Difference Imaging Approach Research Articles

Nonlinear Difference Imaging to Image Local Conductivity of Single-Layer Graphene Using Electrical Impedance Tomography

Single-layer graphene synthesized using the chemical vapor deposition (CVD) technique contains defects on the graphene surface that are formed during the growth and transfer process. These defects influence the graphene electrical characteristics, especially the conductivity distribution. Nondestructive characterization of graphene samples is therefore essential for developing single-layer graphene devices. Electrical impedance tomography (EIT), a low-cost nondestructive method with high temporal resolution characteristics, can be applied to obtain the local conductivity distribution. Due to the very high conductivity of the background and inaccurate knowledge of contact impedances, the conventional absolute imaging methods do not give desired conductivity reconstruction for graphene. This study uses a nonlinear difference imaging (NDI) approach to estimate the local conductivity changes across the graphene surface quantitatively. In NDI, the conductivity after the change is parameterized as a linear combination of initial and the conductivity change. The conductivity change restricted to the region of interest (ROI) is determined using the Otsu method. The NDI with Otsu method (NDIWO) estimates the initial distribution and conductivity changes simultaneously from the two datasets measured at different time intervals. NDIWO tolerates modeling errors to some extent and quantitively provides more accurate local conductivity changes. The feasibility of NDIWO is tested with numerical simulations and experiments with graphene wafer of size 2.5 cm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times2.5$ </tex-math></inline-formula> cm, and results show that the proposed method is successful in estimating the conductivity changes with better accuracy than conventional absolute and linear difference imaging.

Imaging Conductivity Changes in Monolayer Graphene Using Electrical Impedance Tomography.

Recently, graphene has gained a lot of attention in the electronic industry due to its unique properties and has paved the way for realizing novel devices in the field of electronics. For the development of new device applications, it is necessary to grow large wafer-sized monolayer graphene samples. Among the methods to synthesize large graphene films, chemical vapor deposition (CVD) is one of the promising and common techniques. However, during the growth and transfer of the CVD graphene monolayer, defects such as wrinkles, cracks, and holes appear on the graphene surface. These defects can influence the electrical properties and it is of interest to know the quality of graphene samples non-destructively. Electrical impedance tomography (EIT) can be applied as an alternate method to determine conductivity distribution non-destructively. The EIT inverse problem of reconstructing conductivity is highly non-linear and is heavily dependent on measurement accuracy and modeling errors related to an accurate knowledge of electrode location, contact resistances, the exact outer boundary of the graphene wafer, etc. In practical situations, it is difficult to eliminate these modeling errors as complete knowledge of the electrode contact impedance and outer domain boundary is not fully available, and this leads to an undesirable solution. In this paper, a difference imaging approach is proposed to estimate the conductivity change of graphene with respect to the reference distribution from the data sets collected before and after the change. The estimated conductivity change can be used to locate the defects on the graphene surface caused due to the CVD transfer process or environment interaction. Numerical and experimental results with graphene sample of size 2.5 × 2.5 cm are performed to determine the change in conductivity distribution and the results show that the proposed difference imaging approach handles the modeling errors and estimates the conductivity distribution with good accuracy.

Improving EBSD precision by orientation refinement with full pattern matching.

We present a comparison of the precision of different approaches for orientation imaging using electron backscatter diffraction (EBSD) in the scanning electron microscope. We have used EBSD to image the internal structure of WC grains, which contain features due to dislocations and subgrains. We compare the conventional, Hough-transform based orientation results from the EBSD system software with results of a high-precision orientation refinement using simulated pattern matching at the full available detector resolution of 640 × 480 pixels. Electron channelling contrast imaging (ECCI) is used to verify the correspondence of qualitative ECCI features with the quantitative orientation data from pattern matching. For the investigated sample, this leads to an estimated pattern matching sensitivity of about 0.5 mrad (0.03°) and a spatial feature resolution of about 100 nm. In order to investigate the alternative approach of postprocessing noisy orientation data, we analyse the effects of two different types of orientation filters. Using reference features in the high-precision pattern matching results for comparison, we find that denoising of orientation data can reduce the spatial resolution, and can lead to the creation of orientation artefacts for crystallographic features near the spatial and orientational resolution limits of EBSD.

Mapping of language and motor function during awake neurosurgery with intraoperative optical imaging.

Intraoperative optical imaging (IOI) is a marker-free, contactless, and noninvasive imaging technique that is able to visualize metabolic changes of the brain surface following neuronal activation. Although it has been used in the past mainly for the identification of functional brain areas under general anesthesia, the authors investigated the potential of the method during awake surgery. Measurements were performed in 10 patients who underwent resection of lesions within or adjacent to cortical language or motor sites. IOI was applied in 3 different scenarios: identification of motor areas by using finger-tapping tasks, identification of language areas by using speech tasks (overt and silent speech), and a novel approach-the application of IOI as a feedback tool during direct electrical stimulation (DES) mapping of language. The functional maps, which were calculated from the IOI data (activity maps), were qualitatively compared with the functional MRI (fMRI) and the electrophysiological testing results during the surgical procedure to assess their potential benefit for surgical decision-making.The results reveal that the intraoperative identification of motor sites with IOI in good agreement with the preoperatively acquired fMRI and the intraoperative electrophysiological measurements is possible. Because IOI provides spatially highly resolved maps with minimal additional hardware effort, the application of the technique for motor site identification seems to be beneficial in awake procedures. The identification of language processing sites with IOI was also possible, but in the majority of cases significant differences between fMRI, IOI, and DES were visible, and therefore according to the authors' findings the IOI results are too unspecific to be useful for intraoperative decision-making with respect to exact language localization. For this purpose, DES mapping will remain the method of choice.Nevertheless, the IOI technique can provide additional value during the language mapping procedure with DES. Using a simple difference imaging approach, the authors were able to visualize and calculate the spatial extent of activation for each stimulation. This might enable surgeons in the future to optimize the mapping process. Additionally, differences between tumor and nontumor stimulation sites were observed with respect to the spatial extent of the changes in cortical optical properties. These findings provide further evidence that the method allows the assessment of the functional state of neurovascular coupling and is therefore suited for the delineation of pathologically altered tissue.

A Novel Approach for Breast Cancer Investigation and Recognition Using M-Level Set-Based Optimization Functions

Breast cancer identification is the first and foremost step in the journey of proper diagnosis and treatment of this disease. Therefore, many medical examinations and applications are devised and used including different approaches for breast imaging. For medical image analysis, nonparametric algorithm has been used. This work focuses on improving breast cancer recognition in mammogram images using a nonparametric approach based on image pixel intensities (IBCNP). A nonparametric approach is a new expression to deal with uncertainty. In this paper, the nonparametric approach is used to solve the uncertainty in breast cancer detection; uncertainty means no one knows for sure how to detect breast cancer without imaging, which creates an uncertainty for the oncologists. This paper investigates the feasibility of applying the nonparametric approach in mammography (x-ray breast imaging) to detect and hence diagnose and treat breast cancer. Analysing breast images is a very complicated process and requires distinguishing normal and malignant pixel intensities. Maximizing the shared data between image pixel intensities and the desired areas (infected region) is defined as the identification problem, subject to a constraint on the total size of the desired area borders. As part of this research, a comparative study was conducted on the proposed algorithm, Breast Cancer Cells Infestation based on a set of partial differential equations (PDEs), Incoherent Motion in Breast Cancer (IMBC), Global Dynamics of a Breast Cancer Competition Model using five simple differential equations (SDFs), and Estimation of Intensity Using Nonparametric Method (EINP) techniques. These five techniques of medical image segmentation are applied to 156 different mammogram images to obtain the exact measurement of the efficiency of the identification process. This process uses multiple performance metrics such as the overall mammogram image quality (Q), the dice similarity coefficient (DSC), Peak signal-to-noise ratio (PSNR) metric, Haussdorff Distance (HD), and probability of tumor detection (PTD). This paper assumes lack of knowledge of the probability densities in relation to pixel intensities of an image within a given area. It proposes an estimation of non-parametric density that formulates the theoretical data optimization problem by applying curve evolution methods and deriving the correlated gradient flows. It uses level-set techniques to achieve the resulting evolution. The algorithm was applied to 156 sets of different mammogram images from twelve groups with both malignant and/or normal. The experimental findings showed that the proposed algorithm is 94.937% accurately effective in breast cancer detection.

A Parametric Level Set-Based Approach to Difference Imaging in Electrical Impedance Tomography.

This paper presents a novel difference imaging approach based on the recently developed parametric level set (PLS) method for estimating the change in a target conductivity from electrical impedance tomography measurements. As in conventional difference imaging, the reconstruction of conductivity change is based on data sets measured from the surface of a body before and after the change. The key feature of the proposed approach is that the conductivity change to be reconstructed is assumed to be piecewise constant, while the geometry of the anomaly is represented by a shape-based PLS function employing Gaussian radial basis functions (GRBFs). The representation of the PLS function by using GRBF provides flexibility in describing a large class of shapes with fewer unknowns. This feature is advantageous, as it may significantly reduce the overall number of unknowns, improve the condition number of the inverse problem, and enhance the computational efficiency of the technique. To evaluate the proposed PLS-based difference imaging approach, results obtained via simulation, phantom study, and in vivo pig data are studied. We find that the proposed approach tolerates more modeling errors and leads to a significant improvement in image quality compared with the conventional linear approach.

Snapshot difference imaging using correlation time-of-flight sensors

Computational photography encompasses a diversity of imaging techniques, but one of the core operations performed by many of them is to compute image differences. An intuitive approach to computing such differences is to capture several images sequentially and then process them jointly. In this paper, we introduce a snapshot difference imaging approach that is directly implemented in the sensor hardware of emerging time-of-flight cameras. With a variety of examples, we demonstrate that the proposed snapshot difference imaging technique is useful for direct-global illumination separation, for direct imaging of spatial and temporal image gradients, for direct depth edge imaging, and more.

Tackling modelling error in the application of electrical impedance tomography to the head.

In the head application of Electrical Impedance Tomography (EIT), reconstruction of voltage measurements for a conductivity distribution image using an ordinary method, the absolute imaging approach, is impossible due to the traditional ignorance of modelling error. The modelling error comes from the inaccuracy of geometry and structure, which are unable to be known accurately in practice, and are usually large in head application of EIT. Difference imaging is an alternative approach which is able to reduce the size of this error, but it introduces other kinds of error. In this work, we demonstrate that in situations like head EIT, the nonlinear difference imaging approach can reconstruct difference conductivity effectively: the reduced modelling error and the new errors arising are able to be ignored, because they are much smaller than the original modelling error. The magnitude of conductivity change in the head-like situation is also investigated, and a selection scheme for the initial guess in the reconstruction process is also proposed.

High sensitivity imaging of resin-rich regions in graphite/epoxy laminates using joint entropy

The continuing difficulty of detecting critical flaws in advanced materials requires novel approaches that enhance sensitivity to defects that might impact performance. This study compares different approaches for imaging a near-surface resin-rich defect in a thin graphite/epoxy plate using backscattered ultrasound. The specimen, having a resin-rich void immediately below the top surface ply, was scanned with a 1 in. dia., 5 MHz center frequency, and 4 in. focal length transducer. A computer controlled apparatus comprised of an x-y-z motion controller, a digitizer (LeCroy 9400A), and an ultrasonic pulser/receiver (Panametrics 5800) was used to acquire data on a 100 ×100 grid of points covering a 3 × 3 in. square. At each grid point 256 512-word, 8-bit backscattered waveforms, were digitized, signal averaged, and then stored on computer for off-line analysis. The same backscattered waveforms were used to produce peak-to-peak, signal energy, as well as entropy images. All of the entropy images exhibit better border delineation and defect contrast than the either peak-to-peak or signal energy. The best results are obtained using the joint entropy of the backscattered waveforms with a reference function. Two different references are examined: a reflection from a stainless steel reflector, and an approximate optimum obtained from an iterative parametric search. The joint entropy images produced using the optimum reference exhibit ~3 times the contrast obtained in previous studies.

An advanced phantom study assessing the feasibility of neuronal current imaging by ultra-low-field NMR

In ultra-low-field (ULF) NMR/MRI, a common scheme is to magnetize the sample by a polarizing field of up to hundreds of mT, after which the NMR signal, precessing in a field on the order of several μT, is detected with superconducting quantum interference devices (SQUIDs). In our ULF-NMR system, we polarize with up to 50mT and deploy a single-stage DC-SQUID current sensor with an integrated input coil which is connected to a wire-wound Nb gradiometer. We developed this system (white noise 0.50fT/Hz) for assessing the feasibility of imaging neuronal currents by detecting their effect on the ULF-NMR signal. Magnetoencephalography investigations of evoked brain activity showed neuronal dipole moments below 50nAm. With our instrumentation, we have studied two different approaches for neuronal current imaging. In the so-called DC effect, long-lived neuronal activity shifts the Larmor frequency of the surrounding protons. An alternative strategy is to exploit fast neuronal activity as a tipping pulse. This so-called AC effect requires the proton Larmor frequency to match the frequency of the neuronal activity, which ranges from near-DC to ∼kHz. We emulated neuronal activity by means of a single dipolar source in a physical phantom, consisting of a hollow sphere filled with an aqueous solution of CuSO4 and NaCl. In these phantom studies, with physiologically relevant dipole depths, we determined resolution limits for our set-up for the AC and the DC effect of ∼10μAm and ∼50nAm, respectively. Hence, the DC effect appears to be detectable in vivo by current ULF-NMR technology.

MDia and POTS

We describe the Munich Difference Imaging Analysis pipeline that we developed and implemented in the framework of the Astro-WISE package to automatically measure high precision light curves of a large number of stellar objects using the difference imaging approach. Combined with programs to detect time variability, this software can be used to search for planetary systems or binary stars with the transit method and for variable stars of different kinds. As a first scientific application, we discuss the data reduction and analysis performed with Astro-WISE on the pre-OmegaTranS data set, that we collected during a monitoring campaign of a dense stellar field with the Wide Field Imager at the ESO 2.2m telescope.

Imaging of integrin αvβ3 expression

The integrin alphavbeta3 plays an important role in angiogenesis and tumor metastasis. It is expressed on activated endothelial cells as well as some tumor cells. Therefore it is a promising imaging target as a potential surrogate parameter of angiogenic activity. Molecular imaging of alphavbeta3 expression could potentially facilitate response evaluation of antiangiogenic drugs (e. g. bevacizumab) or aid in selecting and monitoring patients receiving humanized monoclonal antibody therapies directed against alphavbeta3 (EMD121974). Therefore many different approaches for imaging of alphavbeta3 expression have been studied in the recent years, including positron emission tomography (PET), single photon emission tomography (SPECT), magnetic resonance imaging (MRI), optical imaging and ultrasound using targeted microbubbles. While optical imaging techniques lend themselves for preclinical studies, PET and SPECT using the alphavbeta3 specific tracers [(18)F]galacto-RGD and [(99m)Tc]NC100692 are the only techniques up to now which have been successfully used in patients. The various advantages and disadvantages of several imaging techniques will be discussed in this article.

Three-dimensional electrical impedance tomography applied to a metal-walled filtration test platform

The first true three-dimensional image reconstructions from a metal-walled vessel using electrical impedance tomography (EIT) are presented. Two image reconstruction techniques have been applied via relatively sophisticated FEM modelling of a bespoke laboratory test vessel from which data have been obtained using an EIT instrument designed to intrinsically safe requirements. A generalized Tikhonov regularization method is compared with the linear back-projection (LBP) technique. Subsequent image reconstructions strongly suggest that the LBP method when applied to a metal-walled vessel is highly sensitive to the level of detail within the FEM model. By comparison, the regularized technique is far less sensitive to the complexity of the modelled geometry. Additionally, unlike the LBP method, the regularization technique has been successful in accurately reconstructing multiple inhomogeneities within an aqueous system. A further experiment has shown similar sensitivity in a wetted powder-based system. It is concluded that EIT via a regularized difference imaging approach has significant potential for detecting 3D malformations and non-uniformities in industrial pressure filtration systems.

Computerized detection of pulmonary nodules in digital chest images: Use of morphological filters in reducing false‐positive detections

Currently, radiologists can fail to detect lung nodules in up to 30% of actually positive cases. If a computerized scheme could alert the radiologist to locations of suspected nodules, then potentially the number of missed nodules could be reduced. We are developing such a computerized scheme that involves a difference-image approach and various feature-extraction techniques. In this paper, we describe our use of digital morphological processing in the reduction of computer-identified false-positive detections. A feature-extraction technique, which includes the sequential application of nonlinear filters of erosion and dilation, is employed to reduce the camouflaging effect of ribs and vessels on nodule detection. This additional feature-extraction technique reduced the true-positive rate of the computerized scheme by 13% and the false-positive rate by 50%. In a comparison of the scheme with and without the additional feature-extraction technique, inclusion of the additional technique increased the detection sensitivity by about half at the level of three to four false-positive detections per chest image.

Pulmonary nodules: computer-aided detection in digital chest images.

Currently, radiologists fail to detect pulmonary nodules in up to 30% of cases with actually positive findings. Diagnoses may be missed due to camouflaging effects of anatomic background, subjective and varying decision criteria, or distractions in clinical situations. We developed a computerized method to detect locations of lung nodules in digital chest images. The method is based on a difference-image approach and feature-extraction techniques, including growth, slope, and profile tests. Computer results were used to alert 12 radiologists to possible nodule locations in 60 clinical cases. Preliminary results suggest that computer aid can improve the detection performance of radiologists.

Preface

The authors investigated the feasibility of using computer methods for automated detection of clustered microcalcifications on clinical mammograms. A new difference-image approach using a matched filter/box-rim filter combination effectively removed the structured background from the image. A locally adaptive gray-level thresholding technique was then used for extraction of the signals from the resulting difference image. Signal-extraction criteria based on the size, contrast, number, and clustering properties of microcalcifications were next imposed on the detected signals to distinguish true signals from noise or artifacts. The detection accuracy of the computer scheme was evaluated by means of a free response receiver operating characteristic (FROC) analysis. It was found that, for simulated subtle microcalcifications superimposed on normal mammograms, the difference-image approach with a matched filter/box-rim filter combination could yield a true-positive cluster detection rate of 80% at a false-positive detection rate of one cluster per image. In a study of 20 clinical images containing moderately subtle microcalcifications, the automated computer scheme obtained an 82% truepositive cluster detection rate at a false-positive detection rate of one cluster per image. These results indicate that the automated method has the potential to aid radiologists in screening mammograms for clustered microcalcifications.

Image feature analysis and computer-aided diagnosis in digital radiography. 3. Automated detection of nodules in peripheral lung fields.

We are investigating the characteristic features of lung nodules and the surrounding normal anatomic background in order to develop an algorithm of computer vision for use as an aid in the detection of nodules in digital chest radiographs. Our technique involves an attempt to eliminate the background anatomic structures in the lung fields by means of a difference image approach. Then, feature-extraction techniques, such as tests for circularity, size, and their variation with threshold level, are applied so that suspected nodules can be isolated. Preliminary results of this automated detection scheme yielded high true-positive rates and low false-positive rates in the peripheral lung regions of the chest. This detection scheme, which can assist the final diagnosis by the clinician, has the potential to improve the early detection of lung carcinomas.