A dual-modal approach using Raman spectroscopy and optical pH sensing was investigated to discriminate between normal and cancerous tissues. Raman spectroscopy has demonstrated the potential for in vivo cancer detection. However, Raman spectroscopy has suffered from strong fluorescence background of biological samples and subtle spectral differences between normal and disease tissues. To overcome those issues, pH sensing is adopted to Raman spectroscopy as a dual-modal approach. Based on the fact that the pH level in cancerous tissues is lower than that in normal tissues due to insufficient vasculature formation, the dual-modal approach combining the chemical information of Raman spectrum and the metabolic information of pH level can improve the specificity of cancer diagnosis. From human breast tissue samples, Raman spectra and pH levels are measured using fiber-optic-based Raman and pH probes, respectively. The pH sensing is based on the dependence of pH level on optical transmission spectrum. Multivariate statistical analysis is performed to evaluate the classification capability of the dual-modal method. The analytical results show that the dual-modal method based on Raman spectroscopy and optical pH sensing can improve the performance of cancer classification.
To discriminate between normal and cancerous tissue, a dual modal approach using Raman spectroscopy and pH sensor was designed and applied. Raman spectroscopy has demonstrated the possibility of using as diagnostic method for the early detection of precancerous and cancerous lesions in vivo. It also can be used in identifying markers associated with malignant change. However, Raman spectroscopy lacks sufficient sensitivity due to very weak Raman scattering signal or less distinctive spectral pattern. A dual modal approach could be one of the solutions to solve this issue. The level of extracellular pH in cancer tissue is lower than that in normal tissue due to increased lactic acid production, decreased interstitial fluid buffering and decreased perfusion. High sensitivity and specificity required for accurate cancer diagnosis could be achieved by combining the chemical information from Raman spectrum with metabolic information from pH level. Raman spectra were acquired by using a fiber optic Raman probe, a cooled CCD camera connected to a spectrograph and 785 nm laser source. Different transmission spectra depending on tissue pH were measured by a lossy-mode resonance sensor based on fiber optic. The discriminative capability of pH-Raman dual modal method was evaluated using principal component analysis (PCA). The obtained results showed that the pH-Raman dual modal approach can improve discriminative capability between normal and cancerous tissue, which can lead to very high sensitivity and specificity. The proposed method for cancer detection is expected to be used in endoscopic diagnosis later.
As low-dose computed tomography becomes a hot issue in the field of clinical x-ray imaging, photon counting detectors
have drawn great attention as alternative x-ray image sensors. Even though photon-counting image sensors have several
advantages over the integration-type sensors, such as low noise and high DQE, they are known to be more sensitive to
the various experimental conditions like temperature and electric drift. Particularly, time-varying detector response
during the CT scan is troublesome in photon-counting-detector-based CTs. To overcome the time-varying behavior of
the image sensor during the CT scan, we developed a flat-field correction method together with an automated scanning
mechanism. We acquired the flat-field images and projection data every view alternatively. When we took the flat-field
image, we moved down the imaging sample away from the field-of-view with aid of computer controlled linear
positioning stage. Then, we corrected the flat-field effects view-by-view with the flat-field image taken at given view.
With a CdTe photon-counting image sensor (XRI-UNO, IMATEK), we took CT images of small bugs. The CT images
reconstructed with the proposed flat-field correction method were much superior to the ones reconstructed with the
conventional flat-field correction method.
Computed Tomography (CT) using Carbon Nanotube (CNT) x-ray source is a technique of generating reconstruction
images of the structure of teeth sample. A proto type CNT x-ray CT was designed for medical imaging to examine
whether it could be used to analysis the equipment of medical and industrial application. The CNT field emitter array was grown on silicon substrate through a resist-assisted patterning (RAP) process. The field emission properties showed a gate turn-on field of 3.8 V/μm at an anode emission current of 0.5 mA. The author demonstrated the x-ray source with four electrode structures utilizing the CNT emitter. The acquisitioned images were reconstructed by filtered back projection (FBP) method.
Scan time of spectral-CTs is much longer than conventional CTs due to limited number of x-ray photons detectable by
photon-counting detectors. However, the spectral pixel information in spectral-CT has much richer information on
physiological and pathological status of the tissues than the
CT-number in conventional CT, which makes the spectral-
CT one of the promising future imaging modalities. One simple way to reduce the scan time in spectral-CT imaging is to
reduce the number of views in the acquisition of projection data. But, this may result in poorer SNR and strong streak
artifacts which can severely compromise the image quality. In this work, spectral-CT projection data were obtained from
a lab-built spectral-CT consisting of a single CdTe photon counting detector, a micro-focus x-ray tube and scan
mechanics. For the image reconstruction, we used two iterative image reconstruction methods, the simultaneous iterative
reconstruction technique (SIRT) and the total variation minimization based on conjugate gradient method (CG-TV),
along with the filtered back-projection (FBP) to compare the image quality. From the imaging of the iodine containing
phantoms, we have observed that SIRT and CG-TV are superior to the FBP method in terms of SNR and streak artifacts.
The use of flat-panel detectors (FPDs) is becoming increasingly popular in the cone beam volume and multi-slice
CT imaging. But due to the deficient semiconductor array processing, the diagnostic quality of the FPD-based CT
images in both CT systems is degraded by different types of artifacts known as the ring and radiant artifacts. Several
techniques have been already published in eliminating the stripe artifacts from the projection data of the multi-slice
CT system or in other words, from the sinogram image with a view to suppress the ring and radiant artifacts from
the 2-D reconstructed CT images. On the other hand, till now a few articles have been reported to remove the
artifacts from the cone beam CT images. In this paper, an effective approach is presented to eliminate the artifacts
from the cone beam projection data using the sinogram based stripe artifact removal methods. The improvement in
the required diagnostic quality is achieved by applying them both in horizontal and vertical sinograms constituted
sequentially from the stacked cone beam projections. Finally, some real CT images have been used to demonstrate
the effectiveness of the proposed technique in eliminating the ring and radiant artifacts from the cone beam volume
CT images. A comparative study with the conventional sinogram based approaches is also presented to see the
effectiveness of the proposed technique.
We have analyzed magnetic field perturbations caused by local magnetic susceptibility changes at a brain when the brain is immersed in the main magnetic field of an MRI system. When a local region of brain tissue is activated, the effective magnetic susceptibility of the region changes due to the blood volume and blood oxygenation level changes at the activated region. This local susceptibility change will, then, perturb the main magnetic field of the MRI system. After estimating the effective magnetic susceptibility change at an activated region, we have analyzed the perturbation field through theoretical and numerical calculations. We have found that the magnetic field perturbation due to the local susceptibility change is as big as can be measured by SQUID devices. We expect that the perturbation field can be used for brain function studies in conjunction with fMRI.
Mice have been widely used in various areas of biomedical research such as drug development and genetic engineering. To investigate drug effect or treatment efficacy in the mouse studies, in-vivo mouse imaging modalities have been recently developed. Among various kinds of imaging modalities, micro-CT is expected to be useful in many biological applications since it provides 3-dimensional high-resolution tomographic images. In this study, we have developed a high-resolution micro-CT system using a microfocus x-ray source and a flat panel detector. The flat panel detector contains a CsI scintillator plate, a 2-dimensional photodiode array, and a 12-bit ADC. The photodiode array consists of 2400 x 2400 pixels of 50μm size. In order to improve computing speed in the micro-CT, we have also developed a distributed parallel processing system using multiple computers. The computation-intensive cone-beam reconstruction process has been divided into multiple tasks in a way that each CPU takes care of similar amount of computations. We have linked four desktop computers with dual AMD Athlon processors by Ethernet and optimized the task allocations among them. The developed parallel processing system has shown to have about eight times faster reconstruction speed. It is expected that the distributed image reconstruction technique can be a low cost solution for the in-vivo mouse imaging system.
The purpose of this study is to improve noise immunity of electrical current density image reconstruction in magnetic resonance current density imaging (MRCDI) and magnetic resonance electrical impedance tomography (MREIT). In the MRCDI and MREIT techniques, electrical current densities have been calculated by taking curls to the extra magnetic fields generated by externally applied electrical currents. The extra magnetic fields are calculated from the phase information in magnetic resonance images. Both the phase calculations and curl operations are very sensitive to the noise. Since the curl operation in the space domain appears as a high pass filter in the spatial frequency domain and the current density images have very little high frequency components, we can adopt various kinds of spatial filtering techniques during the curl operation to improve the signal-to-noise ratio of the current density images. We have compared current density estimation results between the proposed and conventional methods for several kinds of current distribution patterns. For the current distributions with little high spatial frequency components, the proposed current density reconstruction method has been found to be superior to the conventional method.
KEYWORDS: Digital signal processing, Charge-coupled devices, Signal processing, Infrared imaging, Imaging systems, Phase interferometry, Sensors, Holographic interferometry, Infrared radiation, Signal to noise ratio
We have proposed a new method of image reconstruction in EIT (electrical impedance tomography). In EIT, we usually use boundary current and voltage measurements to provide the information about the spatial distribution of electrical impedance or resistivity. One of the major problems in EIT has been the inaccessibility of internal voltage or current data in finding the internal impedance values. The new method uses internal current density data measured by NMR imaging technique. By knowing the internal current density, we can improve the accuracy of the impedance images.