Daniel Hernandez

Electrical conductivity and permittivity maps of brain tissues derived from water content based on T1 -weighted acquisition.

To develop an electrical properties tomography (EPT) technique that can provide in vivo electrical conductivity and permittivity images of biological tissue without performing complex‐valued radiofrequency field measurements.

Denoising of B1+ field maps for noise-robust image reconstruction in electrical properties tomography

PURPOSE To validate the use of adaptive nonlinear filters in reconstructing conductivity and permittivity images from the noisy B₁(+) maps in electrical properties tomography (EPT). METHODS In EPT, electrical property images are computed by taking Laplacian of the B₁(+) maps. To mitigate the noise amplification in computing the Laplacian, the authors applied adaptive nonlinear denoising filters to the measured complex B₁(+) maps. After the denoising process, they computed the Laplacian by central differences. They performed EPT experiments on phantoms and a human brain at 3 T along with corresponding EPT simulations on finite-difference time-domain models. They evaluated the EPT images comparing them with the ones obtained by previous EPT reconstruction methods. RESULTS In both the EPT simulations and experiments, the nonlinear filtering greatly improved the EPT image quality when evaluated in terms of the mean and standard deviation of the electrical property values at the regions of interest. The proposed method also improved the overall similarity between the reconstructed conductivity images and the true shapes of the conductivity distribution. CONCLUSIONS The nonlinear denoising enabled us to obtain better-quality EPT images of the phantoms and the human brain at 3 T.

Iterative image reconstruction in spectral-CT

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.

A head motion estimation algorithm for motion artifact correction in dental CT imaging

A small head motion of the patient can compromise the image quality in a dental CT, in which a slow cone-beam scan is adopted. We introduce a retrospective head motion estimation method by which we can estimate the motion waveform from the projection images without employing any external motion monitoring devices. We compute the cross-correlation between every two successive projection images, which results in a sinusoid-like displacement curve over the projection view when there is no patient motion. However, the displacement curve deviates from the sinusoid-like form when patient motion occurs. We develop a method to estimate the motion waveform with a single parameter derived from the displacement curve with aid of image entropy minimization. To verify the motion estimation method, we use a lab-built micro-CT that can emulate major head motions during dental CT scans, such as tilting and nodding, in a controlled way. We find that the estimated motion waveform conforms well to the actual motion waveform. To further verify the motion estimation method, we correct the motion artifacts with the estimated motion waveform. After motion artifact correction, the corrected images look almost identical to the reference images, with structural similarity index values greater than 0.81 in the phantom and rat imaging studies.

Dual‐energy‐based metal segmentation for metal artifact reduction in dental computed tomography

PURPOSE In a dental CT scan, the presence of dental fillings or dental implants generates severe metal artifacts that often compromise readability of the CT images. Many metal artifact reduction (MAR) techniques have been introduced, but dental CT scans still suffer from severe metal artifacts particularly when multiple dental fillings or implants exist around the region of interest. The high attenuation coefficient of teeth often causes erroneous metal segmentation, compromising the MAR performance. We propose a metal segmentation method for a dental CT that is based on dual-energy imaging with a narrow energy gap. METHODS Unlike a conventional dual-energy CT, we acquire two projection data sets at two close tube voltages (80 and 90 kVp ), and then, we compute the difference image between the two projection images with an optimized weighting factor so as to maximize the contrast of the metal regions. We reconstruct CT images from the weighted difference image to identify the metal region with global thresholding. We forward project the identified metal region to designate metal trace on the projection image. We substitute the pixel values on the metal trace with the ones computed by the region filling method. The region filling in the metal trace removes high-intensity data made by the metallic objects from the projection image. We reconstruct final CT images from the region-filled projection image with the fusion-based approach. We have done imaging experiments on a dental phantom and a human skull phantom using a lab-built micro-CT and a commercial dental CT system. RESULTS We have corrected the projection images of a dental phantom and a human skull phantom using the single-energy and dual-energy-based metal segmentation methods. The single-energy-based method often failed in correcting the metal artifacts on the slices on which tooth enamel exists. The dual-energy-based method showed better MAR performances in all cases regardless of the presence of tooth enamel on the slice of interest. We have compared the MAR performances between both methods in terms of the relative error (REL), the sum of squared difference (SSD) and the normalized absolute difference (NAD). For the dental phantom images corrected by the single-energy-based method, the metric values were 95.3%, 94.5%, and 90.6%, respectively, while they were 90.1%, 90.05%, and 86.4%, respectively, for the images corrected by the dual-energy-based method. For the human skull phantom images, the metric values were improved from 95.6%, 91.5%, and 89.6%, respectively, to 88.2%, 82.5%, and 81.3%, respectively. CONCLUSIONS The proposed dual-energy-based method has shown better performance in metal segmentation leading to better MAR performance in dental imaging. We expect the proposed metal segmentation method can be used to improve the MAR performance of existing MAR techniques that have metal segmentation steps in their correction procedures.

A method for the analysis and data reduction from Hartmann and Shack-Hartmann tests

A computer software is proposed in order to determine the centroids of the spots in a Hartmann pattern or Hartmanngrams. The software was developed using algorithms for segmentation of images, which are techniques used in digital image processing. Centroid determination for a Shack-Hartmann pattern is the key point to obtain reliable results. We focus in obtaining good centroid determination for Hartmanngrams under conditions of high noise. The proposed algorithms are the essential part of this work, as they are morphological algorithms, which mainly are modifications of the weighted averaging algorithm. They have several advantages, such as, the adjustment to the shape of every spot of the Hartmanngram and that it is an interactive and automatic software. Although the software is more complete and reliable than other techniques and algorithms, since it can analyze complicated pictures of Hartmanngrams and measure the centroids of the spots.A computer software is proposed in order to determine the centroids of the spots in a Hartmann pattern or Hartmanngrams. The software was developed using algorithms for segmentation of images, which are techniques used in digital image processing. Centroid determination for a Shack-Hartmann pattern is the key point to obtain reliable results. We focus in obtaining good centroid determination for Hartmanngrams under conditions of high noise. The proposed algorithms are the essential part of this work, as they are morphological algorithms, which mainly are modifications of the weighted averaging algorithm. They have several advantages, such as, the adjustment to the shape of every spot of the Hartmanngram and that it is an interactive and automatic software. Although the software is more complete and reliable than other techniques and algorithms, since it can analyze complicated pictures of Hartmanngrams and measure the centroids of the spots.