine-Division of Cardiology, The Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA
| | - Arunark Kolipaka
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA
- Department of Biomedical Engineering, The Ohio State University, Columbus, OH, 43210, USA
- Department of Internal Medicine-Division of Cardiology, The Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA
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22
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Fovargue D, Nordsletten D, Sinkus R. Stiffness reconstruction methods for MR elastography. NMR IN BIOMEDICINE 2018; 31:e3935. [PMID: 29774974 PMCID: PMC6175248 DOI: 10.1002/nbm.3935] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Revised: 03/27/2018] [Accepted: 03/27/2018] [Indexed: 05/19/2023]
Abstract
Assessment of tissue stiffness is desirable for clinicians and researchers, as it is well established that pathophysiological mechanisms often alter the structural properties of tissue. Magnetic resonance elastography (MRE) provides an avenue for measuring tissue stiffness and has a long history of clinical application, including staging liver fibrosis and stratifying breast cancer malignancy. A vital component of MRE consists of the reconstruction algorithms used to derive stiffness from wave-motion images by solving inverse problems. A large range of reconstruction methods have been presented in the literature, with differing computational expense, required user input, underlying physical assumptions, and techniques for numerical evaluation. These differences, in turn, have led to varying accuracy, robustness, and ease of use. While most reconstruction techniques have been validated against in silico or in vitro phantoms, performance with real data is often more challenging, stressing the robustness and assumptions of these algorithms. This article reviews many current MRE reconstruction methods and discusses the aforementioned differences. The material assumptions underlying the methods are developed and various approaches for noise reduction, regularization, and numerical discretization are discussed. Reconstruction methods are categorized by inversion type, underlying assumptions, and their use in human and animal studies. Future directions, such as alternative material assumptions, are also discussed.
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Affiliation(s)
- Daniel Fovargue
- Imaging Sciences & Biomedical EngineeringKing's College LondonLondonUK
| | - David Nordsletten
- Imaging Sciences & Biomedical EngineeringKing's College LondonLondonUK
| | - Ralph Sinkus
- Imaging Sciences & Biomedical EngineeringKing's College LondonLondonUK
- Inserm U1148, LVTSUniversity Paris Diderot, University Paris 13Paris75018France
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Hasse K, Han F, Neylon J, Min Y, Hu P, Yang Y, Santhanam A. Estimation and validation of patient-specific liver elasticity distributions derived from 4DMR for radiotherapy purposes. Biomed Phys Eng Express 2018. [DOI: 10.1088/2057-1976/aace4d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Liu C, Zhou Y. Detection of gaps between high-intensity focused ultrasound (HIFU)-induced lesions using transient axial shear strain elastograms. Med Phys 2018; 45:3831-3847. [PMID: 29963699 DOI: 10.1002/mp.13075] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Revised: 06/03/2018] [Accepted: 06/25/2018] [Indexed: 01/01/2023] Open
Abstract
PURPOSE High-intensity focused ultrasound (HIFU) is becoming an effective and noninvasive treatment modality for cancer and solid tumors. In order to avoid the cancer relapse and guarantee the success of ablation, there should be no gaps left among all HIFU-generated lesions. However, there are few imaging approaches available for detecting the HIFU lesion gaps in real time during ablation. METHODS Transient axial shear strain elastograms (ASSEs) were proposed and evaluated both numerically and experimentally to detect the lesion gaps immediately after the cessation of therapeutic HIFU exposure. Acoustic intensity and subsequent acoustic radiation force were first calculated by solving the nonlinear Khokhlov-Zabolotskaya-Kuznetzov (KZK) equation. Motion of being- and already-treated lesions during and after HIFU exposure was simulated using the transient dynamic analysis module of finite element method (FEM). The corresponding B-mode sonography of tissue-mimicking phantom with two HIFU lesions inside was simulated by FIELD II, and then axial strain elastograms (ASEs) under static compression and transient ASSEs were reconstructed. An ultrasound imaging probe was integrated with the HIFU transducer and used to obtain radio frequency (RF) echo signals at high frame rate using plane wave imaging (PWI). The resulting strains were mapped using the correlation-based method and block search strategy. RESULTS Acoustic radiation force from the therapeutic HIFU burst is sufficiently strong to produce significant displacement. As a result, large and highly localized axial shear strain appears in the gap zone between two HIFU-generated lesions and then disappears after sufficient HIFU ablation (no gap between them). Such capability of detecting the lesion gap is validated at the varied acoustic radiation force density, gap width, and the size of the lesion. In contrast, conventional ASEs using the static compression cannot distinguish whether a gap exists between lesions. Static ASEs and transient ASSEs reconstructed using both high-speed photography and sonography in the gel phantom show the same conclusion as that in the simulation. Ex vivo tissue experiments further confirmed that the presence of large axial shear strain in the gap zone. The ratios of axial shear strain in the porcine kidney and liver samples had statistical differences for two HIFU-generated lesions without and with a gap (P < 0.05). CONCLUSIONS Large axial shear strain induced by the acoustic radiation force from therapeutic HIFU burst only appears between two HIFU-generated lesions with a gap between them. Transient ASSEs reconstructed immediately after the cession of HIFU exposure can easily, reliably, and sensitively detect the gap between produced lesions, which would provide real-time feedback to enhance the success of HIFU ablation.
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Affiliation(s)
- Chenhui Liu
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
| | - Yufeng Zhou
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
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25
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FEM-based elasticity reconstruction using ultrasound for imaging tissue ablation. Int J Comput Assist Radiol Surg 2018; 13:885-894. [PMID: 29666974 DOI: 10.1007/s11548-018-1714-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 02/16/2018] [Indexed: 12/17/2022]
Abstract
PURPOSE Success of ablation treatment depends on the accurate placement of the target ablation focus and the complete destruction of the pathological tissue. Thus, monitoring the formation, location, and size of the ablated lesion is essential. As ablated tissue gets stiffer, an option for ablation monitoring is ultrasound elastography, for imaging the tissue mechanical properties. Reconstruction of elasticity distribution can be achieved by solving an inverse problem from observed displacements, based on a deformable tissue model, commonly discretized by the finite element method (FEM). However, available reconstruction techniques are prone to noise and may achieve suboptimal accuracy. METHODS We propose a novel inverse problem formulation and elasticity reconstruction method, in which both the elasticity parameters and the model displacements are estimated as independent parameters of an unconstrained optimization problem. Total variation regularization of spatial elasticity distribution is introduced in this formulation, providing robustness to noise. RESULTS Our approach was compared to state of the art direct and iterative harmonic elastography techniques. We employed numerical simulation studies using various noise and inclusion contrasts, given multiple excitation frequencies. Compared to alternatives, our method leads to a decrease in RMSE of up to 50% and an increase in CNR of up to 11 dB in numerical simulations. The methods were also compared on an ex vivo bovine liver sample that was locally subjected to ablation, for which improved lesion delineation was obtained with our proposed method. Our method takes [Formula: see text] for [Formula: see text] reconstruction grid. CONCLUSIONS We present a novel FEM problem formulation that improves reconstruction accuracy and inclusion delineation compared to currently available techniques.
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Uff C, Garcia L, Fromageau J, Chakraborty A, Dorward N, Bamber J. Further characterization of changes in axial strain elastograms due to the presence of slippery tumor boundaries. J Med Imaging (Bellingham) 2018; 5:021211. [PMID: 29430480 PMCID: PMC5798943 DOI: 10.1117/1.jmi.5.2.021211] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Accepted: 01/04/2018] [Indexed: 01/05/2023] Open
Abstract
Elastography measures tissue strain, which can be interpreted under certain simplifying assumptions to be representative of the underlying stiffness distribution. This is useful in cancer diagnosis where tumors tend to have a different stiffness to healthy tissue and has also shown potential to provide indication of the degree of bonding at tumor-tissue boundaries, which is clinically useful because of its dependence on tumor pathology. We consider the changes in axial strain for the case of a symmetrical model undergoing uniaxial compression, studied by characterizing changes in tumor contrast transfer efficiency (CTE), inclusion to background strain contrast and strain contrast generated by slip motion, as a function of Young's modulus contrast and applied strain. We present results from a finite element simulation and an evaluation of these results using tissue-mimicking phantoms. The simulation results show that a discontinuity in displacement data at the tumor boundary, caused by the surrounding tissue slipping past the tumor, creates a halo of "pseudostrain" across the tumor boundary. Mobile tumors also appear stiffer on elastograms than adhered tumors, to the extent that tumors that have the same Young's modulus as the background may in fact be visible as low-strain regions, or those that are softer than the background may appear to be stiffer than the background. Tumor mobility also causes characteristic strain heterogeneity within the tumor, which exhibits low strain close to the slippery boundary and increasing strain toward the center of the tumor. These results were reproduced in phantom experiments. In addition, phantom experiments demonstrated that when fluid lubrication is present at the boundary, these effects become applied strain-dependent as well as modulus-dependent, in a systematic and characteristic manner. The knowledge generated by this study is expected to aid interpretation of clinical strain elastograms by helping to avoid misinterpretation as well as provide additional diagnostic criteria stated in the paper and stimulate further research into the application of elastography to tumor mobility assessment.
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Affiliation(s)
- Christopher Uff
- Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Joint Department of Physics, Sutton, Surrey, United Kingdom
| | - Leo Garcia
- Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Joint Department of Physics, Sutton, Surrey, United Kingdom
| | - Jeremie Fromageau
- Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Joint Department of Physics, Sutton, Surrey, United Kingdom
| | - Aabir Chakraborty
- Southampton General Hospital, Department of Neurosurgery, Southampton, United Kingdom
| | - Neil Dorward
- National Hospital for Neurology and Neurosurgery, Victor Horsley Department of Neurosurgery, Queen Square, London, United Kingdom
| | - Jeffrey Bamber
- Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Joint Department of Physics, Sutton, Surrey, United Kingdom
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Wang W, Zou W, Hu D, Wang J. Adaptive mesh refinement for elastic modulus reconstruction in elastography. Proc Inst Mech Eng H 2018; 232:215-229. [DOI: 10.1177/0954411917752026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Meshes play a crucial role in determining the accuracy of the elastic modulus reconstruction in the elastography when the finite element method is employed. In this article, we propose an adaptive mesh refinement strategy which can ensure the coincidence of the meshes with the shape of the inclusions in the observed tissue. This strategy is based on the intensity distribution of the strain image where the variance of the strain distribution in each element of the meshes is used to measure the homogeneity of the element, that is, the larger the strain variance is the more inhomogeneous the element will be and hence more detailed information will be included in this element. For more accurate reconstruction of such detailed information, mesh refinement procedure is implemented in such elements. Besides, two refinement steps are employed for the reconstruction to improve the fitness of the reconstructed image and the observed tissue. Simulation results show that the two-stage adaptive mesh refinement algorithm performs well without needing any prior information about the internal geometric shape in tissue. Not only Young’s moduli of models but also shapes of the inclusions can be reconstructed perfectly and quickly with our proposed method.
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Affiliation(s)
- Wenxia Wang
- School of Electronic and Information Engineering, Soochow University, Suzhou, China
- College of Information Engineering, Henan University of Science and Technology, Luoyang, China
| | - Wei Zou
- School of Electronic and Information Engineering, Soochow University, Suzhou, China
| | - Danfeng Hu
- School of Electronic and Information Engineering, Soochow University, Suzhou, China
| | - Jiajun Wang
- School of Electronic and Information Engineering, Soochow University, Suzhou, China
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Ozkan E, Goksel O. Compliance boundary conditions for patient-specific deformation simulation using the finite element method. Biomed Phys Eng Express 2018. [DOI: 10.1088/2057-1976/aa918d] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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29
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Hasse K, O'Connell D, Min Y, Neylon J, Low DA, Santhanam A. Estimation and validation of patient‐specific high‐resolution lung elasticity derived from 4DCT. Med Phys 2017; 45:666-677. [DOI: 10.1002/mp.12697] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 11/16/2017] [Accepted: 11/16/2017] [Indexed: 11/08/2022] Open
Affiliation(s)
- Katelyn Hasse
- Department of Radiation Oncology University of California Los Angeles CA USA
| | - Dylan O'Connell
- Department of Radiation Oncology University of California Los Angeles CA USA
| | - Yugang Min
- Department of Radiation Oncology University of California Los Angeles CA USA
| | - John Neylon
- Department of Radiation Oncology University of California Los Angeles CA USA
| | - Daniel A. Low
- Department of Radiation Oncology University of California Los Angeles CA USA
| | - Anand Santhanam
- Department of Radiation Oncology University of California Los Angeles CA USA
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Perotti LE, Ponnaluri AV, Krishnamoorthi S, Balzani D, Ennis DB, Klug WS. Method for the unique identification of hyperelastic material properties using full-field measures. Application to the passive myocardium material response. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2017; 33:10.1002/cnm.2866. [PMID: 28098434 PMCID: PMC5515704 DOI: 10.1002/cnm.2866] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2016] [Accepted: 01/14/2017] [Indexed: 06/06/2023]
Abstract
Quantitative measurement of the material properties (eg, stiffness) of biological tissues is poised to become a powerful diagnostic tool. There are currently several methods in the literature to estimating material stiffness, and we extend this work by formulating a framework that leads to uniquely identified material properties. We design an approach to work with full-field displacement data-ie, we assume the displacement field due to the applied forces is known both on the boundaries and also within the interior of the body of interest-and seek stiffness parameters that lead to balanced internal and external forces in a model. For in vivo applications, the displacement data can be acquired clinically using magnetic resonance imaging while the forces may be computed from pressure measurements, eg, through catheterization. We outline a set of conditions under which the least-square force error objective function is convex, yielding uniquely identified material properties. An important component of our framework is a new numerical strategy to formulate polyconvex material energy laws that are linear in the material properties and provide one optimal description of the available experimental data. An outcome of our approach is the analysis of the reliability of the identified material properties, even for material laws that do not admit unique property identification. Lastly, we evaluate our approach using passive myocardium experimental data at the material point and show its application to identifying myocardial stiffness with an in silico experiment modeling the passive filling of the left ventricle.
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Affiliation(s)
- Luigi E. Perotti
- Department of Radiological Sciences and Department of
Bioengineering, University of California, Los Angeles
| | - Aditya V. Ponnaluri
- Department of Mechanical and Aerospace Engineering, University of
California, Los Angeles
| | | | - Daniel Balzani
- Institute of Mechanics and Shell Structures, TU Dresden, Germany,
and Dresden Center of Computational Material Science
| | - Daniel B. Ennis
- Department of Radiological Sciences and Department of
Bioengineering, University of California, Los Angeles
| | - William S. Klug
- Department of Mechanical and Aerospace Engineering, University of
California, Los Angeles
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31
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Ghosh S, Zou Z, Babaniyi O, Aquino W, Diaz MI, Bayat M, Fatemi M. Modified error in constitutive equations (MECE) approach for ultrasound elastography. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2017; 142:2084. [PMID: 29092577 PMCID: PMC5643161 DOI: 10.1121/1.5006911] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
A partial differential equation-constrained optimization approach is presented for reconstructing mechanical properties (e.g., elastic moduli). The proposed method is based on the minimization of an error in constitutive equations functional augmented with a least squares data misfit term referred to as MECE for "modified error in constitutive equations." The main theme of this paper is to demonstrate several key strengths of the proposed method on experimental data. In addition, some illustrative examples are provided where the proposed method is compared with a common shear wave elastography (SWE) approach. To this end, both synthetic data, generated with transient finite element simulations, as well as ultrasonically tracked displacement data from an acoustic radiation force (ARF) experiment are used in a standard elasticity phantom. The results indicate that the MECE approach can produce accurate shear modulus reconstructions with significantly less bias than SWE.
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Affiliation(s)
- Susanta Ghosh
- Department of Civil and Environmental Engineering, Duke University, Durham, North Carolina 27708, USA
| | - Zilong Zou
- Department of Civil and Environmental Engineering, Duke University, Durham, North Carolina 27708, USA
| | - Olalekan Babaniyi
- Department of Civil and Environmental Engineering, Duke University, Durham, North Carolina 27708, USA
| | - Wilkins Aquino
- Department of Civil and Environmental Engineering, Duke University, Durham, North Carolina 27708, USA
| | - Manuel I Diaz
- Department of Civil and Environmental Engineering, Duke University, Durham, North Carolina 27708, USA
| | - Mahdi Bayat
- Department of Physiology and Biomedical Engineering, Mayo College of Medicine, Rochester, Minnesota 55902, USA
| | - Mostafa Fatemi
- Department of Physiology and Biomedical Engineering, Mayo College of Medicine, Rochester, Minnesota 55902, USA
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32
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Model-based vascular elastography improves the detection of flow-induced carotid artery remodeling in mice. Sci Rep 2017; 7:12081. [PMID: 28935983 PMCID: PMC5608712 DOI: 10.1038/s41598-017-12321-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Accepted: 09/07/2017] [Indexed: 01/25/2023] Open
Abstract
Increased arterial thickness measured with ultrasound correlates with future cardiovascular events, but conventional ultrasound imaging techniques cannot distinguish between intima, media, or atherosclerotic plaque in the carotid artery. In this work, we evaluated how well vascular elastography can detect intimal changes in a mouse model of carotid remodeling. We ligated the left external and internal branches of the carotid artery of male FVB mice and performed sham operations for 2 weeks. High-resolution ultrasound imaging accurately detected lower blood velocities and low blood volume flow in the carotid arteries after ligation in FVB mice. However, ultrasound could not detect differences in the carotid wall even at 2 weeks post-surgery. The Young’s modulus was measured based on displacements of the carotid artery wall, and Young’s modulus was 2-fold greater in shams at 1 week post ligation, and 3-fold greater 2 weeks after ligation. Finally, the higher Young’s modulus was most associated with higher intimal thickness but not medial or adventitial thickness as measured by histology. In conclusion, we developed a robust ultrasound-based elastography method for early detection of intimal changes in small animals.
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33
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Honarvar M, Sahebjavaher RS, Rohling R, Salcudean SE. A Comparison of Finite Element-Based Inversion Algorithms, Local Frequency Estimation, and Direct Inversion Approach Used in MRE. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:1686-1698. [PMID: 28333623 DOI: 10.1109/tmi.2017.2686388] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
In quantitative elastography, maps of the mechanical properties of soft tissue, or elastograms, are calculated from the measured displacement data by solving an inverse problem. The model assumptions have a significant effect on elastograms. Motivated by the high sensitivity of imaging results to the model assumptions for in vivo magnetic resonance elastography of the prostate, we compared elastograms obtained with four different methods. Two finite-element method (FEM)-based methods developed by our group were compared with two other commonly used methods, local frequency estimator (LFE) and curl-based direct inversion (c-DI). All the methods assume a linear isotropic elastic model, but the methods vary in their assumptions, such as local homogeneity or incompressibility, and in the specific approach used. We report results using simulations, phantom, and ex vivo and in vivo data. The simulation and phantom studies show, for regions with an inclusion, that the contrast to noise ratio (CNR) for the FEM methods is about three to five times higher than the CNR for the LFE and c-DI and the rms error is about half. The LFE method produces very smooth results (i.e., low CNR) and is fast. c-DI is faster than the FEM methods but it is only accurate in areas where elasticity variations are small. The artifacts resulting from the homogeneity assumption in c-DI is detrimental in regions with large variations. The ex vivo and in vivo results also show similar trends as the simulation and phantom studies. The c-FEM method is more sensitive to noise compared with the mixed-FEM due to higher orders derivatives. This is especially evident at lower frequencies, where the wave curvature is smaller and it is more prone to such error, causing a discrepancy in the absolute values between the mixed-FEM and c-FEM in our in vivo results. In general, the proposed FEMs use fewer simplifying assumptions and outperform the other methods but they are computationally more expensive.
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34
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Li GY, Cao Y. Mechanics of ultrasound elastography. Proc Math Phys Eng Sci 2017; 473:20160841. [PMID: 28413350 PMCID: PMC5378248 DOI: 10.1098/rspa.2016.0841] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Accepted: 01/23/2017] [Indexed: 12/19/2022] Open
Abstract
Ultrasound elastography enables in vivo measurement of the mechanical properties of living soft tissues in a non-destructive and non-invasive manner and has attracted considerable interest for clinical use in recent years. Continuum mechanics plays an essential role in understanding and improving ultrasound-based elastography methods and is the main focus of this review. In particular, the mechanics theories involved in both static and dynamic elastography methods are surveyed. They may help understand the challenges in and opportunities for the practical applications of various ultrasound elastography methods to characterize the linear elastic, viscoelastic, anisotropic elastic and hyperelastic properties of both bulk and thin-walled soft materials, especially the in vivo characterization of biological soft tissues.
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Affiliation(s)
- Guo-Yang Li
- Department of Engineering Mechanics, Institute of Biomechanics and Medical Engineering, AML, Tsinghua University, Beijing 100084, People's Republic of China
| | - Yanping Cao
- Department of Engineering Mechanics, Institute of Biomechanics and Medical Engineering, AML, Tsinghua University, Beijing 100084, People's Republic of China
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35
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Hasse K, Neylon J, Santhanam AP. Feasibility and quantitative analysis of a biomechanical model-guided lung elastography for radiotherapy. Biomed Phys Eng Express 2017. [DOI: 10.1088/2057-1976/aa5d1c] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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36
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Hasse K, Neylon J, Sheng K, Santhanam AP. Systematic feasibility analysis of a quantitative elasticity estimation for breast anatomy using supine/prone patient postures. Med Phys 2016; 43:1299-1311. [PMID: 26936715 DOI: 10.1118/1.4941745] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
PURPOSE Breast elastography is a critical tool for improving the targeted radiotherapy treatment of breast tumors. Current breast radiotherapy imaging protocols only involve prone and supine CT scans. There is a lack of knowledge on the quantitative accuracy with which breast elasticity can be systematically measured using only prone and supine CT datasets. The purpose of this paper is to describe a quantitative elasticity estimation technique for breast anatomy using only these supine/prone patient postures. Using biomechanical, high-resolution breast geometry obtained from CT scans, a systematic assessment was performed in order to determine the feasibility of this methodology for clinically relevant elasticity distributions. METHODS A model-guided inverse analysis approach is presented in this paper. A graphics processing unit (GPU)-based linear elastic biomechanical model was employed as a forward model for the inverse analysis with the breast geometry in a prone position. The elasticity estimation was performed using a gradient-based iterative optimization scheme and a fast-simulated annealing (FSA) algorithm. Numerical studies were conducted to systematically analyze the feasibility of elasticity estimation. For simulating gravity-induced breast deformation, the breast geometry was anchored at its base, resembling the chest-wall/breast tissue interface. Ground-truth elasticity distributions were assigned to the model, representing tumor presence within breast tissue. Model geometry resolution was varied to estimate its influence on convergence of the system. A priori information was approximated and utilized to record the effect on time and accuracy of convergence. The role of the FSA process was also recorded. A novel error metric that combined elasticity and displacement error was used to quantify the systematic feasibility study. For the authors' purposes, convergence was set to be obtained when each voxel of tissue was within 1 mm of ground-truth deformation. RESULTS The authors' analyses showed that a ∼97% model convergence was systematically observed with no-a priori information. Varying the model geometry resolution showed no significant accuracy improvements. The GPU-based forward model enabled the inverse analysis to be completed within 10-70 min. Using a priori information about the underlying anatomy, the computation time decreased by as much as 50%, while accuracy improved from 96.81% to 98.26%. The use of FSA was observed to allow the iterative estimation methodology to converge more precisely. CONCLUSIONS By utilizing a forward iterative approach to solve the inverse elasticity problem, this work indicates the feasibility and potential of the fast reconstruction of breast tissue elasticity using supine/prone patient postures.
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Affiliation(s)
- Katelyn Hasse
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California 90095
| | - John Neylon
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California 90095
| | - Ke Sheng
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California 90095
| | - Anand P Santhanam
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California 90095
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37
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Huntzicker S, Shekhar H, Doyley MM. Contrast-Enhanced Quantitative Intravascular Elastography: The Impact of Microvasculature on Model-Based Elastography. ULTRASOUND IN MEDICINE & BIOLOGY 2016; 42:1167-81. [PMID: 26924697 PMCID: PMC4811726 DOI: 10.1016/j.ultrasmedbio.2015.12.024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Revised: 12/18/2015] [Accepted: 12/22/2015] [Indexed: 05/03/2023]
Abstract
Model-based intravascular ultrasound elastography visualizes the stress distribution within vascular tissue-information that clinicians could use to predict the propensity of atherosclerotic plaque rupture. However, there are concerns that clusters of microvessels may reduce the accuracy of the estimated stress distribution. Consequently, we have developed a contrast-enhanced intravascular ultrasound system to investigate how plaque microvasculature affects the performance of model-based elastography. In simulations, diameters of 200, 400 and 800 μm were used, where the latter diameter represented a cluster of microvessels. In phantoms, we used a microvessel with a diameter of 750 μm. Peak stress errors of 3% and 38% were incurred in the fibrous cap when stress recovery was performed with and without a priori information about microvessel geometry. The results indicate that incorporating geometric information about plaque microvasculature obtained with contrast-enhanced ultrasound imaging improves the accuracy of estimates of the stress distribution within the fibrous cap precisely.
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Affiliation(s)
- Steven Huntzicker
- Department of Electrical & Computer Engineering, Hajim School of Engineering and Applied Sciences, University of Rochester, Rochester, New York, USA
| | - Himanshu Shekhar
- Department of Electrical & Computer Engineering, Hajim School of Engineering and Applied Sciences, University of Rochester, Rochester, New York, USA
| | - Marvin M Doyley
- Department of Electrical & Computer Engineering, Hajim School of Engineering and Applied Sciences, University of Rochester, Rochester, New York, USA.
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38
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Honarvar M, Rohling R, Salcudean SE. A comparison of direct and iterative finite element inversion techniques in dynamic elastography. Phys Med Biol 2016; 61:3026-48. [DOI: 10.1088/0031-9155/61/8/3026] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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39
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Diaz MI, Aquino W, Bonnet M. A Modified Error in Constitutive Equation Approach for Frequency-Domain Viscoelasticity Imaging Using Interior Data. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2015; 296:129-149. [PMID: 26388656 PMCID: PMC4570248 DOI: 10.1016/j.cma.2015.07.025] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
This paper presents a methodology for the inverse identification of linearly viscoelastic material parameters in the context of steady-state dynamics using interior data. The inverse problem of viscoelasticity imaging is solved by minimizing a modified error in constitutive equation (MECE) functional, subject to the conservation of linear momentum. The treatment is applicable to configurations where boundary conditions may be partially or completely underspecified. The MECE functional measures the discrepancy in the constitutive equations that connect kinematically admissible strains and dynamically admissible stresses, and also incorporates the measurement data in a quadratic penalty term. Regularization of the problem is achieved through a penalty parameter in combination with the discrepancy principle due to Morozov. Numerical results demonstrate the robust performance of the method in situations where the available measurement data is incomplete and corrupted by noise of varying levels.
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Affiliation(s)
- Manuel I. Diaz
- Department of Civil and Environmental Engineering, Duke University, Durham, North Carolina 27708 USA
| | - Wilkins Aquino
- Department of Civil and Environmental Engineering, Duke University, Durham, North Carolina 27708 USA
| | - Marc Bonnet
- POems (UMR 7231 CNRS-ENSTA-INRIA), Dept. of Appl. Math., ENSTA, Paris, France
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40
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Ozkan E, Goksel O. Compliance boundary conditions for simulating deformations in a limited target region. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2015:929-932. [PMID: 26736415 DOI: 10.1109/embc.2015.7318515] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Patient-specific models in medical procedures are often limited to a relatively small region of interest due either to computational concerns or to the fact that only a part of anatomy could be observed in the input medical images. Thus, for deformable planning or training simulations, boundary conditions at the borders of such models are necessitated. Zero-displacement or -force constraints at outer boundaries are commonly used, with the assumption that the selected region is large enough to minimize effects on the deformable behavior inside the region of interest. This may, however, still result in errors and does require superfluous elements to extend models. In this work, a mixed boundary condition type, called compliance boundary condition, is proposed to constrain model boundaries. Different techniques to define and estimate these boundary constraints are studied with simulation experiments. Results are presented for palpation on 2D and 3D phantoms and needle insertion to a male pelvic anatomical model.
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41
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Weis JA, Flint KM, Sanchez V, Yankeelov TE, Miga MI. Assessing the accuracy and reproducibility of modality independent elastography in a murine model of breast cancer. J Med Imaging (Bellingham) 2015; 2:036001. [PMID: 26158120 DOI: 10.1117/1.jmi.2.3.036001] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2015] [Accepted: 06/02/2015] [Indexed: 01/21/2023] Open
Abstract
Cancer progression has been linked to mechanics. Therefore, there has been recent interest in developing noninvasive imaging tools for cancer assessment that are sensitive to changes in tissue mechanical properties. We have developed one such method, modality independent elastography (MIE), that estimates the relative elastic properties of tissue by fitting anatomical image volumes acquired before and after the application of compression to biomechanical models. The aim of this study was to assess the accuracy and reproducibility of the method using phantoms and a murine breast cancer model. Magnetic resonance imaging data were acquired, and the MIE method was used to estimate relative volumetric stiffness. Accuracy was assessed using phantom data by comparing to gold-standard mechanical testing of elasticity ratios. Validation error was [Formula: see text]. Reproducibility analysis was performed on animal data, and within-subject coefficients of variation ranged from 2 to 13% at the bulk level and 32% at the voxel level. To our knowledge, this is the first study to assess the reproducibility of an elasticity imaging metric in a preclinical cancer model. Our results suggest that the MIE method can reproducibly generate accurate estimates of the relative mechanical stiffness and provide guidance on the degree of change needed in order to declare biological changes rather than experimental error in future therapeutic studies.
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Affiliation(s)
- Jared A Weis
- Vanderbilt University , Department of Biomedical Engineering, PMB 351631, 2301 Vanderbilt Place, Nashville, Tennessee 37235-1631, United States ; Vanderbilt University , Institute of Imaging Science, 1161 21st Avenue South, AA-1105 MCN, Nashville, Tennessee 37232-2310, United States ; Vanderbilt University , Radiology and Radiological Sciences, 1161 21st Avenue South, MCN CCC-1118, Nashville, Tennessee 37232-2675, United States
| | - Katelyn M Flint
- Vanderbilt University , Department of Biomedical Engineering, PMB 351631, 2301 Vanderbilt Place, Nashville, Tennessee 37235-1631, United States
| | - Violeta Sanchez
- Vanderbilt University , Vanderbilt-Ingram Cancer Center, 2220 Pierce Avenue, 691 PRB, Nashville, Tennessee 37232-6838, United States
| | - Thomas E Yankeelov
- Vanderbilt University , Department of Biomedical Engineering, PMB 351631, 2301 Vanderbilt Place, Nashville, Tennessee 37235-1631, United States ; Vanderbilt University , Institute of Imaging Science, 1161 21st Avenue South, AA-1105 MCN, Nashville, Tennessee 37232-2310, United States ; Vanderbilt University , Radiology and Radiological Sciences, 1161 21st Avenue South, MCN CCC-1118, Nashville, Tennessee 37232-2675, United States ; Vanderbilt University , Vanderbilt-Ingram Cancer Center, 2220 Pierce Avenue, 691 PRB, Nashville, Tennessee 37232-6838, United States ; Vanderbilt University , Physics and Astronomy, PMB 401807, 2301 Vanderbilt Place, Nashville, Tennessee 37240-1807, United States ; Vanderbilt University , Cancer Biology, 2220 Pierce Avenue, 771 PRB, Nashville, Tennessee 37232-6840, United States
| | - Michael I Miga
- Vanderbilt University , Department of Biomedical Engineering, PMB 351631, 2301 Vanderbilt Place, Nashville, Tennessee 37235-1631, United States ; Vanderbilt University , Institute of Imaging Science, 1161 21st Avenue South, AA-1105 MCN, Nashville, Tennessee 37232-2310, United States ; Vanderbilt University , Radiology and Radiological Sciences, 1161 21st Avenue South, MCN CCC-1118, Nashville, Tennessee 37232-2675, United States ; Vanderbilt University , Vanderbilt-Ingram Cancer Center, 2220 Pierce Avenue, 691 PRB, Nashville, Tennessee 37232-6838, United States ; Vanderbilt University , Neurosurgery, T-4224 MCN Nashville, Tennessee 37232-2380, United States
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Lu M, Tang Y, Sun R, Wang T, Chen S, Mao R. A real time displacement estimation algorithm for ultrasound elastography. COMPUT IND 2015. [DOI: 10.1016/j.compind.2014.09.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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43
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Honarvar M, Lobo J, Mohareri O, Salcudean SE, Rohling R. Direct vibro-elastography FEM inversion in Cartesian and cylindrical coordinate systems without the local homogeneity assumption. Phys Med Biol 2015; 60:3847-68. [DOI: 10.1088/0031-9155/60/9/3847] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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44
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Tyagi M, Goenezen S, Barbone PE, Oberai AA. Algorithms for quantitative quasi-static elasticity imaging using force data. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2014; 30:1421-36. [PMID: 25073623 PMCID: PMC4285660 DOI: 10.1002/cnm.2665] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2014] [Revised: 06/24/2014] [Accepted: 07/20/2014] [Indexed: 05/20/2023]
Abstract
Quasi-static elasticity imaging can improve diagnosis and detection of diseases that affect the mechanical behavior of tissue. In this methodology, images of the shear modulus of the tissue are reconstructed from the measured displacement field. This is accomplished by seeking the spatial distribution of mechanical properties that minimizes the difference between the predicted and the measured displacement fields, where the former is required to satisfy a finite element approximation to the equations of equilibrium. In the absence of force data, the shear modulus is determined only up to a multiplicative constant. In this manuscript, we address the problem of calibrating quantitative elastic modulus reconstructions created from measurements of quasi-static deformations. We present two methods that utilize the knowledge of the applied force on a portion of the boundary. The first involves rescaling the shear modulus of the original minimization problem to best match the measured force data. This approach is easily implemented but neglects the spatial distribution of tractions. The second involves adding a force-matching term to the original minimization problem and a change of variables wherein we seek the log of the shear modulus. We present numerical results that demonstrate the usefulness of both methods.
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Affiliation(s)
- Mohit Tyagi
- Department of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute Troy, NY 12180
| | - Sevan Goenezen
- Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843
| | | | - Assad A. Oberai
- Department of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute Troy, NY 12180
- Correspondence to: Department of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute, Phone: 518 276 3386, Fax: 518 276 3055,
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45
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Mousavi SR, Sadeghi-Naini A, Czarnota GJ, Samani A. Towards clinical prostate ultrasound elastography using full inversion approach. Med Phys 2014; 41:033501. [PMID: 24593743 DOI: 10.1118/1.4864476] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
PURPOSE Various types of cancers including prostate cancer are known to be associated with biological changes that lead to tissue stiffening. Digital rectal examination is based on manually palpating the prostate tissue via the rectum. This test lacks sufficient accuracy required for early diagnosis which is necessary for effective management of prostate cancer. To develop an effective prostate cancer diagnostic technique, the authors propose an imaging technique that maps the distribution of the relative prostate tissue's elasticity modulus. Unlike digital rectal examination, this technique is quantitative, capable of accurately detecting small prostate lesions that cannot be sensed by manual palpation, and its accuracy is independent of the physician's experience. METHODS The proposed technique is a quasistatic elastography technique which uses ultrasound imaging to acquire tissue displacements resulting from transrectal ultrasound mechanical stimulation. The system involves a standard ultrasound imaging unit with accessibility to its radiofrequency data. The displacements are used as data for the tissue elasticity reconstruction. This reconstruction does not require tissue segmentation and is based on physics governing tissue mechanics. It is formulated using an inverse problem framework where elastic tissue deformation equations are fully inverted using an iterative scheme where each iteration involves stress calculation followed by elastic modulus updating until convergence is achieved.In silico and tissue mimicking phantom studies were conducted to validate the proposed technique, followed by a clinical pilot study involving two prostate cancer patients with whole-mount histopathology analysis on prostatectomy specimens to confirm a cancer location. RESULTS The phantom studies demonstrated robustness and reasonably high accuracy of the proposed method. Obtained Young's modulus ratios indicated reconstruction errors of less than 12%. Reconstructed elastic modulus images of the two clinical cases were compared to whole-mount histopathology slides where cancerous areas were identified. This comparison indicated marked tissue stiffening in the cancer area with reasonably accurate consistency observed between cancerous lesions identified by histopathology and high stiffness areas of the elastography images. CONCLUSIONS Results obtained from the phantom and patient studies indicate that the proposed method is reasonably accurate for detecting cancerous lesions. The proposed system does not require any additional hardware attachment for mechanical stimulation or data acquisition while the elasticity reconstruction algorithm can be easily implemented, leading to a low cost system that can be potentially utilized as an effective clinical tool for prostate cancer diagnosis.
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Affiliation(s)
- Seyed Reza Mousavi
- Department of Electrical and Computer Engineering, Western University, London, Ontario N6A 5B9, Canada
| | - Ali Sadeghi-Naini
- Departments of Medical Biophysics, Radiation Oncology and Imaging Research - Physical Sciences, University of Toronto and Sunnybrook Health Sciences Centre, Toronto, Ontario M4N 3M5, Canada
| | - Gregory J Czarnota
- Departments of Medical Biophysics, Radiation Oncology and Imaging Research - Physical Sciences, University of Toronto and Sunnybrook Health Sciences Centre, Toronto, Ontario M4N 3M5, Canada
| | - Abbas Samani
- Department of Electrical and Computer Engineering, Western University, London, Ontario N6A 5B9, Canada; Department of Medical Biophysics, Western University, London, Ontario N6A 5C1, Canada; Graduate Program in Biomedical Engineering, Western University, London, Ontario N6A 5B9, Canada; and Imaging Research Laboratories, Robarts Research Institute (RRI), London, Ontario N6A 5K8, Canada
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46
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Frisch KE, Marcu D, Baer GS, Thelen DG, Vanderby R. Influence of tendon tears on ultrasound echo intensity in response to loading. J Biomech 2014; 47:3813-9. [PMID: 25468301 DOI: 10.1016/j.jbiomech.2014.10.026] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2014] [Revised: 09/18/2014] [Accepted: 10/17/2014] [Indexed: 10/24/2022]
Abstract
Acoustoelastic (AE) ultrasound image analysis is a promising non-invasive approach that uses load-dependent echo intensity changes to characterize stiffness of tendinous tissue. The purpose of this study was to investigate whether AE can detect localized changes in tendon stiffness due to partial and full-thickness tendon tears. Ovine infraspinatus tendons with different levels of damage (Intact, 33%, 66% and full thickness cuts initiated on the articular and bursal sides) were cyclically loaded in a mechanical testing system while cine ultrasound images were recorded. The load-induced changes in echo intensity on the bursal and articular side of the tendon were determined. Consistent with AE theory, the undamaged tendons exhibited an increase in echo intensity with tendon loading, reflecting the strain-stiffening behavior of the tissue. In the intact condition, the articular region demonstrated a significantly greater increase in echo intensity during loading than the bursal region. Cuts initiated on the bursal side resulted in a progressive decrease in echo intensity of the adjacent tissue, likely reflecting the reduced load transmission through that region. However, image intensity information was less sensitive for identifying load transmission changes that result from partial thickness cuts initiated on the articular side. We conclude that AE approaches may be useful to quantitatively assess load-dependent changes in tendon stiffness, and that disruption of AE behavior may be indicative of substantial tendon damage.
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Affiliation(s)
- Kayt E Frisch
- Department of Engineering/Dordt College/Sioux Center, Iowa, United States.
| | - David Marcu
- Sauk Prairie Memorial Hospital, Prairie du Sac, Wiscosin, United States.
| | - Geoffrey S Baer
- Department of Orthopedics and Rehabilitation/University of Wisconsin, Madison, Wiscosin, United States.
| | - Darryl G Thelen
- Department of Biomedical Engineering, Department of Mechanical Engineering/University of Wisconsin, Madison, Wiscosin, United States.
| | - Ray Vanderby
- Departments of Orthopedics and Rehabilitation and Biomedical Engineering/University of Wisconsin, Madison, Wiscosin, United States.
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Pan X, Liu K, Bai J, Luo J. A regularization-free elasticity reconstruction method for ultrasound elastography with freehand scan. Biomed Eng Online 2014; 13:132. [PMID: 25194553 PMCID: PMC4164754 DOI: 10.1186/1475-925x-13-132] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2014] [Accepted: 09/02/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In ultrasound elastography, reconstruction of tissue elasticity (e.g., Young's modulus) requires regularization and known information of forces and/or displacements on tissue boundaries. In practice, it is challenging to choose an appropriate regularization parameter; and the boundary conditions are difficult to obtain in vivo. The purpose of this study is to develop a more applicable algorithm that does not need any regularization or boundary force/displacement information. METHODS The proposed method adopts the bicubic B-spline as the tissue motion model to estimate the displacement fields. Then the estimated displacements are input to the finite element inversion scheme to reconstruct the Young's modulus of each element. In the inversion, a modulus boundary condition is used instead of force/displacement boundary conditions. Simulation and experiments on tissue-mimicking phantoms are carried out to test the proposed method. RESULTS The simulation results demonstrate that Young's modulus reconstruction of the proposed method has a relative error of -3.43 ± 0.43% and root-squared-mean error of 16.94 ± 0.25%. The phantom experimental results show that the target hardening artifacts in the strain images are significantly reduced in the Young's modulus images. In both simulation and phantom studies, the size and position of inclusions can be accurately depicted in the modulus images. CONCLUSIONS The proposed method can reconstruct tissue Young's modulus distribution with a high accuracy. It can reduce the artifacts shown in the strain image and correctly delineate the locations and sizes of inclusions. Unlike most modulus reconstruction methods, it does not need any regularization during the inversion procedure. Furthermore, it does not need to measure the boundary conditions of displacement or force. Thus this method can be used with a freehand scan, which facilitates its usage in the clinic.
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Affiliation(s)
- Xiaochang Pan
- />Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084 China
| | - Ke Liu
- />Division of Electronics and Information Technology, National Institute of Metrology, Beijing, 100013 China
| | - Jing Bai
- />Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084 China
| | - Jianwen Luo
- />Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084 China
- />Center for Biomedical Imaging Research, Tsinghua University, Beijing, 100084 China
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48
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Chin L, Curatolo A, Kennedy BF, Doyle BJ, Munro PRT, McLaughlin RA, Sampson DD. Analysis of image formation in optical coherence elastography using a multiphysics approach. BIOMEDICAL OPTICS EXPRESS 2014; 5:2913-30. [PMID: 25401007 PMCID: PMC4230875 DOI: 10.1364/boe.5.002913] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2014] [Revised: 07/17/2014] [Accepted: 07/23/2014] [Indexed: 05/18/2023]
Abstract
IMAGE FORMATION IN OPTICAL COHERENCE ELASTOGRAPHY (OCE) RESULTS FROM A COMBINATION OF TWO PROCESSES: the mechanical deformation imparted to the sample and the detection of the resulting displacement using optical coherence tomography (OCT). We present a multiphysics model of these processes, validated by simulating strain elastograms acquired using phase-sensitive compression OCE, and demonstrating close correspondence with experimental results. Using the model, we present evidence that the approximation commonly used to infer sample displacement in phase-sensitive OCE is invalidated for smaller deformations than has been previously considered, significantly affecting the measurement precision, as quantified by the displacement sensitivity and the elastogram signal-to-noise ratio. We show how the precision of OCE is affected not only by OCT shot-noise, as is usually considered, but additionally by phase decorrelation due to the sample deformation. This multiphysics model provides a general framework that could be used to compare and contrast different OCE techniques.
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Affiliation(s)
- Lixin Chin
- Optical + Biomedical Engineering Laboratory, School of Electrical, Electronic & Computer Engineering, The University of Western Australia, Crawley, Australia
- These authors contributed equally to this work
| | - Andrea Curatolo
- Optical + Biomedical Engineering Laboratory, School of Electrical, Electronic & Computer Engineering, The University of Western Australia, Crawley, Australia
- These authors contributed equally to this work
| | - Brendan F. Kennedy
- Optical + Biomedical Engineering Laboratory, School of Electrical, Electronic & Computer Engineering, The University of Western Australia, Crawley, Australia
| | - Barry J. Doyle
- Vascular Engineering, Intelligent Systems for Medicine Laboratory, School of Mechanical & Chemical Engineering, The University of Western Australia, Crawley, Australia
- Centre for Cardiovascular Science, The University of Edinburgh, Edinburgh, UK
| | - Peter R. T. Munro
- Optical + Biomedical Engineering Laboratory, School of Electrical, Electronic & Computer Engineering, The University of Western Australia, Crawley, Australia
- Centre for Microscopy, Characterisation & Analysis, The University of Western Australia, Crawley, Australia
| | - Robert A. McLaughlin
- Optical + Biomedical Engineering Laboratory, School of Electrical, Electronic & Computer Engineering, The University of Western Australia, Crawley, Australia
| | - David D. Sampson
- Optical + Biomedical Engineering Laboratory, School of Electrical, Electronic & Computer Engineering, The University of Western Australia, Crawley, Australia
- Centre for Microscopy, Characterisation & Analysis, The University of Western Australia, Crawley, Australia
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49
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Lopata RGP, Peters MFJ, Nijs J, Oomens CWJ, Rutten MCM, van de Vosse FN. Vascular elastography: a validation study. ULTRASOUND IN MEDICINE & BIOLOGY 2014; 40:1882-1895. [PMID: 24798385 DOI: 10.1016/j.ultrasmedbio.2014.02.029] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2013] [Revised: 01/13/2014] [Accepted: 02/23/2014] [Indexed: 06/03/2023]
Abstract
Vascular elastography techniques are promising tools for mechanical characterization of diseased arteries. These techniques are usually validated with simulations or phantoms or by comparing results with histology or other imaging modalities. In the study described here, vascular elastography was applied to porcine aortas in vitro during inflation testing (n = 10) and results were compared with those of standard bi-axial tensile testing, a technique that directly measures the force applied to the tissue. A neo-Hookean model was fit to the stress-strain data, valid for large deformations. Results indicated good correspondence between the two techniques, with GUS = 110 ± 11 kPa and GTT = 108 ± 10 kPa for ultrasound and tensile testing, respectively. Bland-Altman analysis revealed little bias (GUS-GTT = 2 ± 20 kPa). The next step will be the application of a non-linear material model that is also adaptable for in vivo measurements.
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Affiliation(s)
- Richard G P Lopata
- Cardiovascular Biomechanics, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
| | - Mathijs F J Peters
- Cardiovascular Biomechanics, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Jan Nijs
- Department of Cardiac Surgery, University Hospital Brussels, Brussels, Belgium
| | - Cees W J Oomens
- Soft Tissue Biomechanics & Engineering, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Marcel C M Rutten
- Cardiovascular Biomechanics, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Frans N van de Vosse
- Cardiovascular Biomechanics, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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50
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Huntzicker S, Nayak R, Doyley MM. Quantitative sparse array vascular elastography: the impact of tissue attenuation and modulus contrast on performance. J Med Imaging (Bellingham) 2014; 1:027001. [PMID: 26158040 PMCID: PMC4478787 DOI: 10.1117/1.jmi.1.2.027001] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2014] [Revised: 05/29/2014] [Accepted: 05/30/2014] [Indexed: 11/14/2022] Open
Abstract
Quantitative sparse array vascular elastography visualizes the shear modulus distribution within vascular tissues, information that clinicans could use to reduce the number of strokes each year. However, the low transmit power sparse array (SA) imaging could hamper the clinical usefulness of the resulting elastograms. In this study, we evaluated the performance of modulus elastograms recovered from simulated and physical vessel phantoms with varying attenuation coefficients (0.6, 1.5, and [Formula: see text]) and modulus contrasts ([Formula: see text], [Formula: see text], and [Formula: see text]) using SA imaging relative to those obtained with conventional linear array (CLA) and plane-wave (PW) imaging techniques. Plaques were visible in all modulus elastograms, but those produced using SA and PW contained less artifacts. The modulus contrast-to-noise ratio decreased rapidly with increasing modulus contrast and attenuation coefficient, but more quickly when SA imaging was performed than for CLA or PW. The errors incurred varied from 10.9% to 24% (CLA), 1.8% to 12% (SA), and [Formula: see text] (PW). Modulus elastograms produced with SA and PW imagings were not significantly different ([Formula: see text]). Despite the low transmit power, SA imaging can produce useful modulus elastograms in superficial organs, such as the carotid artery.
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Affiliation(s)
- Steven Huntzicker
- University of Rochester, Hajim School of Engineering and Applied Sciences, Department of Electrical and Computer Engineering, Rochester, New York 14627
| | - Rohit Nayak
- University of Rochester, Hajim School of Engineering and Applied Sciences, Department of Electrical and Computer Engineering, Rochester, New York 14627
| | - Marvin M. Doyley
- University of Rochester, Hajim School of Engineering and Applied Sciences, Department of Electrical and Computer Engineering, Rochester, New York 14627
- University of Rochester, Hajim School of Engineering and Applied Sciences, Department of Biomedical Engineering, Rochester, New York 14627
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