Age-related cortical thinning has been studied by many researchers using quantitative MR images for the past three decades and vastly differing results have been reported. Although results have shown age-related cortical thickening in elderly cohort statistically in some brain regions under certain conditions, cortical thinning in elderly cohort requires further systematic investigation. This paper leverages our previously reported brain surface intensity model (BSIM)1 based technique to measure cortical thickness to study cortical changes due to normal aging. We measured cortical thickness of cognitively normal persons from 60 to 89 years old using Australian Imaging Biomarkers and Lifestyle Study (AIBL) data. MRI brains of 56 healthy people including 29 women and 27 men were selected. We measured average cortical thickness of each individual in eight brain regions: parietal, frontal, temporal, occipital, visual, sensory motor, medial frontal and medial parietal. Unlike the previous published studies, our results showed consistent age-related thinning of cerebral cortex in all brain regions. The parietal, medial frontal and medial parietal showed fastest thinning rates of 0.14, 0.12 and 0.10 mm/decade respectively while the visual region showed the slowest thinning rate of 0.05 mm/decade. In sensorimotor and parietal areas, women showed higher thinning (0.09 and 0.16 mm/decade) than men while in all other regions men showed higher thinning than women. We also created high resolution cortical thinning rate maps of the cohort and compared them to typical patterns of PET metabolic reduction of moderate AD and frontotemporal dementia (FTD). The results seemed to indicate vulnerable areas of cortical deterioration that may lead to brain dementia. These results validate our cortical thickness measurement technique by demonstrating the consistency of the cortical thinning and prediction of cortical deterioration trend with AIBL database.
Cortical thinning and metabolic reduction can be possible imaging biomarkers for Alzheimer’s disease (AD) diagnosis and monitoring. Many techniques have been developed for the cortical measurement and widely used for the clinical statistical studies. However, the measurement consistency of individuals, an essential requirement for a clinically useful technique, requires proper further investigation. Here we leverage our previously developed BSIM technique 1 to measure cortical thickness and thinning and use it with longitudinal MRI from ADNI to investigate measurement consistency and spatial resolution. 10 normal, 10 MCI, and 10 AD subjects in their 70s were selected for the study. Consistent cortical thinning patterns were observed in all baseline and follow up images. Rapid cortical thinning was shown in some MCI and AD cases. To evaluate the correctness of the cortical measurement, we compared longitudinal cortical thinning with clinical diagnosis and longitudinal PET metabolic reduction measured using 3D-SSP technique2 for the same person. Longitudinal MR cortical thinning and corresponding PET metabolic reduction showed high level pattern similarity revealing certain correlations worthy of further studies. Severe cortical thinning that might link to disease conversion from MCI to AD was observed in two cases. In summary, our results suggest that consistent cortical measurements using our technique may provide means for clinical diagnosis and monitoring at individual patient’s level and MR cortical thinning measurement can complement PET metabolic reduction measurement.
Alzheimer’s disease (AD) is caused by pathological changes including cortical thinning occurring throughout the brain.
Traditional methods for assessing cortical thickness are challenged by the sub-millimeter accuracy required for clinical
conditions and the convoluted nature of brain surface. Furthermore, there is a significant overlap of gray and white
matter intensities. A novel Brain Surface Intensity Model (BSIM) has been developed for use as a potential imaging
biomarker for neurodegenerative diseases. BSIM technique extracts MR intensity profiles perpendicular to a mathematically defined gray matter iso-intensity layer (GMIIL) at predefined reference points, fits that profile to BSIM,
and computes cortical thickness. A 3D visualization tool has been developed to evaluate intensity extraction and model
calculation. 29 normal subjects aged between 70 to 80 years from ADNI database were used to generate normal
references and measure individual Z-score cortical thinning. 30 age-matched AD subjects were used to study thinning
patterns. Significant cortical thinning (p < 0.0001) was found for AD group. 95% confidence interval of the cortical
thinning in AD patients was from 0.17 to 0.23 mm. The cortical thinning of the AD patients showed distinct features that differentiate AD patients from normal controls. The thickness measurements of 29 normal controls were validated by comparing with results from literature (p = 0.94). BSIM technique avoids complicated 3D segmentation of brain gray
and white matters, and simplifies the thickness calculation. Moreover, it is less affected by the image noise, inhomogeneity, partial volume effects, and the intensity overlap of the white and gray matters.
Cone-beam filtered backprojection (CB-FBP) is one of the major reconstruction algorithms for digital tomosynthesis. In conventional FBP, the photon fluxes in projections are evenly distributed along the X-ray beam. Due to the limited view angles and finite detector dimensions, this uniform weighting causes non-uniformity in the recon images and leads to cone-beam artifact. In this paper, we propose a 3-D view weighting technique in combination with FBP to combat this artifact. An anthropomorphic chest phantom was placed at supine position to enable the imaging of chest PA view. During a linear sweep of X-ray source, 41 X-ray images at different projection angles were acquired with the following protocol: 120kVp, 160mA, and 0.64mAs/exposure. To create the worst scenario for testing, we chose 60 degrees as the sweep angle in this exam. The data set was reconstructed with conventional CB-FBP and proposed algorithm under the same parameters: FOV = 40x40 cm^2, and slice thickness = 4mm. 3 recon slices were randomly selected for review with slice height = 10.5/14.5/17.5cm. Results were assessed qualitatively by human observers and quantitatively through ROI measurement. In each slice, three pre-defined ROIs (50x50 pixels)--ROI A and B are in artifact more pronounced area, and ROI C is in relatively artifact-free area--are extracted and measured. The non-uniformity error was defined as the ratio of MEAN(AVG(C-A), AVG(C-B)) / AVG(C). The average non-uniformity error over the three test images was 0.428 for without view weighting and only 0.041 for with view weighting.
In digital tomosynthesis, one of the limitations is the presence of out-of-plane blur due to the limited angle acquisition. The point spread function (PSF) characterizes blur in the imaging volume, and is shift-variant in tomosynthesis. The purpose of this research is to classify the tomosynthesis imaging volume into four different categories based on PSF-driven focus criteria. We considered linear tomosynthesis geometry and simple back projection algorithm for reconstruction. The three-dimensional PSF at every pixel in the imaging volume was determined. Intensity profiles were computed for every pixel by integrating the PSF-weighted intensities contained within the line segment defined by the PSF, at each slice. Classification rules based on these intensity profiles were used to categorize image regions. At background and low-frequency pixels, the derived intensity profiles were flat curves with relatively low and high maximum intensities respectively. At in-focus pixels, the maximum intensity of the profiles coincided with the PSF-weighted intensity of the pixel. At out-of-focus pixels, the PSF-weighted intensity of the pixel was always less than the maximum intensity of the profile. We validated our method using human observer classified regions as gold standard. Based on the computed and manual classifications, the mean sensitivity and specificity of the algorithm were 77+/-8.44% and 91+/-4.13% respectively (t=-0.64, p=0.56, DF=4). Such a classification algorithm may assist in mitigating out-of-focus blur from tomosynthesis image slices.
KEYWORDS: Image quality, Image resolution, Modulation transfer functions, Sensors, Signal to noise ratio, Radiography, Chest, Medical imaging, Digital imaging, X-rays
Digital tomosynthesis (DTS) is emerging as an advanced imaging technique that enables volumetric slice imaging with a detector typically used for projection radiography. An understanding of the interactions between DTS acquisition parameters and characteristics of the reconstructed slice images is required for optimizing the acquisition protocols of various clinical applications. This paper presents our investigation of the effects and interactions of acquisition parameters, including sweep angle, number of projections, and dose, on clinically relevant image-quality metrics. Metrics included the image characteristics of in-slice resolution, depth resolution, image noise level, and presence of ripple.
Phantom experiments were performed to characterize the relationship between the acquisition parameters and image quality. Results showed that the depth resolution was mainly dependent on sweep angle. Visibility of ripple was determined by the projection density (number of projections divided by sweep angle), as well as properties of the imaged object. Image noise was primarily dependent on total dose and not significantly affected by the number of projections. These experimental and theoretical results were confirmed using anthropomorphic phantoms and also used to develop clinical acquisition protocols. Assessment of phantom and clinical images obtained with these protocols revealed that the use of acquisition protocols optimized for a given clinical exam enables rapid, low-dose, high quality DTS imaging for diverse clinical applications including abdomen, hand, shoulder, spine, and chest.
We conclude that DTS acquisition parameters have a significant effect on image quality and should be tailored for the imaged anatomy and desired clinical application. Relationships developed in this work will guide the selection of acquisition protocols to improve image quality and clinical utility of DTS for a wide variety of clinical exams.
In this study, we investigate the relationship between quantum noise and spatial resolution for volumetric CT. Both theoretical analysis and experiments were performed to investigate their relationship. In theory, quantum noise can be derived from its relationship to dose, in-plane spatial resolution, recon kernel, and signal-to-noise ratio (SNR). In the experiments, by scanning a Teflon sphere phantom, the 3-D MTF was measured from the edge profile along the spherical surface. Cases of different resolutions (and noise levels) were generated by adjusting recon kernel. To reduce bias, the total photon fluxes were matched: 120kVp, 260mA, and 1sec per gantry rotation. In the end, all data sets were reconstructed using modified FDK algorithm under the same condition: FOV=10cm and slice thickness=0.625mm. Finally, we investigated the efficiency of an image-space adaptive smoothing filter as a noise reduction tool and its impact on spatial resolution. The theoretical analysis indicated that the variance of noise is proportional to at least 4th power of the spatial resolution. Our experimental results supported this conclusion by showing the relationship is 4.6th (helical) or 5th (axial) power. Results also showed that, with properly designed image-space smoothing filters, it is feasible to reduce quantum noise (and the power relationship to a lower order) with smaller loss of spatial resolution.
Tomosynthesis is widely used for three-dimensional reconstruction of objects acquired from limited angle X-ray projection imaging with stationary digital detector. Traditionally, the point-spread function (PSF) in digital tomosynthesis is assumed to be symmetrical with respect to the central axis and shift invariant. The purpose of this research is to characterize the true nature of the PSF by intensity and shape considerations. We assumed that tomosynthesis PSF depended on the imaging geometry and the reconstruction algorithms. In this paper, we describe PSF characterization with respect to the linear geometry and back projection reconstruction. We considered the following parameters: source to image distance (SID) (mm), total number of slices reconstructed after reconstruction, distance (in z-direction) from the first and the last slice to the detector (mm), resolution in X, Y & Z (pix/mm), and total number of projections. Using these parameters, we determined the PSF at every location of the reconstructed volume. The PSF was contained in the plane formed by the linear source trajectory and the point under consideration that extended through all the slices. The results show that the PSF is shift variant and unique at every location and gradually changing over the entire reconstructed volume. The shift from the central axis and central reconstructed slice caused the PSF to exhibit shear corresponding to the X-shift, tilt with the Y-shift and asymmetry with the Z-shift. In summary, we have characterized tomosynthesis PSF to be globally shift variant exhibiting shear, tilt and asymmetry.
Dual-energy imaging shows increased conspicuity and specificity of lung nodule detection through the removal of undesired contrast resulting from overlying bone structures.
We have developed an algorithm that automatically determines the optimal cancellation parameters for a log-subtraction technique for a pair of high- and low-energy images. The core algorithm involves shrinking the data, extracting bone features, extracting salient edge from these bone features, calculating a tissue-cancellation map, computing the maximum-likelihood bone contrast cancellation parameter, and finally, calculating the soft-tissue cancellation parameter using an empirical relationship.
We verified the performance of the algorithm using observer studies, in which the value of the tissue-cancellation parameter calculated by the algorithm was compared to the value manually selected by nineteen trained observers. A number of dual-energy images were acquired with a modified GE Revolution XQ/i, flat-panel-detector chest imaging system, using an anthropomorphic phantom. The effects of variables such as patient size, kVp, mAs, lung texture, patient motion, and the presence of foreign objects in field-of-view on algorithmic performance were evaluated.
We found that the algorithm-selected parameter values had less variability than those selected by the observers. Furthermore, the algorithm-selected parameter was within the limits of the variability of the observers for all cases.
Dual-energy (DE) chest radiography with a digital flat panel (DFP) shows significant potential for increased sensitivity and specificity of pulmonary nodule detection. DFP-based DE produces significantly better image quality compared to Computed Radiography (CR) due to high detective quantum efficiency (DQE) and wide energy separation. We developed novel noise reduction filtering that significantly improves image quality at a given dose level, thereby allowing considerable additional dose reduction compared to CR. The algorithm segments images into structures, which are processed using anisotropic smoothing and sharpening, and non-structures, which are processed using isotropic smoothing. A fraction of the original image is blended with the processed image to obtain an image with improved noise characteristics. DE decomposed radiographs were obtained at film equivalent of 400 speed chest exam dose for 12 patients (set A) and at twice the dose for 7 other patients (set C). Images from set A were filtered using our algorithm to form set B. Images were evaluated by four radiologists using a noise rating scale. A two-sample t-test showed no significant difference in ratings between B and C, while significant differences were found between A and B, and A and C. Therefore, our algorithm enables effective patient dose reduction while maintaining perceptual image quality.
Cortical bone is the major barrier in visualizing the 3-D blood vessel tree from CT Angiography [CTA] data. Thus, we have developed a novel semi-automatic technique that removes the cortical bone and retains the clinical diagnostic information such as blood vessels, aneurysms, and calcifications. The technique is based on a methodical composite set of filters that use region-growing, adaptive, and morphological filtering algorithms. While using only voxel intensity value and region size information, this technique retains most of the CTA data untouched. We have implemented this method on 10 CTA abdomen and head data sets. The accuracy of the method was tested and proved successful by visual inspection of all segmented slices. The segmented CTA data were also visualized in 3-D with different Ray Casting Volume Rendering techniques (e.g. Maximum Intensity Projection). The blood vessels along with other diagnostic information were clearly visualized in 3-D without the obstruction of bone. The segmentation technique ran under one second per slice (image size is 512x512x2 bytes) on a PC with 550 MHz processor.
Dual-energy subtraction imaging increases the sensitivity and specificity of pulmonary nodule detection in chest radiography by reducing the contrast of overlying bone structures. Recent development of a fast, high-efficiency detector enables dual-energy imaging to be integrated into the traditional workflow. We have modified a GE RevolutionTM XQ/i chest imaging system to construct a dual-energy imaging prototype system. Here we describe the operating characteristics of this prototype and evaluate image quality. Empirical results show that the dual-energy CNR is maximized if the dose is approximately equal for both high and low energy exposures. Given the high detector DQE, and allocation of dose between the two views, we can acquire dual-energy PA and conventional lateral images with total dose equivalent to a conventional two-view film chest exam. Calculations have shown that the dual-exposure technique has superior CNR and tissue cancellation than single-exposure CR systems. Clinical images obtained on a prototype dual-energy imaging system show excellent tissue contrast cancellation, low noise, and modest motion artefacts. In summary, a prototype dual-energy system has been constructed which enables rapid, dual-exposure imaging of the chest using a commercially available high-efficiency, flat-panel x-ray detector. The quality of the clinical images generated with this prototype exceeds that of CR techniques and demonstrates the potential for improved detection and characterization of lung disease through dual-energy imaging.
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