RESUMEN
Purpose: The aortic time-enhancement curve obtained from dynamic CT myocardial perfusion imaging can be used to derive the cardiac output (CO) index based on the indicator dilution principle. The objective of this study was to investigate the effect of cardiac phase at which CT myocardial perfusion imaging is triggered on the CO index measurement with this approach. Methods: Electrocardiogram (ECG) gated myocardial perfusion imaging was performed on farm pigs with consecutive cardiac axial scans using a large-coverage CT scanner (Revolution, GE Healthcare) after intravenous contrast administration. Multiple sets of dynamic contrast-enhanced (DCE) cardiac images were reconstructed retrospectively from 30% to 80% R-R intervals with a 5% phase increment. The time-enhancement curve sampled from above the aortic orifice in each DCE image set was fitted with a modified gamma variate function (MGVF). The fitted curve was then normalized to the baseline data point unaffected by the streak artifact emanating from the contrast solution in the right heart chamber. The Stewart−Hamilton equation was used to calculate the CO index based on the integral of the fitted normalized aortic curve, and the results were compared among different cardiac phases. Results: The aortic time-enhancement curves sampled at different cardiac phases were different from each other, especially in the baseline portion of the curve where the effect of streak artifact was prominent. After properly normalizing and denoising with a MGVF, the integrals of the aortic curve were minimally different among cardiac phases (0.228 ± 0.001 Hounsfield Unit × second). The corresponding mean CO index was 4.031 ± 0.028 L/min. There were no statistical differences in either the integral of the aortic curve or CO index among different cardiac phases (p > 0.05 for all phases).
Asunto(s)
Imagen de Perfusión Miocárdica , Animales , Gasto Cardíaco , Corazón/diagnóstico por imagen , Imagen de Perfusión Miocárdica/métodos , Estudios Retrospectivos , Porcinos , Tomografía Computarizada por Rayos X/métodosRESUMEN
The mechanical properties of normal soft tissues, including breast tissue, have been of interest to the biomedical research community as there are many clinical and industrial applications that can benefit from quantitative information characterizing such properties. For instance, computer assisted surgery planning, elastography for breast cancer diagnosis, and bra design can all involve biomechanical modeling of the breast to predict its deformation or stress distribution. It is known that most biological soft tissues, including breast tissue, exhibit nonlinear mechanical response over large strains. As such, it is necessary to model such tissues as hyperelastic. In this work, we used indentation testing to estimate the hyperelastic parameters of 4 models (3rd order Ogden, 5-term polynomial, Veronda-Westman and Yeoh) estimated from 72 healthy ex vivo breast tissue samples covering adipose, fibroglandular, and mixed tissue. All estimated parameter sets were confirmed to represent stable material using Drucker's stability criterion. We observed that all three tissue types were statistically similar solidifying the use of homogenous breast modelling over large strain simulation.
Asunto(s)
Mama , Diagnóstico por Imagen de Elasticidad , Algoritmos , Fenómenos Biomecánicos , Simulación por Computador , Elasticidad , Análisis de Elementos Finitos , Humanos , Modelos Biológicos , Estrés MecánicoRESUMEN
There is growing awareness that brain mechanical properties are important for neural development and health. However, published values of brain stiffness differ by orders of magnitude between static measurements and in vivo magnetic resonance elastography (MRE), which covers a dynamic range over several frequency decades. We here show that there is no fundamental disparity between static mechanical tests and in vivo MRE when considering large-scale properties, which encompass the entire brain including fluid filled compartments. Using gradient echo real-time MRE, we investigated the viscoelastic dispersion of the human brain in, so far, unexplored dynamic ranges from intrinsic brain pulsations at 1 Hz to ultralow-frequency vibrations at 5, 6.25, 7.8 and 10 Hz to the normal frequency range of MRE of 40 Hz. Surprisingly, we observed variations in brain stiffness over more than two orders of magnitude, suggesting that the in vivo human brain is superviscous on large scales with very low shear modulus of 42±13 Pa and relatively high viscosity of 6.6±0.3 Paâs according to the two-parameter solid model. Our data shed light on the crucial role of fluid compartments including blood vessels and cerebrospinal fluid (CSF) for whole brain properties and provide, for the first time, an explanation for the variability of the mechanical brain responses to manual palpation, local indentation, and high-dynamic tissue stimulation as used in elastography.
Asunto(s)
Diagnóstico por Imagen de Elasticidad , Encéfalo/diagnóstico por imagen , Elasticidad , Humanos , Imagen por Resonancia Magnética , Vibración , ViscosidadRESUMEN
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.
Asunto(s)
Diagnóstico por Imagen de Elasticidad , Algoritmos , Femenino , Humanos , Fantasmas de Imagen , Reproducibilidad de los Resultados , Ultrasonografía MamariaRESUMEN
Damaged cardiac muscle tissue caused by ischemia leads to compromised cardiac function. While conventional imaging can view the ischemic tissue, currently there is no clinical way to quantitatively predict improved heart function after revascularization treatment. This increases the decision difficulty of treatment planning as there is no guarantee the heart function will improve enough to justify the cost of revascularization treatment. The complement of biomechanical modelling with conventional imaging offers an alternative method to determine the amount of ischemic tissue which can then be used as a potential predictor to estimate the range of functional improvement. A novel shape optimization technique is presented to predict the contractility of ischemic tissue in an in-silico left ventricle model that has suffered acute myocardial infarction. Preliminary results show that the proposed technique can reconstruct the damage caused by ischemic tissue within 18%. A range of minimum to maximum predicted cardiac improvement can then be given based on this error to help decide if the cost of revascularization treatment is justified.