Dual-energy imaging can enhance lesion conspicuity. However, the conventional (fast kilovoltage switching)
dual-shot dual-energy imaging is vulnerable to patient motion. The single-shot method requires a special design
of detector system. Alternatively, single-shot bone-suppressed imaging is possible using post-image processing
combined with a filter obtained from training an artificial neural network. In this study, the authors investigate
the general properties of artificial neural network filters for bone-suppressed digital radiography. The filter
properties are characterized in terms of various parameters such as the size of input vector, the number of hidden
units, the learning rate, and so on. The preliminary result shows that the bone-suppressed image obtained from
the filter, which is designed with 5,000 teaching images from a single radiograph, results in about 95% similarity
with a commercial bone-enhanced image.
The authors introduce an algorithm to estimate the spatial dose distributions in computed tomography (CT)
images. The algorithm calculates dose distributions due to the primary and scattered photons separately. The
algorithm only requires the CT data set that includes the patient CT images and the scanner acquisition parameters.
Otherwise the scanner acquisition parameters are extracted from the CT images. Using the developed
algorithm, the dose distributions for head and chest phantoms are computed and the results show the excellent
agreements with the dose distributions obtained using a commercial Monte Carlo code. The developed algorithm
can be applied to a patient-specific CT dose estimation based on the CT data.
Single-shot dual-energy sandwich detector can produce sharp images because of subtraction of images from two sub-detector layers, which have different thick x-ray converters, of the sandwich detector. Inspired by this observation, the authors have developed a microtomography system with the sandwich detector in pursuit of high-resolution bone-enhanced small-animal imaging. The preliminary results show that the bone-enhanced images reconstructed with the subtracted projection data are better in visibility of bone details than the conventionally reconstructed images. In addition, the bone-enhanced images obtained from the sandwich detector are relatively immune to the artifacts caused by photon starvation. The microtomography with the single-shot dual-energy sandwich detector will be useful for the high-resolution bone imaging.
Dual-energy imaging method has been introduced to improve conspicuity of abnormalities in radiographs. The method typically uses the fast kilovoltage-switching approach, which acquires low and high-energy projections in successive x-ray exposures with the same detector. However, it is typically known that there exists an optimal detector thickness regarding specific imaging tasks or energies used. In this study, the dual-energy detectability has been theoretically addressed for various combinations of detector thicknesses for low and high-energy spectra using the cascaded-systems analysis. Cesium iodide (CsI) is accounted for the x-ray converter in the hypothetical detector. The simple prewhitening model shows that a larger CsI thickness (250 mg cm-2 for example) would be preferred to the the typical CsI thickness of 200 mg cm-2 for better detectability. On the other hand, the typical CsI thickness is acceptable for the prewhitening model considering human-eye filter. The theoretical strategy performed in this study will be useful for a better design of detectors for dual-energy imaging.
The scatter effect on detective quantum efficiency (DQE) of digital mammography is investigated using the
cascaded-systems model. The cascaded-systems model includes a scatter-reduction device as a binomial selection
stage. Quantum-noise-limited operation approximates the system DQE into the multiplication form of the
scatter-reduction device DQE and the conventional detector DQE. The developed DQE model is validated in
comparisons with the measured results using a CMOS flat-panel detector under scatter environments. For various
scatter-reduction devices, the slot-scan method shows the best scatter-cleanup performance in terms of DQE,
and the scatter-cleanup performance of the conventional one-dimensional grid is rather worse than the air gap.
The developed model can also be applied to general radiography and will be very useful for a better design of
We present a theoretical framework describing projections obtained from computed tomography systems considering physics of each component consisting of the systems. The projection model mainly consists of the attenuation of x-ray photons through objects including x-ray scatter and the detection of attenuated/scattered x-ray photons at pixel detector arrays. X-ray photons are attenuated by the Beers-Lambert law and scattered by using the Klein-Nishina formula. The cascaded signal-transfer model for the detector includes x-ray photon detection and light photon conversion/spreading in scintillators, light photon detection in photodiodes, and the addition of electronic noise quanta. On the other hand, image noise is considered by re-distributing the pixel signals in pixel-by-pixel ways at each image formation stage using the proper distribution functions. Instead of iterating the ray tracing over each energy bin in the x-ray spectrum, we first perform the ray tracing for an object only considering the thickness of each component. Then, we assign energy-dependent linear attenuation coefficients to each component in the projected images. This approach reduces the computation time by a factor of the number of energy bins in the x-ray spectrum divided by the number of components in the object compared with the conventional ray-tracing method. All the methods developed in this study are validated in comparisons with the measurements or the Monte Carlo simulations.
The modulation transfer function (MTF) is a typical parameter to measure the spatial resolution, which is an essential
factor for evaluating the performance of computed tomography (CT) systems. It is known that the CT system does not
follow the shift-invariant manner because of the cone-beam geometry and the transformation from the cylindrical
coordinates to the axial coordinates when the image reconstruction is employed. Several studies reported that if the
position of impulse receded from the center of a region of interest (ROI), the MTF degraded continuously. In this study,
the trend of shift-variant characteristics of CT systems was measured and analyzed using a novel multi-cylindrical
phantom. This study used to determine a point spread function (PSF) and MTF of a CT system using a simple cylindrical
phantom. First of all, the optimal diameter of cylinder phantoms was experimentally determined as 70 mm to obtain
reliable PSFs. Two kinds of field of views (FOVs), 40 cm and 60 cm, were used to vary reconstructed pixel sizes. The
shift-variant MTF curves were acquired at five off-center positions per FOV. For the effective analysis of MTF shiftvariance,
the integrated MTF values were calculated and used. In the result, the MTF slightly decreased as diameter
increased from CT center in the central region within the distance of 10 cm. Moreover, a considerable MTF decrease
suddenly occurred around the distance of 15 cm in the actual FOVs. The decreasing trend of the off-center spatial
resolution of CT cannot be neglected in recent radiologic and radio-therapeutic fields requiring high degree of image
precision, especially in sub-mm images. It is recommended that the ROI is laid on the CT center as close as possible. A
novel cylindrical phantom was finally suggested to effectively measure PSFs with optimal diameters for clinical FOVs in
this study. This phantom is cheap and convenient to use because it was only made of acryl with simple geometry. It is
expected that the spatial resolution of CT can be easily monitored using our methodology in clinical CT sites.