Bone attenuation remains a methodological challenge in hybrid PET/MR, as bone is hard to visualize via magnetic resonance imaging (MRI). Therefore, novel methods for taking into account bone ...attenuation in MR-based attenuation correction (MRAC) are needed. In this study, we propose a tissue-probability based attenuation correction (TPB-AC), which employs the commonly available neurological toolbox SPM8, to derive a subject-specific μ-map by segmentation of T1-weighted MR images. The procedures to derive a μ-map representing soft tissue, air and bone from the New Segment function in SPM8 and MATLAB are described. Visual and quantitative comparisons against CT-based attenuation correction (CTAC) data were performed using two μ-values ( 0.135 cm -1 and 0.145 cm -1 ) for bone. Results show improvement of visual quality and quantitative accuracy of positron emission tomography (PET) images when TPB-AC μ-map is used in PET/MR image reconstruction. Underestimation in PET images was decreased by an average of 5 ±2 percent in the whole brain across all patients. In addition, the method performed well when compared to CTAC, with maximum differences (mean ± standard deviation) of - 3 ±2 percent and 2 ±4 percent in two regions out of 28. Finally, the method is simple and computationally efficient, offering a promising platform for further development. Therefore, a subject-specific MR-based μ-map can be derived only from the tissue probability maps from the New Segment function of SPM8.
Attenuation correction (AC) is required for accurate quantification of the reconstructed activity distribution in positron emission tomography (PET). For simultaneous PET/magnetic resonance (MR), ...however, AC is challenging, since the MR images do not provide direct information on the attenuating properties of the underlying tissue. Standard MR-based AC does not account for the presence of bone and thus leads to an underestimation of the activity distribution. To improve quantification for non-time-of-flight PET/MR, we propose an algorithm which simultaneously reconstructs activity and attenuation distribution from the PET emission data using available MR images as anatomical prior information. The MR information is used to derive voxel-dependent expectations on the attenuation coefficients. The expectations are modeled using Gaussian-like probability functions. An iterative reconstruction scheme incorporating the prior information on the attenuation coefficients is used to update attenuation and activity distribution in an alternating manner. We tested and evaluated the proposed algorithm for simulated 3D PET data of the head and the pelvis region. Activity deviations were below 5% in soft tissue and lesions compared to the ground truth whereas standard MR-based AC resulted in activity underestimation values of up to 12%.
Functional imaging has successfully been applied to capture functional changes in the pathological tissues of a body in recent years. Nuclear medicine functional imaging has been used to acquire ...information about areas of concerns (e.g. lesions and organs) in a non-invasive manner, enabling semi-automated or automated decision-making for disease diagnosis, treatment, evaluation, and prediction. Focusing on functional nuclear medicine images, in this study, the authors review existing work on the classification of single-photon emission computed tomography, positron emission tomography, and their hybrid modalities with computed tomography and magnetic resonance imaging images by using convolutional neural network (CNN) techniques. Specifically, they first present an overview of nuclear imaging and the CNN technique, such as nuclear imaging modalities, nuclear image data format, CNN architecture, and the main CNN classification models. According to the diseases of concern, they then classify the existing CNN-based work on the classification of functional nuclear images into three different categories. For the typical work in each of these categories, they present details about their research objectives, adopted CNN models, and achieved main results. Finally, they discuss research challenges and directions for developing technological solutions to classify nuclear medicine images based on the CNN technique.
What-You-Prescribe-Is-What-You-Get (WYPIWYG) procedures are a novel and general phenomenological approach to modelling the behavior of soft materials, applicable to biological tissues in particular. ...For the hyperelastic case, these procedures solve numerically the nonlinear elastic material determination problem. In this paper we show that they can be applied to determine the stored energy density of superficial fascia. In contrast to the usual approach, in such determination no user-prescribed material parameters and no optimization algorithms are employed. The strain energy densities are computed solving the equilibrium equations of the set of experiments. For the case of superficial fascia it is shown that the mechanical behavior derived from such strain energies is capable of reproducing simultaneously the measured load-displacement curves of three experiments to a high accuracy.
Purpose:
The quantification of body fat plays an important role in the study of numerous diseases. It is common current practice to use the fat area at a single abdominal computed tomography (CT) ...slice as a marker of the body fat content in studying various disease processes. This paper sets out to answer three questions related to this issue which have not been addressed in the literature. At what single anatomic slice location do the areas of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) estimated from the slice correlate maximally with the corresponding fat volume measures? How does one ensure that the slices used for correlation calculation from different subjects are at the same anatomic location? Are there combinations of multiple slices (not necessarily contiguous) whose area sum correlates better with volume than does single slice area with volume?
Methods:
The authors propose a novel strategy for mapping slice locations to a standardized anatomic space so that same anatomic slice locations are identified in different subjects. The authors then study the volume-to-area correlations and determine where they become maximal. To address the third issue, the authors carry out similar correlation studies by utilizing two and three slices for calculating area sum.
Results:
Based on 50 abdominal CT data sets, the proposed mapping achieves significantly improved consistency of anatomic localization compared to current practice. Maximum correlations are achieved at different anatomic locations for SAT and VAT which are both different from the L4-L5 junction commonly utilized currently for single slice area estimation as a marker.
Conclusions:
The maximum area-to-volume correlation achieved is quite high, suggesting that it may be reasonable to estimate body fat by measuring the area of fat from a single anatomic slice at the site of maximum correlation and use this as a marker. The site of maximum correlation is not at L4-L5 as commonly assumed, but is more superiorly located at T12-L1 for SAT and at L3-L4 for VAT. Furthermore, the optimal anatomic locations for SAT and VAT estimation are not the same, contrary to common assumption. The proposed standardized space mapping achieves high consistency of anatomic localization by accurately managing nonlinearities in the relationships among landmarks. Multiple slices achieve greater improvement in correlation for VAT than for SAT. The optimal locations in the case of multiple slices are not contiguous.
An in situ needle manipulation technique used by physicians when performing spinal injections is modeled to study its effect on needle shape and needle tip position. A mechanics-based model is ...proposed and solved using finite element method. A test setup is presented to mimic the needle manipulation motion. Tissue phantoms made from plastisol as well as porcine skeletal muscle samples are used to evaluate the model accuracy against medical images. The effect of different compression models as well as model parameters on model accuracy is studied, and the effect of needle-tissue interaction on the needle remote center of motion is examined. With the correct combination of compression model and model parameters, the model simulation is able to predict needle tip position within submillimeter accuracy.
Accurate knowledge of the dielectric properties of biological tissues is fundamental for the design of electromagnetic medical devices. The open-ended coaxial probe technique is commonly used to ...measure the complex permittivity of tissues at microwave frequencies. Since the open-ended coaxial probe technique provides an average complex permittivity for the tissues within its sensing volume, when measuring heterogeneous tissues, a post-measurement histological analysis is often conducted to accurately associate the measured dielectric properties to the tissue content. Subsequently, it is crucial to quantify the sensing volume of the probe accurately. Sensing volume is generally defined as a cylindrical volume (rectangular cross-section) based on sensing depth and sensing radius. In this article, the sensing volume is modified based on the field distribution of the probe. Since in practice defining the sensing depth and sensing radius for a given probe is straightforward, the resulting sensing volume is defined as an ellipsoid based on sensing depth and sensing radius values. Considering a finite heterogeneity in a homogeneous background, it is shown that the ellipsoidal sensing volume is a more accurate representation than the typical cylindrical volume for open-ended coaxial probes, and therefore, results in a more accurate dielectric characterization of heterogeneous tissues.
In this paper, a new type of surgical device for the coagulation and scission of biological tissues, such as blood vessels, using microwave energy is proposed. In addition, the effectiveness of the ...device for tissue coagulation is evaluated using a numerical analysis and an ex vivo experiment with swine liver tissue. In the numerical analysis, the coagulating capability of the device is evaluated by calculating the temperature distribution inside of the tissue grasped with the device. Microwave energy absorbed by the grasped tissue is used as a heat source for the bioheat transfer equation. In the ex vivo experiment, a prototype of the proposed device is used. The results of these investigations indicate that the proposed device has sufficient coagulating capability of the biological tissue. Moreover, for evaluating the effectiveness of the proposed device in a more realistic condition, an in vivo experiment with the swine tissue has been conducted. In this experiment, venous hemorrhage at the surgical site is stopped using the prototype. As a result, the effectiveness of the proposed device has been confirmed by a surgeon.
Perfusion magnetic resonance (MR) images are often used in the assessment of acute ischemic stroke to distinguish between salvageable tissue and infarcted core. Deconvolution methods such as singular ...value decomposition have been used to approximate model-based perfusion parameters from these images. However, studies have shown that these existing deconvolution algorithms can introduce distortions that may negatively influence the utility of these parameter maps. There is limited previous work on utilizing machine learning algorithms to estimate perfusion parameters. In this work, we present a novel bi-input convolutional neural network (bi-CNN) to approximate four perfusion parameters without using an explicit deconvolution method. These bi-CNNs produced good approximations for all four parameters, with relative average root-mean-square errors (ARMSEs) ≤ 5% of the maximum values. We further demonstrate the utility of the estimated perfusion maps for quantifying the salvageable tissue volume in stroke, with more than 80% agreement with the ground truth. These results show that deep learning techniques are a promising tool for perfusion parameter estimation without requiring a standard deconvolution process.
Purpose:
Energy discriminating, photon-counting detectors (PCDs) are an emerging technology for computed tomography (CT) with various potential benefits for clinical CT. The photon energies measured ...by PCDs can be distorted due to the interactions of a photon with the detector and the interaction of multiple coincident photons. These effects result in distorted recorded x-ray spectra which may lead to artifacts in reconstructed CT images and inaccuracies in tissue identification. Model-based compensation techniques have the potential to account for the distortion effects. This approach requires only a small number of parameters and is applicable to a wide range of spectra and count rates, but it needs an accurate model of the spectral distortions occurring in PCDs. The purpose of this study was to develop a model of those spectral distortions and to evaluate the model using a PCD (model DXMCT-1; DxRay, Inc., Northridge, CA) and various x-ray spectra in a wide range of count rates.
Methods:
The authors hypothesize that the complex phenomena of spectral distortions can be modeled by: (1) separating them into count-rate independent factors that we call the spectral response effects (SRE), and count-rate dependent factors that we call the pulse pileup effects (PPE), (2) developing separate models for SRE and PPE, and (3) cascading the SRE and PPE models into a combined SRE+PPE model that describes PCD distortions at both low and high count rates. The SRE model describes the probability distribution of the recorded spectrum, with a photo peak and a continuum tail, given the incident photon energy. Model parameters were obtained from calibration measurements with three radioisotopes and then interpolated linearly for other energies. The PPE model used was developed in the authors’ previous work K. Taguchi
et al.
, “Modeling the performance of a photon counting x-ray detector for CT: Energy response and pulse pileup effects,” Med. Phys.
38(2), 1089–1102 (2011). The agreement between the x-ray spectra calculated by the cascaded SRE+PPE model and the measured spectra was evaluated for various levels of deadtime loss ratios (DLR) and incident spectral shapes, realized using different attenuators, in terms of the weighted coefficient of variation (COVW), i.e., the root mean square difference weighted by the statistical errors of the data and divided by the mean.
Results:
At low count rates, when DLR < 10%, the distorted spectra measured by the DXMCT-1 were in agreement with those calculated by SRE only, with COVW's less than 4%. At higher count rates, the measured spectra were also in agreement with the ones calculated by the cascaded SRE+PPE model; with PMMA as attenuator, COVW was 5.6% at a DLR of 22% and as small as 6.7% for a DLR as high as 55%.
Conclusions:
The x-ray spectra calculated by the proposed model agreed with the measured spectra over a wide range of count rates and spectral shapes. The SRE model predicted the distorted, recorded spectra with low count rates over various types and thicknesses of attenuators. The study also validated the hypothesis that the complex spectral distortions in a PCD can be adequately modeled by cascading the count-rate independent SRE and the count-rate dependent PPE.
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