Measurement Of Tissue Properties Research Articles

Imaging Biomarkers of Oral Dysplasia and Carcinoma Measured with In Vivo Endoscopic Optical Coherence Tomography.

Optical coherence tomography is a noninvasive imaging technique that provides three-dimensional visualization of subsurface tissue structures. OCT has been proposed and explored in the literature as a tool to assess oral cancer status, select biopsy sites, or identify surgical margins. Our endoscopic OCT device can generate widefield (centimeters long) imaging of lesions at any location in the oral cavity-but it is challenging for raters to quantitatively assess and score large volumes of data. Leveraging a previously developed epithelial segmentation network, this work develops quantifiable biomarkers that provide direct measurements of tissue properties in three dimensions. We hypothesize that features related to morphology, tissue attenuation, and contrast between tissue layers will be able to provide a quantitative assessment of disease status (dysplasia through carcinoma). This work retrospectively assesses seven biomarkers on a lesion-contralateral matched OCT dataset of the lateral and ventral tongue (40 patients, 70 sites). Epithelial depth and loss of epithelial-stromal boundary visualization provide the strongest discrimination between disease states. The stroma optical attenuation coefficient provides a distinction between benign lesions from dysplasia and carcinoma. The stratification biomarkers visualize subsurface changes, which provides potential for future utility in biopsy site selection or treatment margin delineation.

The Biomechanics of Musculoskeletal Tissues during Activities of Daily Living: Dynamic Assessment Using Quantitative Transmission-Mode Ultrasound Techniques.

The measurement of musculoskeletal tissue properties and loading patterns during physical activity is important for understanding the adaptation mechanisms of tissues such as bone, tendon, and muscle tissues, particularly with injury and repair. Although the properties and loading of these connective tissues have been quantified using direct measurement techniques, these methods are highly invasive and often prevent or interfere with normal activity patterns. Indirect biomechanical methods, such as estimates based on electromyography, ultrasound, and inverse dynamics, are used more widely but are known to yield different parameter values than direct measurements. Through a series of literature searches of electronic databases, including Pubmed, Embase, Web of Science, and IEEE Explore, this paper reviews current methods used for the in vivo measurement of human musculoskeletal tissue and describes the operating principals, application, and emerging research findings gained from the use of quantitative transmission-mode ultrasound measurement techniques to non-invasively characterize human bone, tendon, and muscle properties at rest and during activities of daily living. In contrast to standard ultrasound imaging approaches, these techniques assess the interaction between ultrasound compression waves and connective tissues to provide quantifiable parameters associated with the structure, instantaneous elastic modulus, and density of tissues. By taking advantage of the physical relationship between the axial velocity of ultrasound compression waves and the instantaneous modulus of the propagation material, these techniques can also be used to estimate the in vivo loading environment of relatively superficial soft connective tissues during sports and activities of daily living. This paper highlights key findings from clinical studies in which quantitative transmission-mode ultrasound has been used to measure the properties and loading of bone, tendon, and muscle tissue during common physical activities in healthy and pathological populations.

Quantifying 3D MR fingerprinting (3D-MRF) reproducibility across subjects, sessions, and scanners automatically using MNI atlases.

Quantitative MRI techniques such as MR fingerprinting (MRF) promise more objective and comparable measurements of tissue properties at the point-of-care than weighted imaging. However, few direct cross-modal comparisons of MRF's repeatability and reproducibility versus weighted acquisitions have been performed. This work proposes a novel fully automated pipeline for quantitatively comparing cross-modal imaging performance in vivo via atlas-based sampling. We acquire whole-brain 3D-MRF, turbo spin echo, and MPRAGE sequences three times each on two scanners across 10 subjects, for a total of 60 multimodal datasets. The proposed automated registration and analysis pipeline uses linear and nonlinear registration to align all qualitative and quantitative DICOM stacks to Montreal Neurological Institute (MNI) 152 space, then samples each dataset's native space through transformation inversion to compare performance within atlas regions across subjects, scanners, and repetitions. Voxel values within MRF-derived maps were found to be more repeatable (σT1 = 1.90, σT2 = 3.20) across sessions than vendor-reconstructed MPRAGE (σT1w = 6.04) or turbo spin echo (σT2w = 5.66) images. Additionally, MRF was found to be more reproducible across scanners (σT1 = 2.21, σT2 = 3.89) than either qualitative modality (σT1w = 7.84, σT2w = 7.76). Notably, differences between repeatability and reproducibility of in vivo MRF were insignificant, unlike the weighted images. MRF data from many sessions and scanners can potentially be treated as a single dataset for harmonized analysis or longitudinal comparisons without the additional regularization steps needed for qualitative modalities.

A narrative review of the measurement methods for biomechanical properties of plantar soft tissue in patients with diabetic foot.

This article provides an overview of the development history and advantages and disadvantages of measurement methods for soft tissue properties of the plantar foot. The measurement of soft tissue properties is essential for understanding the biomechanical characteristics and function of the foot, as well as for designing and evaluating orthotic devices and footwear. Various methods have been developed to measure the properties of plantar soft tissues, including ultrasound imaging, indentation testing, magnetic resonance elastography, and shear wave elastography. Each method has its own strengths and limitations, and choosing the most appropriate method depends on the specific research or clinical objectives. This review aims to assist researchers and clinicians in selecting the most suitable measurement method for their specific needs.

Data-driven myelin water imaging based on T1 and T2 relaxometry.

Long acquisition times preclude the application of multiecho spin echo (MESE) sequences for myelin water fraction (MWF) mapping in daily clinical practice. In search of alternative methods, previous studies of interest explored the biophysical modeling of MWF from measurements of different tissue properties that can be obtained in scan times shorter than those required for the MESE. In this work, a novel data-driven estimation of MWF maps from fast relaxometry measurements is proposed and investigated. T1 and T2 relaxometry maps were acquired in a cohort of 20 healthy subjects along with a conventional MESE sequence. Whole-brain quantitative mapping was achieved with a fast protocol in 6 min 24 s. Reference MWF maps were derived from the MESE sequence (TA = 11 min 17 s) and their data-driven estimation from relaxometry measurements was investigated using three different modeling strategies: two general linear models (GLMs) with linear and quadratic regressors, respectively; a random forest regression model; and two deep neural network architectures, a U-Net and a conditional generative adversarial network (cGAN). Models were validated using a 10-fold crossvalidation. The resulting maps were visually and quantitatively compared by computing the root mean squared error (RMSE) between the estimated and reference MWF maps, the intraclass correlation coefficients (ICCs) between corresponding MWF values in different brain regions, and by performing Bland-Altman analysis. Qualitatively, the estimated maps appear to generally provide a similar, yet more blurred MWF contrast in comparison with the reference, with the cGAN model best capturing MWF variabilities in small structures. By estimating the average adjusted coefficient of determination of the GLM with quadratic regressors, we showed that 87% of the variability in the MWF values can be explained by relaxation times alone. Further quantitative analysis showed an average RMSE smaller than 0.1% for all methods. The ICC was greater than 0.81 for all methods, and the bias smaller than 2.19%. It was concluded that this work confirms the notion that relaxometry parameters contain a large part of the information on myelin water and that MWF maps can be generated from T1 /T2 data with minimal error. Among the investigated modeling approaches, the cGAN provided maps with the best trade-off between accuracy and blurriness. Fast relaxometry, like the 6 min 24 s whole-brain protocol used in this work in conjunction with machine learning, may thus have the potential to replace time-consuming MESE acquisitions.

Three-Dimensional Magnetic Resonance Fingerprinting in Neonates: Quantifying Regional Difference and Maturation in the Brain.

Magnetic resonance fingerprinting (MRF) allows the simultaneous measurement of multiple tissue properties in a single acquisition. Three-dimensional (3D) MRF with high spatial resolution can be used for neonatal brain imaging. The aim of this study is to apply 3D MRF to neonates and show regional differences and maturation in the brain. In this prospective study, 3D MRF using hybrid radial-interleaved acquisition was performed on phantoms and neonates from December 2019 to October 2020. For the reconstruction of 3D MRF, singular value decomposition was applied to reduce reconstruction time, and the iterative reconstruction technique was applied to improve image quality. The accuracies of T1 and T2 values derived from 3D MRF were evaluated in a phantom experiment. Regional T1 and T2 values were obtained from neonates' brain T1 and T2 maps derived from 3D MRF. Regional T1 and T2 values were compared, and their changes according to corrected gestational age were evaluated. The acquisition time for 3D MRF with a spatial resolution of 0.7 × 0.7 × 2 mm3 was less than 5 minutes. The phantom study showed high correlation between T1 and T2 values derived from 3D MRF and those from conventional spin echo sequences (T1, R2 = 0.998, P < 0.001; T2, R2 = 0.998, P < 0.001). Three-dimensional MRF was performed in 25 neonates (15 boys, 10 girls; median corrected gestational age, 263 days; interquartile range, 10 days). In neonates, T1 and T2 values differed in the frontal (median [interquartile range], 2785 [2684-2888] milliseconds and 189.8 [176.7-222.9] milliseconds), parietal (2849 [2741-2950] milliseconds and 191.6 [167.5-232.9] milliseconds), and occipital white matter (2621 [2513-2722] milliseconds and 162.9 [143.5-186.1] milliseconds), showing lower values in occipital white matter (P < 0.001). Regional T1 values showed a negative relationship with corrected gestational age (coefficient, -0.775 to -0.480; P < 0.05). Fast and high spatial resolution 3D MRF was applied to neonates. T1 and T2 maps derived from 3D MRF enabled the quantification of regional differences and maturation in the neonatal brain.

Quantitative measurement of macromolecular tissue properties in white and gray matter in healthy aging and amnestic MCI

Healthy and pathological aging influence brain microstructure via complex processes. Discerning these processes requires measurements that are sensitive to specific biological properties of brain tissue. We integrated a novel quantitative R1 measure with multi-shell diffusion weighted imaging to map age-associated changes in macromolecular tissue volume (MTV) along major white matter tracts in healthy older adults and patients with amnestic Mild Cognitive Impairment (aMCI). Reduced MTV in association tracts was associated with older age in healthy aging, was correlated with memory performance, and distinguished aMCI from controls. We also mapped changes in gray matter tissue properties using quantitative R1 measurements. We documented a widespread decrease in R1 with advancing age across the cortex and decreased R1 in aMCI compared with controls in regions implicated in episodic memory. Our data are the first to characterize MTV loss along major white matter tracts in aMCI and suggest that qMRI is a sensitive measure for detecting subtle degeneration of white and gray matter tissue that cannot be detected by conventional MRI and diffusion measures.

Identification of Brain Damage after Seizures Using an MR-Based Electrical Conductivity Imaging Method.

Previous imaging studies have shown the morphological malformation and the alterations of ionic mobility, water contents, electrical properties, or metabolites in seizure brains. Magnetic resonance electrical properties tomography (MREPT) is a recently developed technique for the measurement of electrical tissue properties with a high frequency that provides cellular information regardless of the cell membrane. In this study, we examined the possibility of MREPT as an applicable technique to detect seizure-induced functional changes in the brain of rats. Ultra-high field (9.4 T) magnetic resonance imaging (MRI) was performed, 2 h, 2 days, and 1 week after the injection of N-methyl-D-aspartate (NMDA; 75 mg/kg). The conductivity images were reconstructed from B1 phase images using a magnetic resonance conductivity imaging (MRCI) toolbox. The high-frequency conductivity was significantly decreased in the hippocampus among various brain regions of NMDA-treated rats. Nissl staining showed shrunken cell bodies and condensed cytoplasm potently at 2 h after NMDA treatment, and neuronal cell loss at all time points in the hippocampus. These results suggest that the reduced electrical conductivity may be associated with seizure-induced neuronal loss in the hippocampus. Magnetic resonance (MR)-based electrical conductivity imaging may be an applicable technique to non-invasively identify brain damage after a seizure.

Focused ultrasound ablation of solid tumors: Feasibility of planning tissue-selective treatments.

e15600 Background: Focused ultrasound (FUS) is a noninvasive, nonionizing, repeatable local ablative therapy that induces mechanical fractionation or thermal necrosis of a variety of solid tumors including hepatocellular carcinoma, prostate cancer, and desmoid fibromatosis. Recent feasibility studies in animal models have demonstrated the possibility of designing focused ultrasound treatments that are selective (e.g. spare healthy tissue, nerves, and blood vessels) due to differences in tissue and tumor mechanical properties. Given wide variation in individual tumor and patient characteristics, mechanics-based predictions of ablation zone features in different tissues under a range of FUS device settings are needed to permit personalized treatment planning. Methods: A finite difference computational method is used to simulate FUS ablation of tissues with variable mechanical properties (shear moduli of 0.6 – 200 kPa) under different FUS sonication parameters (frequency and peak pressure). The model calculates strain fields contributing to tissue ablation in FUS treatments which are used to predict ablation zone radii and boundary characteristics. Simulation predictions in model tissues are then compared to histology obtained from FUS-treated porcine tissue samples with similar mechanical properties. Results: The mechanical properties of model tissues and FUS treatment parameters have distinct effects on predicted minimum ablation zone radii. For example, smaller ablation zone radii are achieved in stiffer vessel wall than liver under given FUS sonication parameters. In each tissue, lower frequency and higher peak pressure FUS sonication predict a larger ablation zone. Combined variation of sonication frequency and peak pressure are found to achieve wider variation in ablation zone radius than previously achieved with frequency variation alone. Predicted ablation zone radii and boundary characteristics are consistent with the observed histology of FUS-treated tissues. Conclusions: Results show that simulations accounting for tissue mechanical properties and device settings can predict tissue selectivity and ablation zone characteristics observed in FUS procedures. This study demonstrates the potential of using noninvasive measurements of tissue and tumor properties obtained, for example, via shear wave elastography, in combination with micromechanical tissue ablation simulations to develop personalized, selective focused ultrasound treatments for solid tumors.

Apparatus and Method for Rapid Detection of Acoustic Anisotropy in Cartilage.

Articular cartilage is known to be mechanically anisotropic. In this paper, the acoustic anisotropy of bovine articular cartilage and the effects of freeze-thaw cycling on acoustic anisotropy were investigated. We developed apparatus and methods that use a magnetic L-shaped sample holder, which allowed minimal handling of a tissue, reduced the number of measurements compared to previous studies, and produced highly reproducible results. SOS was greater in the direction perpendicular to the articular surface compared to the direction parallel to the articular surface (N=17, P = 0.00001). Average SOS was 1,758 ± 107 m/s perpendicular to the surface, and 1,617 ± 55 m/s parallel to it. The average percentage difference in SOS between the perpendicular and parallel directions was 8.2% (95% CI: 5.4% to 11%). Freeze-thaw cycling did not have a significant effect on SOS (P>0.4). Acoustic measurement of tissue properties is particularly attractive for work in our laboratory since it has the potential for nondestructive characterization of the properties of developing engineered cartilage. Our approach allowed us to observe acoustic anisotropy of articular cartilage rapidly and reproducibly. This property was not significantly affected by freeze-thawing of the tissue samples, making cryopreservation practical for these assays.

Emerging quantitative MR imaging biomarkers in inflammatory arthritides

PurposeTo review quantitative magnetic resonance imaging (qMRI) methods for imaging inflammation in connective tissues and the skeleton in inflammatory arthritis. This review is designed for a broad audience including radiologists, imaging technologists, rheumatologists and other healthcare professionals. MethodsWe discuss the use of qMRI for imaging skeletal inflammation from both technical and clinical perspectives. We consider how qMRI can be targeted to specific aspects of the pathological process in synovium, cartilage, bone, tendons and entheses. Evidence for the various techniques from studies of both adults and children with inflammatory arthritis is reviewed and critically appraised. ResultsqMRI has the potential to objectively identify, characterize and quantify inflammation of the connective tissues and skeleton in both adult and pediatric patients. Measurements of tissue properties derived using qMRI methods can serve as imaging biomarkers, which are potentially more reproducible and informative than conventional MRI methods. Several qMRI methods are nearing transition into clinical practice and may inform diagnosis and treatment decisions, with the potential to improve patient outcomes. ConclusionsqMRI enables specific assessment of inflammation in synovium, cartilage, bone, tendons and entheses, and can facilitate a more consistent, personalized approach to diagnosis, characterisation and monitoring of disease.

Diffusivity and quantitative T1 profile of human visual white matter tracts after retinal ganglion cell damage.

In patients with retinal ganglion cell diseases, recent diffusion tensor imaging (DTI) studies have revealed structural abnormalities in visual white matter tracts such as the optic tract, and optic radiation. However, the microstructural origin of these diffusivity changes is unknown as DTI metrics involve multiple biological factors and do not correlate directly with specific microstructural properties. In contrast, recent quantitative T1 (qT1) mapping methods provide tissue property measurements relatively specific to myelin volume fractions in white matter. This study aims to improve our understanding of microstructural changes in visual white matter tracts following retinal ganglion cell damage in Leber's hereditary optic neuropathy (LHON) patients by combining DTI and qT1 measurements. We collected these measurements from seven LHON patients and twenty age-matched control subjects. For all individuals, we identified the optic tract and the optic radiation using probabilistic tractography, and evaluated diffusivity and qT1 profiles along them. Both diffusivity and qT1 measurements in the optic tract differed significantly between LHON patients and controls. In the optic radiation, these changes were observed in diffusivity but were not evident in qT1 measurements. This suggests that myelin loss may not explain trans-synaptic diffusivity changes in the optic radiation as a consequence of retinal ganglion cell disease.

Synthetic Brain MRI: Review of Current Concepts and Future Directions.

Synthetic magnetic resonance imaging is a novel imaging technique that allows generating multiple contrast-weighted images based on relaxivity measurements of tissue properties in a single acquisition using a multiecho, multidelay saturation recovery spin-echo sequence. The synthetic images can be generated postacquisition from the parametric tissue maps, which can be beneficial to reduce scan time and improve patient throughput. Based on relaxometry maps, synthetic magnetic resonance imaging can also perform brain tissue segmentation and myelin quantification without additional scan time. The quantitative analysis may have implications for understanding and monitoring of the evolution of the maturation process. Similarly, the myelination process is vitally important to central nervous system functioning. Measuring myelin volume could provide relevant information for the diagnosis and treatment of patients with myelination disorders.

Deep Learning for Fast and Spatially-Constrained Tissue Quantification from Highly-Undersampled Data in Magnetic Resonance Fingerprinting (MRF).

Magnetic resonance fingerprinting (MRF) is a novel quantitative imaging technique that allows simultaneous measurements of multiple important tissue properties in human body, e.g., T1 and T2 relaxation times. While MRF has demonstrated better scan efficiency as compared to conventional quantitative imaging techniques, further acceleration is desired, especially for certain subjects such as infants and young children. However, the conventional MRF framework only uses a simple template matching algorithm to quantify tissue properties, without considering the underlying spatial association among pixels in MRF signals. In this work, we aim to accelerate MRF acquisition by developing a new post-processing method that allows accurate quantification of tissue properties with fewer sampling data. Moreover, to improve the accuracy in quantification, the MRF signals from multiple surrounding pixels are used together to better estimate tissue properties at the central target pixel, which was simply done with the signal only from the target pixel in the original template matching method. In particular, a deep learning model, i.e., U-Net, is used to learn the mapping from the MRF signal evolutions to the tissue property map. To further reduce the network size of U-Net, principal component analysis (PCA) is used to reduce the dimensionality of the input signals. Based on in vivo brain data, our method can achieve accurate quantification for both T1 and T2 by using only 25% time points, which are four times of acceleration in data acquisition compared to the original template matching method.

Compression sonography for non-invasive measurement of lower leg compartment pressure in an animal model

IntroductionCompression ultrasound is a non-invasive technique allowing for qualitative visualization and quantitative measurements of mechanical tissue properties. In acute compartment syndrome (ACS), cadaver studies have proven that the intra-compartmental pressure (ICP) measured by compression sonography correlates with the ICP measured invasively. This study aimed to evaluate compression sonography for compartment pressure measurements in an animal model. Material and methodsThe pressure in the anterior tibial compartment of 6 domestic pig legs was increased from baseline to 40mmHg in 5mmHg steps. Using compression sonography, the compartment diameter was measured without external pressure and during manual application of five levels of external pressure. The elasticity ratio (ER) was computed as the ratio of the compartment diameter with and without external pressure.At 40mmHg of external pressure the ERs at different ICP levels were compared using repeated ANOVA measurements. Post-hoc comparisons evaluated the lowest detectable ICP fulfilling the definition of ACS (ICP≥30mmHg) by starting from each pressure below 30mmHg (baseline, 20mmHg and 25mmHg, respectively). Receiver operator characteristic analyses defined ER limits with appropriate sensitivity and specificity to detect ACS. ResultsThe ER increased from 79.0% at baseline ICP to 89.3% at 40mmHg ICP. The ER at baseline and at 20mmHg ICP significantly differed from the ER at 30mmHg ICP (p=0.007 and 0.002, respectively); the ER at 25mmHg ICP significantly differed from the ER at 40mmHg ICP (p=0.001).An ER less than 87.1% had a sensitivity of 94.4% and a specificity of 88.9% to proper diagnosis of ACS. ConclusionCompression sonography might offer a non-invasive technique to guide treatment in cases of uncertain acute compartment syndrome. Further studies are needed to collect elasticity ratio data in humans and to clinically validate compression sonography for compartment pressure measurements.

Multifunctional OCT for preclincial imaging

SummaryFunctional extensions of optical coherence tomography (OCT) expand image contrast and enable quantitative measurements of optical tissue properties, improved segmentation of tissue morphology and the assessment of physiological parameters. In this presentation, we will discuss applications of multifunctional OCT including high‐resolution polarization‐sensitive OCT and OCT angiography for preclinical studies of ocular disease models.

Quantitative tissue parameters of Achilles tendon and plantar fascia in healthy subjects using a handheld myotonometer

ObjectiveThe aim of the study was to examine the quantitative tissue properties of the Achilles tendon and plantar fascia using a handheld, non-invasive MyotonPRO device, in order to generate normal values and examine the biomechanical relationship of both structures. DesignProspective study of a large, healthy sample population. ParticipantsThe study sample included 207 healthy subjects (87 males and 120 females) for the Achilles tendon and 176 healthy subjects (73 males and 103 females) for the plantar fascia. For the correlations of the tissue parameters of the Achilles tendon and plantar fascia an intersection of both groups was formed which included 150 healthy subjects (65 males and 85 females). InterventionsAll participants were measured in a prone position. Consecutive measurements of the Achilles tendon and plantar fascia were performed by MyotonPRO device at defined sites. ResultsFor the left and right Achilles tendons and plantar fasciae all five MyotonPRO parameters (Frequency [Hz], Decrement, Stiffness [N/m], Creep and Relaxation Time [ms]) were calculated of healthy males and females. The correlation of the tissue parameters of the Achilles tendon and plantar fascia showed a significant positive correlation of all parameters on the left as well as on the right side. ConclusionsThe MyotonPRO is a feasible device for easy measurement of passive tissue properties of the Achilles tendon and plantar fascia in a clinical setting. The generated normal values of the Achilles tendon and plantar fascia are important for detecting abnormalities in patients with Achilles tendinopathy or plantar fasciitis in the future. Biomechanically, both structures are positively correlated. This may provide new aspects in the diagnostics and therapy of plantar fasciitis and Achilles tendinopathy.

Individual differences in human brain development.

This article discusses recent scientific advances in the study of individual differences in human brain development. Focusing on structural neuroimaging measures of brain morphology and tissue properties, two kinds of variability are related and explored: differences across individuals of the same age and differences across age as a result of development. A recent multidimensional modeling study is explained, which was able to use brain measures to predict an individual's chronological age within about one year on average, in children, adolescents, and young adults between 3 and 20 years old. These findings reveal great regularity in the sequence of the aggregate brain state across different ages and phases of development, despite the pronounced individual differences people show on any single brain measure at any given age. Future research is suggested, incorporating additional measures of brain activity and function. WIREs Cogn Sci 2017, 8:e1389. doi: 10.1002/wcs.1389For further resources related to this article, please visit the WIREs website.

Uncertainty Analysis of the Core Body Temperature Under Thermal and Physical Stress Using a Three-Dimensional Whole Body Model.

Heat stress experienced by firefighters is a common consequence of extreme firefighting activity. In order to avoid the adverse health conditions due to uncompensable heat stress, the prediction and monitoring of the thermal response of firefighters is critical. Tissue properties, among other parameters, are known to vary between individuals and influence the prediction of thermal response. Further, measurement of tissue properties of each firefighter is not practical. Therefore, in this study, we developed a whole body computational model to evaluate the effect of variability (uncertainty) in tissue parameters on the thermal response of a firefighter during firefighting. Modifications were made to an existing human whole body computational model, developed in our lab, for conducting transient thermal analysis for a firefighting scenario. In conjunction with nominal (baseline) tissue parameters obtained from literature, and physiologic conditions from a firefighting drill, the Pennes bioheat and energy balance equations were solved to obtain the core body temperature of a firefighter. Subsequently, the uncertainty in core body temperature due to variability in the tissue parameters (input parameters), metabolic rate, specific heat, density, and thermal conductivity was computed using the sensitivity coefficient method. On comparing the individual effect of tissue parameters on the uncertainty in core body temperature, the metabolic rate had the highest contribution (within ±0.20°C) followed by specific heat (within ±0.10°C), density (within ±0.07°C), and finally thermal conductivity (within ±0.01 °C). A maximum overall uncertainty of ±0.23 °C in the core body temperature was observed due to the combined uncertainty in the tissue parameters. Thus, the model results can be used to effectively predict a realistic range of thermal response of the firefighters during firefighting or similar activities.

Robust Tracking of Small Displacements With a Bayesian Estimator.

Radiation-force-based elasticity imaging describes a group of techniques that use acoustic radiation force (ARF) to displace tissue to obtain qualitative or quantitative measurements of tissue properties. Because ARF-induced displacements are on the order of micrometers, tracking these displacements in vivo can be challenging. Previously, it has been shown that Bayesian-based estimation can overcome some of the limitations of a traditional displacement estimator such as normalized cross-correlation (NCC). In this work, we describe a Bayesian framework that combines a generalized Gaussian-Markov random field (GGMRF) prior with an automated method for selecting the prior's width. We then evaluate its performance in the context of tracking the micrometer-order displacements encountered in an ARF-based method such as ARF impulse (ARFI) imaging. The results show that bias, variance, and mean-square error (MSE) performance vary with prior shape and width, and that an almost one order-of-magnitude reduction in MSE can be achieved by the estimator at the automatically selected prior width. Lesion simulations show that the proposed estimator has a higher contrast-to-noise ratio but lower contrast than NCC, median-filtered NCC, and the previous Bayesian estimator, with a non-Gaussian prior shape having better lesion-edge resolution than a Gaussian prior. In vivo results from a cardiac, radio-frequency ablation ARFI imaging dataset show quantitative improvements in lesion contrast-to-noise ratio over NCC as well as the previous Bayesian estimator.