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.
Hyperelastic properties of deflated lung tissue have been characterized via an inverse finite element approach. Such properties are useful in many medical diagnosis and treatment applications where ...tissue deformation can be modeled to account for during the procedure. Several indentation experiments were conducted on various porcine lungs' tissue specimens resected immediately from different regions and lobes after the animals were sacrificed. Three different strain energy models, namely Ogden, Yeoh, and Polynomial, were used and respective hyperelastic parameters were obtained. The parameters for each model were estimated through an optimization process where the experimental force-displacement profiles of indentation were fitted to those obtained from finite element simulations performed specifically for the samples' geometries. Results obtained in this investigation for all the three models indicate convergence with reasonably low average fitting errors ranging from 2.3% to 6.2%. Independent tests were also performed to assess the effects of samples' heterogeneities on the obtained parameters. The outcome of these tests was encouraging and confirmed small impact of tissue inhomogeneities on the estimated parameters. The reported hyperelastic properties can, accordingly, pave the way for more accurate biomechanical modeling of the lung's soft tissue in the emerging applications of minimally invasive medical intervention for lung cancer diagnosis and treatment.
Purpose:
Association between tissue stiffness alteration and pathology is well known. This has formed the basis for prostate elastography imaging techniques where images of prostate tissue mechanical ...properties are reconstructed. In this paper, the authors present a novel prostate elastography technique which, unlike other techniques, relies on magnitude image data only.
Methods:
This proposed technique works in conjunction with ultrasound or magnetic resonance imaging (MRI) imaging modalities and it requires the prostate's pre‐ and postdeformation images as input. It uses a constrained reconstruction method where the elastic moduli of the prostate's normal and pathological tissues are determined based on an essential subset of the tissue deformation provided by the images data. The elasticity reconstruction technique uses optimization where similarity between calculated and observed shape features of the postcompression prostate image is maximized. The method was validated with an in silico phantom study followed by studies using ultrasound and MR with tissue‐mimicking phantoms.
Results:
Using the proposed methods, it was observed that the maximum uncertainties of the reconstructed Young's modulus ratios of tumor to normal tissue were 15.6% and 9.7%, which were obtained from the transrectal ultrasound (TRUS) and MR tissue‐mimicking phantom studies, respectively.
Conclusions:
This novel prostate elastography technique relies on prostate TRUS or MRI images that can be routinely acquired without additional imaging hardware. The phantom studies provided evidence that the proposed technique has a good potential to reconstruct prostate stiffness maps noninvasively particularly when applied in conjunction with MRI. Further studies are necessary to evaluate the technique's merits for clinical use.
Patient-specific finite element (FE) modeling of the upper airway is an effective tool for accurate assessment of obstructive sleep apnea (OSA) syndrome. It is also useful for planning minimally ...invasive surgical procedures under severe OSA conditions. A major requirement of FE modeling is having reliable data characterizing the biomechanical properties of the upper airway tissues, particularly oropharyngeal soft tissue. While some data characterizing this tissue's linear elastic regime is available, reliable data characterizing its hyperelasticity is scarce. The aim of the current study is to estimate the hyperelastic mechanical properties of the oropharyngeal soft tissues, including the palatine tonsil, soft palate, uvula, and tongue base. Fresh tissue specimens of human oropharyngeal tissue were acquired from 13 OSA patients who underwent standard surgical procedures. Indentation testing was performed on the specimens to obtain their force–displacement data. To determine the specimens' hyperelastic parameters using these data, an inverse FE framework was utilized. In this work, the hyperelastic parameters corresponding to the commonly used Yeoh and 2nd order Ogden models were obtained. Both models captured the experimental force-displacement data of the tissue specimens reasonably accurately with mean errors of 11.65% or smaller. This study has provided estimates of the hyperelastic parameters of all upper airway soft tissues using fresh human tissue specimens for the first time.
Computational models are effective tools to study cardiac mechanics under normal and pathological conditions. They can be used to gain insight into the physiology of the heart under these conditions ...while they are adaptable to computer assisted patient-specific clinical diagnosis and therapeutic procedures. Realistic cardiac mechanics models incorporate tissue active/passive response in conjunction with hyperelasticity and anisotropy. Conventional formulation of such models leads to mathematically-complex problems usually solved by custom-developed non-linear finite element (FE) codes. With a few exceptions, such codes are not available to the research community. This article describes a computational cardiac mechanics model developed such that it can be implemented using off-the-shelf FE solvers while tissue pathologies can be introduced in the model in a straight-forward manner. The model takes into account myocardial hyperelasticity, anisotropy, and active contraction forces. It follows a composite tissue modeling approach where the cardiac tissue is decomposed into two major parts: background and myofibers. The latter is modelled as rebars under initial stresses mimicking the contraction forces. The model was applied in silico to study the mechanics of infarcted left ventricle (LV) of a canine. End-systolic strain components, ejection fraction, and stress distribution attained using this LV model were compared quantitatively and qualitatively to corresponding data obtained from measurements as well as to other corresponding LV mechanics models. This comparison showed very good agreement.
Objective
The use of computer simulation to develop a high‐fidelity model has been proposed as a novel and cost‐effective alternative to help guide therapeutic intervention in sleep apnea surgery. We ...describe a computer model based on patient‐specific anatomy of obstructive sleep apnea (OSA) subjects wherein the percentage and sites of upper airway collapse are compared to findings on drug‐induced sleep endoscopy (DISE).
Study Design
Basic science computer model generation.
Methods
Three‐dimensional finite element techniques were undertaken for model development in a pilot study of four OSA patients. Magnetic resonance imaging was used to capture patient anatomy and software employed to outline critical anatomical structures. A finite‐element mesh was applied to the volume enclosed by each structure. Linear and hyperelastic soft‐tissue properties for various subsites (tonsils, uvula, soft palate, and tongue base) were derived using an inverse finite‐element technique from surgical specimens. Each model underwent computer simulation to determine the degree of displacement on various structures within the upper airway, and these findings were compared to DISE exams performed on the four study patients.
Results
Computer simulation predictions for percentage of airway collapse and site of maximal collapse show agreement with observed results seen on endoscopic visualization.
Conclusion
Modeling the upper airway in OSA patients is feasible and holds promise in aiding patient‐specific surgical treatment.
Level of Evidence
NA. Laryngoscope, 128:277–282, 2018
For studying cardiac mechanics, hyperelastic anisotropic computational models have been developed which require the tissue anisotropic and hyperelastic parameters. These parameters are obtained by ...tissue samples mechanically testing. The validity of such parameters are limited to the specific tissue sample only. They are not adaptable for pathological tissues commonly associated with tissue microstructure alterations. To investigate cardiac tissue mechanics, a novel approach is proposed to model hyperelasticity and anisotropy. This approach is adaptable to various tissue microstructural constituent's distributions in normal and pathological tissues. In this approach, the tissue is idealized as composite material consisting of cardiomyocytes distributed in extracellular matrix (ECM). The major myocardial tissue constituents are mitochondria and myofibrils while the main ECM's constituents are collagen fibers and fibroblasts. Accordingly, finite element simulations of uniaxial and equibiaxial tests of normal and infarcted tissue samples with known amounts of these constituents were conducted, leading to corresponding tissue stress-strain data that were fitted to anisotropic/hyperelastic models. The models were validated where they showed good agreement characterized by maximum average stress-strain errors of 16.17 and 10.01% for normal and infarcted cardiac tissue, respectively. This demonstrate the effectiveness of the proposed models in accurate characterization of healthy and pathological cardiac tissues.
A quasistatic magnetic resonance elastography (MRE) method for the evaluation of breast cancer is proposed. Using a phase contrast, stimulated echo MRI approach, strain imaging in phantoms and ...volunteers is presented. First-order assessment of tissue biomechanical properties based on inverse strain mapping is outlined and demonstrated. The accuracy of inverse strain imaging is studied through simulations in a two-dimensional model and in an anthropomorphic, three-dimensional finite-element model of the breast. To improve the accuracy of modulus assessment by elastography, inverse methods are discussed as an extension to strain imaging, and simulations quantify MRE in terms of displacement signal/noise required for robust inversion. A direct inversion strategy providing information on tissue modulus and pressure distribution is described along with a novel iterative method utilizing a priori knowledge of tissue geometry. It is shown that through the judicious choice of information from previous contrast-enhanced MRI breast images, MRE data acquisition requirements can be significantly reduced while maintaining robust modulus reconstruction in the presence of strain noise. An experimental apparatus for clinical breast MRE and preliminary images of a normal volunteer are presented.
Compressibility of biological tissues such as brain parenchyma is related to its poroelastic nature characterized by the geometry and pressure of vasculature and interconnected fluid-filled spaces. ...Thus, cerebral volumetric strain may be sensitive to intracranial pressure which can be altered under physiological conditions. So far volumetric strain has attained little attention in studies of the mechanical behavior of the brain. This paper reports a study of measuring the in vivo cerebral volumetric strain induced by the Valsalva maneuver (VM) where forced expiration against a closed glottis leads to a transient increase in the intracranial pressure. For this purpose we applied three-dimensional magnetic resonance imaging equipped with a patient-controlled acquisition system to five healthy volunteers. With each volunteer, three experiments were performed: one with VM and two in resting state. i.e. normal ventilation, which were conducted before and after VM. The VM data were registered to reference data by morphology based non-rigid deformation, yielding 3D maps of total displacements and volumetric strain. On average, VM induced volumetric strain correlated to whole-brain dilatation of -3.14±0.87% and -2.80±0.71% compared to the reference states before and after VM, respectively. These values were well reproduced by repetitive experiments during the same scan as well as by repeated measurements in one volunteer on different days. Combined with literature data of intracranial pressure changes, our volumetric strain values can be used to elucidate the static compression modulus of the in vivo human brain. These results add knowledge to the understanding of the brain's biomechanical properties under physiological conditions.
Several cancer types, including breast cancer, are associated with tissue structural changes that yield tissue stiffening. Clinical breast examination (CBE) is a physical examination of the breast to ...find palpable breast tumors. This test lacks accuracy necessary for effective assessment and diagnosis of breast cancer. To develop an effective breast cancer diagnostic technique, an imaging method is proposed that maps the distribution of breast tissue relative elasticity modulus. Unlike CBE, this technique is quantitative; hence, it is expected that its accuracy is independent of the physician's experience. The proposed technique is a quasi-static elastography technique which uses radiofrequency data acquired through ultrasound imaging to determine both axial and lateral tissue displacements resulting from tissue mechanical stimulation. These displacements serve as input data for elastography image reconstruction. The reconstruction technique is developed using a full inversion framework where elastic tissue deformation equations are inverted using an iterative process. Each iteration in this process involves stress computation using finite-element analysis followed by updating elastic modulus until convergence is achieved. The proposed technique was validated by two tissue mimicking phantom studies before it was successfully applied to a clinical case. The two independent phantom studies demonstrated the robustness of the proposed method demonstrated by reconstruction errors of less than 12%. Elastic modulus images of the clinical case were compared to corresponding B-modes images where cancerous areas were identified as hypo-echoic areas. This comparison indicated marked tissue stiffening in those areas. Results obtained from the phantom and patient studies conducted in this study indicate that the proposed method is reasonably accurate; hence, the technique can be potentially used for quantitative assessment of breast cancer. The elasticity reconstruction algorithm developed in this work can be easily implemented on clinical ultrasound systems with no requirement to any additional hardware attachment for mechanical stimulation or data acquisition. As such, it can be applied as a low cost and potentially widely available technology for breast cancer diagnosis.
