We developed a novel method for determining the presampling modulation transfer function (MTF) of digital radiography systems from slanted edge images based on Wiener deconvolution. The degraded supersampled edge spread function (ESF) was obtained from simulated slanted edge images with known MTF in the presence of poisson noise, and its corresponding ideal ESF without degration was constructed according to its central edge position. To meet the requirements of the absolute integrable condition of Fourier transformation, the origianl ESFs were mirrored to construct the symmetric pattern of ESFs. Then based on Wiener deconvolution technique, the supersampled line spread function (LSF) could be acquired from the symmetric pattern of degraded supersampled ESFs in the presence of ideal symmetric ESFs and system noise. The MTF is then the normalized magnitude of the Fourier transform of the LSF. The determined MTF showed a strong agreement with the theoritical true MTF when appropriated Wiener parameter was chosen. The effects of Wiener parameter value and the width of square-like wave peak in symmetric ESFs were illustrated and discussed. In conclusion, an accurate and simple method to measure the presampling MTF was established using Wiener deconvolution technique according to slanted edge images.
In vivo biomedical imaging using near-infrared light must overcome the effects of highly light scattering, which limit the
spatial resolution and affect image quality. The high-resolution, sensitive and quantitative fluorescence imaging tool is an
urgent need for the applications in small-animal imaging and clinical cancer research. A CT-analogous method for
fluorescence molecular tomography (FMT) on small-animal-sized models is presented to improve the spatial resolution
of FMT to a limit of several millimeters, depending on the size of the tissue region to be imaged. The method combines
FMT physics with the filtered back-projection scheme for image reconstruction of the fan-beam computed tomography,
based on the early-photon detection of time-resolved optical signals, and is suitable for two-dimensional (2D) imaging of
small size biological models. By use of a normalized Born formulation for the inversion, the algorithm is validated using
full time-resolved simulated data for 2D phantom that are generated from a hybrid finite-element and
finite-time-difference photon diffusion modeling, and its superiority in the improvement of the spatial resolution is
demonstrated by imaging different target-to-background contrast ratios.
New X-ray phase contrast imaging techniques without using synchrotron radiation confront a common problem from the
negative effects of finite source sizes and limited spatial resolution. These negative effects swamp the fine phase contrast
fringes and make them almost undetectable. In order to alleviate this problem, deconvolution procedures should be
applied to the x-ray phase contrast images. In this study, four different deconvolution techniques were applied to
experimental phase contrast images of a simple geometric phantom, including Weiner deconvolution method and
Tikhonov regularization techniques with their Tikhonov matrix separately set as identity matrix, first order difference
operator and second order difference operator. According to the free space propagation x-ray phase contrast imaging
system, the source-to-sample distance (SS) of 200cm or 180cm was used with corresponding sample-to-detector distance
(SD) of 20cm or 40cm. Image contrasts of 9.8%, 52.7%, 27.6% and 31.5% were separately obtained corresponding to
above mentioned four techniques with SS/SD=200cm/20cm. For the second system setting (SS/SD=180cm/40cm),
image contrasts of 11.9%, 112.8%, 66.3% and 76.5% were obtained separately. The Tikhonov regularization technique
with Tikhonov matrix chosen as identity matrix obtains the highest contrast among all techniques. However, under this
case, most noticeable artifacts and noise were introduced simultaneously. With full consideration on noise and artifacts,
the Tikhonov matrix of second order difference operator will be the best choice for Tikhonov regularization method.
We obtain absorption and scattering reconstructed images by incorporating a priori information of target location
obtained from fluorescence diffuse optical tomography (FDOT) into the diffuse optical tomography (DOT). The main
disadvantage of DOT lies in the low spatial resolution resulting from highly scattering nature of tissue in the
near-infrared (NIR), but one can use it to monitor hemoglobin concentration and oxygen saturation simultaneously, as
well as several other cheomphores such as water, lipids, and cytochrome-c-oxidase. Up to date, extensive effort has been
made to integrate DOT with other imaging modalities such as MRI, CT, to obtain accurate optical property maps of the
tissue. However, the experimental apparatus is intricate. In this study, DOT image reconstruction algorithm that
incorporates a prior structural information provided by FDOT is investigated in an attempt to optimize recovery of a
simulated optical property distribution. By use of a specifically designed multi-channel time-correlated single photon
counting system, the proposed scheme in a transmission mode is experimentally validated to achieve simultaneous
reconstruction of the fluorescent yield, lifetime, absorption and scattering coefficient. The experimental results
demonstrate that the quantitative recovery of the tumor optical properties has doubled and the spatial resolution improves
as well by applying the new improved method.
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