Spiral CT image deblurring for cochlear implantation

Experimental Validation of a Three-Dimensional Heat Transfer Model Within the Scala Tympani With Application to Magnetic Cochlear Implant Surgery

Magnetic guidance of cochlear implants is a promising technique to reduce the risk of physical trauma during surgery. In this approach, a magnet attached to the tip of the implant electrode array is guided within the scala tympani using a magnetic field. After surgery, the magnet must be detached from the implant electrode array via localized heating, which may cause thermal trauma, and removed from the scala tympani.

OBJECTIVES

The objective of this work is to experimentally validate a three-dimensional (3D) heat transfer model of the scala tympani which will enable accurate predictions of the maximum safe input power to avoid localized hyperthermia when detaching the magnet from the implant electrode array.

METHODS

Experiments are designed using a rigorous scale analysis and performed by measuring transient temperatures in a 3D-printed scala tympani phantom subjected to a sudden change in its isothermal environment and localized heating via a small heat source.

RESULTS

The measured and predicted temperatures are in good agreement with an error less than 6 % ( p= 0.84). For the most conservative case where all boundaries of the model except the insertion opening are adiabatic, the power required to release the magnet attached to the implant electrode array by 1 mm 3 of paraffin is approximately half of the predicted maximum safe input power.

CONCLUSIONS

A 3D heat transfer model of the scala tympani is successfully validated and enables predicting the maximum safe input power required to detach the magnet from the implant electrode array.

SIGNIFICANCE

This work will enable the design of a thermally safe magnetic cochlear implant surgery procedure.

Harmonization of in-plane resolution in CT using multiple reconstructions from single acquisitions

PURPOSE

To providea methodology that removes the spatial variability of in-plane resolution using different CT reconstructions. The methodology does not require any training, sinogram, or specific reconstruction method.

METHODS

The methodology is formulated as a reconstruction problem. The desired sharp image is modeled as an unobservable variable to be estimated from an arbitrary number of observations with spatially variant resolution. The methodology comprises three steps: (1) density harmonization, which removes the density variability across reconstructions; (2) point spread function (PSF) estimation, which estimates a spatially variant PSF with arbitrary shape; (3) deconvolution, which is formulated as a regularized least squares problem. The assessment was performed with CT scans of phantoms acquired with three different Siemens scanners (Definition AS, Definition AS+, Drive). Four low-dose acquisitions reconstructed with backprojection and iterative methods were used for the resolution harmonization. A sharp, high-dose (HD) reconstruction was used as a validation reference. The different factors affecting the in-plane resolution (radial, angular, and longitudinal) were studied with regression analysis of the edge decay (between 10% and 90% of the edge spread function (ESF) amplitude).

RESULTS

Results showed that the in-plane resolution improves remarkably and the spatial variability is substantially reduced without compromising the noise characteristics. The modulated transfer function (MTF) also confirmed a pronounced increase in resolution. The resolution improvement was also tested by measuring the wall thickness of tubes simulating airways. In all scanners, the resolution harmonization obtained better performance than the HD, sharp reconstruction used as a reference (up to 50 percentage points). The methodology was also evaluated in clinical scans achieving a noise reduction and a clear improvement in thin-layered structures. The estimated ESF and MTF confirmed the resolution improvement.

CONCLUSION

We propose a versatile methodology to reduce the spatial variability of in-plane resolution in CT scans by leveraging different reconstructions available in clinical studies. The methodology does not require any sinogram, training, or specific reconstruction, and it is not limited to a fixed number of input images. Therefore, it can be easily adopted in multicenter studies and clinical practice. The results obtained with our resolution harmonization methodology evidence its suitability to reduce the spatially variant in-plane resolution in clinical CT scans without compromising the reconstruction's noise characteristics. We believe that the resolution increase achieved by our methodology may contribute in more accurate and reliable measurements of small structures such as vasculature, airways, and wall thickness.

Combined application of single-energy metal artifact reduction and reconstruction techniques in patients with Cochlear implants

Background

The purpose of this study was to develop an effective method of reducing metal artifacts in cochlear implant (CI) electrodes.

Methods

The temporal bones of 30 patients (34 ears) after CI were examined with 320-detector row computed tomography, which was evaluated by two senior radiologists using a double-blind method. Noise, artifact index, signal-to-noise ratio, and the subjective image quality score were compared before versus after using single-energy metal artifact reduction (SEMAR). The electrode position, single electrode visibility, and electrode count were evaluated using SEMAR combined with either multi-planar reconstruction (MPR) or maximum intensity projection.

Results

The two radiologists’ measurements had good consistency. SEMAR significantly reduced the image noise and artifacts index and significantly improved the signal-to-noise ratio and subjective image quality score (P < 0.01). The combination of SEMAR with MPR was conducive to accurate assessment of electrode position and single-electrode visibility. The combination of SEMAR with MIP facilitated accurate and intuitive matching of the assessed electrode count with the number of electrodes implanted during the operation (P = 0.062).

Conclusion

SEMAR significantly reduces metal artifacts generated by CI electrodes and improves the quality of computed tomography images. The combination of SEMAR with MPR and maximum intensity projection is beneficial for evaluating the position and number of CI electrodes.

CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE)

In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the imaging performance, we apply a parallel 1×1 CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. The quantitative and qualitative evaluative results demonstrate that our proposed model is accurate, efficient and robust for super-resolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three large-scale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.

Weight-Discrimination Sensitivity in Congenitally Blind and Sighted Adults

This study compared the weight-discrimination sensitivity of 41 congenitally blind and 41 normally sighted adults. The superior weight-discrimination sensitivity of the congenitally blind subjects suggests that blindness from birth can cause compensatory adaptations within the cutaneous modality.

Patient-specific estimation of detailed cochlear shape from clinical CT images

PURPOSE

A personalized estimation of the cochlear shape can be used to create computational anatomical models to aid cochlear implant (CI) surgery and CI audio processor programming ultimately resulting in improved hearing restoration. The purpose of this work is to develop and test a method for estimation of the detailed patient-specific cochlear shape from CT images.

METHODS

From a collection of temporal bone [Formula: see text]CT images, we build a cochlear statistical deformation model (SDM), which is a description of how a human cochlea deforms to represent the observed anatomical variability. The model is used for regularization of a non-rigid image registration procedure between a patient CT scan and a [Formula: see text]CT image, allowing us to estimate the detailed patient-specific cochlear shape.

RESULTS

We test the accuracy and precision of the predicted cochlear shape using both [Formula: see text]CT and CT images. The evaluation is based on classic generic metrics, where we achieve competitive accuracy with the state-of-the-art methods for the task. Additionally, we expand the evaluation with a few anatomically specific scores.

CONCLUSIONS

The paper presents the process of building and using the SDM of the cochlea. Compared to current best practice, we demonstrate competitive performance and some useful properties of our method.

Cochlear implant phantom for evaluating computed tomography acquisition parameters

Cochlear implants (CIs) are surgically implantable neuroprosthetic devices used to treat profound hearing loss. Recent literature indicates that there is a correlation between the final intracochlear positioning of the CI electrode arrays and the ultimate hearing outcome of the patient, indicating that further studies to better understand the relationship between electrode position and outcomes could have significant implications for future surgical techniques, array design, and processor programming methods. Postimplantation high-resolution computed tomography (CT) imaging is the best modality for localizing electrodes and provides the resolution necessary to visually identify electrode position, although with an unknown degree of accuracy depending on image acquisition parameters, like the hounsfield unit (HU) range of reconstruction, orientation, radiation dose, and image resolution. We report on the development of a phantom and on its use to study how four acquisition parameters, including image resolution and HU range of reconstruction, affect how accurately the true position of the electrodes can be found in a dataset of CT scans acquired from multiple helical and cone beam scanners. We also show how the phantom can be used to evaluate the effect of acquisition parameters on automatic electrode localization techniques.

Influence of Coronary Artery Diameter on Intracoronary Transluminal Attenuation Gradient During CT Angiography

OBJECTIVES

The goal of this study was to assess the effect of coronary artery diameter on luminal attenuation and the correlation between the transluminal attenuation gradient (TAG) and transluminal diameter gradient (TDG) on computed tomography (CT) coronary angiography.

BACKGROUND

Recent studies have reported promising results of TAG in detecting significant stenosis. However, because of the intrinsic nature of CT reconstruction algorithms, luminal attenuation may be affected by vessel diameter.

METHODS

In this 3-part study, phantom simulating vessels of various diameters immersed in different contrast mixtures were scanned, and intraluminal attenuations were measured. In addition, dynamic volume CT scanning was performed in 3 mongrel dogs (untreated, a stenosis model, and an occlusion model) using 320-row area detector computed tomography and intraluminal attenuations, and TAGs were calculated at each temporal scan and compared. In a separate clinical study, TAGs and TDGs of 152 coronary arteries from 62 patients who underwent 320-row area detector computed tomography coronary angiography and invasive angiography were measured and compared.

RESULTS

Intraluminal attenuation of phantom vessels gradually decreased along with a decrease in diameter. Animal studies revealed that the peak attenuation of distal smaller coronary arteries did not reach that of proximal larger coronary arteries: 55.2% to 78.1% peak attenuation of proximal coronary arteries. No differences in TAG were found between stenotic and normal left circumflex arteries at temporal scans (all, p > 0.05). The clinical study demonstrated significant correlation between TAG and TDG (r = 0.580; p < 0.0001).

CONCLUSIONS

Intraluminal attenuation was shown to decrease with diminution of vessel diameters. In addition, TAG exhibited a significant correlation with TDG, implying that TAG may be a secondary result because of differences in diameters.

Improving Optoacoustic Image Quality via Geometric Pixel Super-Resolution Approach

High fidelity optoacoustic (photoacoustic) tomography requires dense spatial sampling of optoacoustic signals using point acoustic detectors. However, in practice, spatial resolution of the images is often limited by limited sampling either due to coarse multi-element arrays or time in raster scan measurements. Herein, we investigate a method that integrates information from multiple optoacoustic images acquired at sub-diffraction steps into one high resolution image by means of an iterative registration algorithm. Experimental validations performed in target phantoms and ex vivo tissue samples confirm that the suggested approach renders significant improvements in terms of optoacoustic image resolution and quality without introducing significant alterations into the signal acquisition hardware or inversion algorithms.

Three-dimensional models of cochlear implants: A review of their development and how they could support management and maintenance of cochlear implant performance

Three-dimensional (3D) computational modeling of the auditory periphery forms an integral part of modern-day research in cochlear implants (CIs). These models consist of a volume conduction description of implanted stimulation electrodes and the current distribution around these, coupled with auditory nerve fiber models. Cochlear neural activation patterns can then be predicted for a given input stimulus. The objective of this article is to present the context of 3D modeling within the field of CIs, the different models, and approaches to models that have been developed over the years, as well as the applications and potential applications of these models. The process of development of 3D models is discussed, and the article places specific emphasis on the complementary roles of generic models and user-specific models, as the latter is important for translation of these models into clinical application.

An iterative deconvolution algorithm for image recovery in clinical CT: A phantom study

PURPOSE

To study the feasibility of using an iterative reconstruction algorithm to improve previously reconstructed CT images which are judged to be non-diagnostic on clinical review. A novel rapidly converging, iterative algorithm (RSEMD) to reduce noise as compared with standard filtered back-projection algorithm has been developed.

MATERIALS AND METHODS

The RSEMD method was tested on in-silico, Catphan(®)500, and anthropomorphic 4D XCAT phantoms. The method was applied to noisy CT images previously reconstructed with FBP to determine improvements in SNR and CNR. To test the potential improvement in clinically relevant CT images, 4D XCAT phantom images were used to simulate a small, low contrast lesion placed in the liver.

RESULTS

In all of the phantom studies the images proved to have higher resolution and lower noise as compared with images reconstructed by conventional FBP. In general, the values of SNR and CNR reached a plateau at around 20 iterations with an improvement factor of about 1.5 for in noisy CT images. Improvements in lesion conspicuity after the application of RSEMD have also been demonstrated. The results obtained with the RSEMD method are in agreement with other iterative algorithms employed either in image space or with hybrid reconstruction algorithms.

CONCLUSIONS

In this proof of concept work, a rapidly converging, iterative deconvolution algorithm with a novel resolution subsets-based approach that operates on DICOM CT images has been demonstrated. The RSEMD method can be applied to sub-optimal routine-dose clinical CT images to improve image quality to potentially diagnostically acceptable levels.

Electronic cleansing in fecal-tagging dual-energy CT colonography based on material decomposition and virtual colon tagging

Dual-energy CT provides a promising solution to identify tagged fecal materials in electronic cleansing (EC) for fecal-tagging CT colonography (CTC). In this study, we developed a new EC method based on virtual colon tagging (VCT) for minimizing EC artifacts by use of the material decomposition ability in dual-energy CTC images. In our approach, a localized three-material decomposition model decomposes each voxel into a material mixture vector and the first partial derivatives of three base materials: luminal air, soft tissue, and iodine-tagged fecal material. A Poisson-based derivative smoothing algorithm smoothes the derivatives and implicitly smoothes the associated material mixture fields. VCT is a means for marking the entire colonic lumen by virtually elevating the CT value of luminal air as high as that of the tagged fecal materials to differentiate effectively soft-tissue structures from air-tagging mixtures. A dual-energy EC scheme based on VCT method, denoted as VCT-EC, was developed, in which the colonic lumen was first virtually tagged and then segmented by its high values in VCT images. The performance of the VCT-EC scheme was evaluated in a phantom study and a clinical study. Our results demonstrated that our VCT-EC scheme may provide a significant reduction of EC artifacts.

A dictionary learning approach for Poisson image deblurring

The restoration of images corrupted by blur and Poisson noise is a key issue in medical and biological image processing. While most existing methods are based on variational models, generally derived from a maximum a posteriori (MAP) formulation, recently sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, we propose in this paper a model containing three terms: a patch-based sparse representation prior over a learned dictionary, the pixel-based total variation regularization term and a data-fidelity term capturing the statistics of Poisson noise. The resulting optimization problem can be solved by an alternating minimization technique combined with variable splitting. Extensive experimental results suggest that in terms of visual quality, peak signal-to-noise ratio value and the method noise, the proposed algorithm outperforms state-of-the-art methods.

Modelling the physics in the iterative reconstruction for transmission computed tomography

There is an increasing interest in iterative reconstruction (IR) as a key tool to improve quality and increase applicability of x-ray CT imaging. IR has the ability to significantly reduce patient dose; it provides the flexibility to reconstruct images from arbitrary x-ray system geometries and allows one to include detailed models of photon transport and detection physics to accurately correct for a wide variety of image degrading effects. This paper reviews discretization issues and modelling of finite spatial resolution, Compton scatter in the scanned object, data noise and the energy spectrum. The widespread implementation of IR with a highly accurate model-based correction, however, still requires significant effort. In addition, new hardware will provide new opportunities and challenges to improve CT with new modelling.

Modelling the physics in the iterative reconstruction for transmission computed tomography

There is an increasing interest in iterative reconstruction (IR) as a key tool to improve quality and increase applicability of x-ray CT imaging. IR has the ability to significantly reduce patient dose; it provides the flexibility to reconstruct images from arbitrary x-ray system geometries and allows one to include detailed models of photon transport and detection physics to accurately correct for a wide variety of image degrading effects. This paper reviews discretization issues and modelling of finite spatial resolution, Compton scatter in the scanned object, data noise and the energy spectrum. The widespread implementation of IR with a highly accurate model-based correction, however, still requires significant effort. In addition, new hardware will provide new opportunities and challenges to improve CT with new modelling.

A decomposition-based CT reconstruction formulation for reducing blooming artifacts

Cardiac computed tomography represents an important advancement in the ability to assess coronary vessels. The accuracy of these non-invasive imaging studies is limited, however, by the presence of calcium, since calcium blooming artifacts lead to an over-estimation of the degree of luminal narrowing. To address this problem, we have developed a unified decomposition-based iterative reconstruction formulation, where different penalty functions are imposed on dense objects (i.e. calcium) and soft tissue. The result is a quantifiable reduction in blooming artifacts without the introduction of new distortions away from the blooming observed in other methods. Results are shown for simulations, phantoms, ex vivo, and in vivo studies.

Tomographic reconstruction of three-dimensional volumes using the distorted born iterative method

Although real imaging problems involve objects that have variations in three dimensions, a majority of work examining inverse scattering methods for ultrasonic tomography considers 2-D imaging problems. Therefore, the study of 3-D inverse scattering methods is necessary for future applications of ultrasonic tomography. In this work, 3-D reconstructions using different arrays of rectangular elements focused on elevation were studied when reconstructing spherical imaging targets by producing a series of 2-D image slices using the 2-D distorted Born iterative method (DBIM). The effects of focal number f/#, speed of sound contrast c, and scatterer size were considered. For comparison, the 3-D wave equation was also inverted using point-like transducers to produce fully 3-D DBIM image reconstructions. In 2-D slicing, blurring in the vertical direction was highly correlated with the transmit/receive elevation point-spread function of the transducers for low c. The eventual appearance of overshoot artifacts in the vertical direction were observed with increasing c. These diffraction-related artifacts were less severe for smaller focal number values and larger spherical target sizes. When using 3-D DBIM, the overshoot artifacts were not observed and spatial resolution was improved. However, results indicate that array configuration in 3-D reconstructions is important for good image reconstruction. Practical arrays were designed and assessed for image reconstruction using 3-D DBIM.

Selective deblurring for improved calcification visualization and quantification in carotid CT angiography: validation using micro-CT

Visualization and quantification of small structures with computed tomography (CT) is hampered by the limited spatial resolution of the system. Histogram-based selective deblurring (HiSD) is a deconvolution method that restores small high-density structures, i.e., calcifications, of a CT image, using the high-intensity voxel information of the deconvolved image, while preserving the original hounsfield Units (HUs) in the remaining tissues. In this study, high resolution micro-CT data are used to validate the potential of HiSD to improve calcium visualization and quantification in the carotid arteries on in vivo contrast-enhanced CTA data. The evaluation is performed qualitatively and quantitatively on 15 atherosclerotic plaques obtained from ten different patients. HiSD in combination with vessel segmentation significantly improves calcification visualization and quantification on in vivo contrast-enhanced CT images. Calcification blur is reduced, while avoiding noise amplification and edge-ringing artifacts in the surrounding tissues. Calcification quantification errors are reduced by 23.5% on average.

About objective 3-d analysis of airway geometry in computerized tomography

The technology of multislice X-ray computed tomography (MSCT) provides volume data sets with approximately isotropic resolution, which permits a noninvasive 3-D measurement and quantification of airway geometry. In different diseases, like emphysema, chronic obstructive pulmonary disease (COPD), or cystic fribrosis, changes in lung parenchyma are associated with an increase in airway wall thickness. In this paper, we describe an objective measuring method of the airway geometry in the 3-D space. The limited spatial resolution of clinical CT scanners in comparison to thin structures like airway walls causes difficulties in the measurement of the density and the thickness of these structures. Initially, these difficulties will be addressed and then a new method is introduced to circumvent the problems. Therefore the wall thickness is approximated by an integral based closed-form solution, based on the volume conservation property of convolution. We evaluated the method with a phantom containing 10 silicone tubes and proved the repeatability in datasets of eight pigs scanned twice. Furthermore, a comparison of CT datasets of 16 smokers and 15 nonsmokers was done. Further medical studies are ongoing.

Classifying CT image data into material fractions by a scale and rotation invariant edge model

A fully automated method is presented to classify 3-D CT data into material fractions. An analytical scale-invariant description relating the data value to derivatives around Gaussian blurred step edges--arch model--is applied to uniquely combine robustness to noise, global signal fluctuations, anisotropic scale, noncubic voxels, and ease of use via a straightforward segmentation of 3-D CT images through material fractions. Projection of noisy data value and derivatives onto the arch yields a robust alternative to the standard computed Gaussian derivatives. This results in a superior precision of the method. The arch-model parameters are derived from a small, but over-determined, set of measurements (data values and derivatives) along a path following the gradient uphill and downhill starting at an edge voxel. The model is first used to identify the expected values of the two pure materials (named L and H) and thereby classify the boundary. Second, the model is used to approximate the underlying noise-free material fractions for each noisy measurement. An iso-surface of constant material fraction accurately delineates the material boundary in the presence of noise and global signal fluctuations. This approach enables straightforward segmentation of 3-D CT images into objects of interest for computer-aided diagnosis and offers an easy tool for the design of otherwise complicated transfer functions in high-quality visualizations. The method is applied to segment a tooth volume for visualization and digital cleansing for virtual colonoscopy.

Histogram-Based Selective Deblurring to Improve Computed Tomography Imaging of Calcifications

OBJECTIVES

Computed tomography (CT) imaging of small high-density structures, eg, calcifications, is hampered by image blur. This study aims to deconvolve calcifications in the transverse and longitudinal directions while avoiding noise amplification and edge-ringing artifacts in the surrounding low-density structures.

MATERIALS AND METHODS

A method referred to as histogram-based selective deblurring (HiSD) has been developed to generate a restored image by combining the low-intensity (ie, Hounsfield Units) information of the original image with the high-intensity information of the deconvolved image. HiSD is evaluated on phantom and in vitro atherosclerotic plaque CT images by comparing the original and restored images with their corresponding reference micro-CT images both qualitatively and quantitatively.

RESULTS

HiSD reduces calcification blur in the transverse and longitudinal directions without introducing noise and ringing-artifacts in the surrounding tissues. Calcification area and volume measurements are significantly improved in the restored images (reducing on average overestimation by 32% and 83%, respectively).

CONCLUSIONS

HiSD significantly improves CT visualization and quantification of small high-density structures imaged in vitro.

On the convergence of generalized simultaneous iterative reconstruction algorithms

In this paper, we generalize the widely used simultaneous block iterative reconstruction algorithm and show that it converges, at a linear rate, to a weighted least-squares and weighted minimum-norm reconstruction. Our theoretical result provides a much simpler proof of the convergence properties obtained by Jiang and Wang and covers a much more general class of algorithms. The frequency domain iterative reconstruction algorithm is then introduced as a special application of our theory.

Penalized-likelihood sinogram restoration for computed tomography

We formulate computed tomography (CT) sinogram preprocessing as a statistical restoration problem in which the goal is to obtain the best estimate of the line integrals needed for reconstruction from the set of noisy, degraded measurements. CT measurement data are degraded by a number of factors-including beam hardening and off-focal radiation-that produce artifacts in reconstructed images unless properly corrected. Currently, such effects are addressed by a sequence of sinogram-preprocessing steps, including deconvolution corrections for off-focal radiation, that have the potential to amplify noise. Noise itself is generally mitigated through apodization of the reconstruction kernel, which effectively ignores the measurement statistics, although in high-noise situations adaptive filtering methods that loosely model data statistics are sometimes applied. As an alternative, we present a general imaging model relating the degraded measurements to the sinogram of ideal line integrals and propose to estimate these line integrals by iteratively optimizing a statistically based objective function. We consider three different strategies for estimating the set of ideal line integrals, one based on direct estimation of ideal "monochromatic" line integrals that have been corrected for single-material beam hardening, one based on estimation of ideal "polychromatic" line integrals that can be readily mapped to monochromatic line integrals, and one based on estimation of ideal transmitted intensities, from which ideal, monochromatic line integrals can be readily estimated. The first two approaches involve maximization of a penalized Poisson-likelihood objective function while the third involves minimization of a quadratic penalized weighted least squares (PWLS) objective applied in the transmitted intensity domain. We find that at low exposure levels typical of those being considered for screening CT, the Poisson-likelihood based approaches outperform the PWLS objective as well as a standard approach based on adaptive filtering followed by deconvolution. At higher exposure levels, the approaches all perform similarly.

Kronecker product approximation for preconditioning in three-dimensional imaging applications

We derive Kronecker product approximations, with the help of tensor decompositions, to construct approximations of severely ill-conditioned matrices that arise in three-dimensional (3-D) image processing applications. We use the Kronecker product approximations to derive preconditioners for iterative regularization techniques; the resulting preconditioned algorithms allow us to restore 3-D images in a computationally efficient manner. Through examples in microscopy and medical imaging, we show that the Kronecker approximation preconditioners provide a powerful tool that can be used to improve efficiency of iterative image restoration algorithms.

The point spread function of spiral CT

Computed tomography (CT) scanners are usually described by their in-plane resolution and slice-sensitivity profile (SSP). Other imaging systems are characterized by their point spread function (PSF). The PSF is an excellent basis for the analysis, design and enhancement of imaging systems. The 3D PSF of CT systems has rarely been considered, and has usually been approximated by a 3D Gaussian. We present mathematical analysis of the PSF of single-slice and multi-slice fan-beam and cone-beam CT, for major reconstruction algorithms. We show that the PSF has a complicated, non-separable 3D shape. It is anisotropic in the xy plane and twisted in the z direction. Furthermore, the PSF is space variant in all three axes. In particular, it rotates as the input impulse function moves in the z direction. The PSF may also have effective discontinuities that can lead to streaking artefacts. Indirect measurements of the PSF can be misleading. We support the theoretical results by direct experimental measurements of the PSF.

Design, analysis and simulation for development of the first clinical micro-CT scanner

In this article, we propose to develop the first clinical micro-CT (CMCT) system for human temporal bone imaging in vivo. This CMCT system consists of medical CT and micro-CT scanners either as separate components or in a combination, a cross-modality registration mechanism such as a facial surface scanner, and associated software. This system integrates the strengths of state-of-the-art medical CT and micro-CT techniques to achieve a spatial resolution that is much higher than currently available for inner ear imaging at acceptable dose levels. Our design, analysis, and simulation results demonstrate that the CMCT system is feasible for inner ear imaging and other clinical applications. For example, the CMCT system has the potential to improve the safety of guiding cochlear implant electrodes within the inner ear and assist the placement of inner ear microcatheters for delivery of gene modification therapy or administration of neurotrophic factors. Imaging of microarchitectures of the cancellous bone would be also an important application.

Digital subtraction CT angiography based on efficient 3D registration and refinement

A novel method for fast, automatic 3D digital subtraction CT angiography (DS-CTA) is presented to generate artifact-free angiograms. The proposed method consists of two steps: 3D registration to align a CT image to the CT angiography (CTA) image and subtraction-and-refinement to extract blood vessels only. For efficient and accurate 3D registration in the first step, an normalized mutual information (NMI) based algorithm is adopted, and its fast version is developed by introducing a new measure. To further improve the subtracted image quality in the second step, a novel 3D refinement algorithm is suggested to effectively remove unwanted residuals. Experimental results of seven clinical CT/CTA head datasets demonstrate that cerebral vessels are well extracted from CTA images with almost no loss. The typical processing time is 3-9 min depending on the image size in a PC with a 2.4 GHz CPU.

Maximizing quantitative accuracy of lung airway lumen and wall measures obtained from X-ray CT imaging

To objectively quantify airway geometry from three-dimensional computed tomographic (CT) images, an idealized (circular cross section) airway model is parameterized by airway luminal caliber, wall thickness, and tilt angle. Using a two-dimensional CT slice, an initial guess for the airway center, and the full-width-half-maximum principle, we form an estimate of inner and outer airway wall locations. We then fit ellipses to the inner and outer airway walls via a direct least squares fit and use the major and minor axes of the ellipses to estimate the tilt and in-plane rotation angles. Convolving the airway model, initialized with these estimates, with the three-dimensional scanner point-spread function forms the predicted image. The difference between predicted and actual images is minimized by refining the model parameter estimates via a multidimensional, unconstrained, nonlinear minimization routine. When optimization converges, airway model parameters estimate the airway inner and outer radii and tilt angle. Results using a Plexiglas phantom show that tilt angle is estimated to within +/-4 degrees and both inner and outer radii to within one-half pixel when a "standard" CT reconstruction kernel is used. By opening up the ability to measure airways that are not oriented perpendicular to the scanning plane, this method allows evaluation of a greater sampling of airways in a two-dimensional CT slice than previously possible. In addition, by combining the tilt-angle compensation with the deconvolution method, we provide significant improvement over the previous full-width-half-maximum method for assessing location of the luminal edge but not the outer edge of the airway wall.

Blind deblurring of spiral CT images

To discriminate fine anatomical features in the inner ear, it has been desirable that spiral computed tomography (CT) may perform beyond their current resolution limits with the aid of digital image processing techniques. In this paper, we develop a blind deblurring approach to enhance image resolution retrospectively without complete knowledge of the underlying point spread function (PSF). An oblique CT image can be approximated as the convolution of an isotropic Gaussian PSF and the actual cross section. Practically, the parameter of the PSF is often unavailable. Hence, estimation of the parameter for the underlying PSF is crucially important for blind image deblurring. Based on the iterative deblurring theory, we formulate an edge-to-noise ratio (ENR) to characterize the image quality change due to deblurring. Our blind deblurring algorithm estimates the parameter of the PSF by maximizing the ENR, and deblurs images. In the phantom studies, the blind deblurring algorithm reduces image blurring by about 24%, according to our blurring residual measure. Also, the blind deblurring algorithm works well in patient studies. After fully automatic blind deblurring, the conspicuity of the submillimeter features of the cochlea is substantially improved.

Spatial variation of resolution and noise in multi-detector row spiral CT

RATIONALE AND OBJECTIVES

The authors performed this study to evaluate an approach for measuring the variations of three-dimensional spatial resolution and image noise throughout a field of view imaged with multi-detector row spiral computed tomographic (CT) scanners.

MATERIALS AND METHODS

The authors designed a phantom (diameter, 320 mm) that contained 37 metallic spheres (diameter, approximately 0.8 mm) positioned between two disks made of a material with attenuation being that of water. One sphere was located at the isocenter of the phantom, and the rest were evenly spaced in three concentric rings with diameters of 100, 200, and 300 mm, respectively. The phantom was imaged with two widely used multi-detector row CT scanners by using a standard protocol and four variations of that protocol. Because a recently developed theory holds that image resolution should be proportional to the square root of the trace of the covariance matrix of a point spread function, the authors developed a software package to segment high-attenuation spheres from the CT image volume and compute point spread functions from blurred images of the spheres. Three-dimensional spatial resolution and image noise were calculated as a function of radial distance within the field of view.

RESULTS

Resolution and noise were quantified in the resultant CT image volumes and found to be nonisotropic, with worse resolution and less noise occurring at the periphery of the field of view.

CONCLUSION

The method enabled measurement of variations in spatial resolution and of their distribution on images obtained with multi-detector row CT scanners. These findings may contribute to the development of an improved algorithm for image reconstruction.

Convergence of the simultaneous algebraic reconstruction technique (SART)

Computed tomography (CT) has been extensively studied for years and widely used in the modern society. Although the filtered back-projection algorithm is the method of choice by manufacturers, efforts are being made to revisit iterative methods due to their unique advantages, such as superior performance with incomplete noisy data. In 1984, the simultaneous algebraic reconstruction technique (SART) was developed as a major refinement of the algebraic reconstruction technique (ART). However, the convergence of the SART has never been established since then. In this paper, the convergence is proved under the condition that coefficients of the linear imaging system are nonnegative. It is shown that from any initial guess the sequence generated by the SART converges to a weighted least square solution.

Phantom smelling

A case of phantom smelling (phantosmia) is described in a 28-yr.-old man who developed permanent bilateral anosmia after a serious injury to olfaction-related brain structures at the age of 25 years. The findings indicate that, even years after loss of input from olfactory receptors, the neural representation of olfactory perception can still recreate olfactory sensations without any conscious recall of them. This indicates that the neural representation of olfactory sensations remains functional and implies that neuronal activity in the olfactory organ or in other brain structures gives rise to olfactory experiences perceived as originating from the perception of original odor substances. The report suggests the intriguing possibility that the olfactory perception is not a passive process that merely reflects its normal input from the olfactory system but is continuously generated by a neural representation in the olfactory organ or in other olfaction-related brain structures, based on both genetic and sensory determinants. To the author's knowledge this is the first reported case of its kind.

Blind deblurring of spiral CT images-comparative studies on edge-to-noise ratios

A recently developed blind deblurring algorithm based on the edge-to-noise ratio has been applied to improve the quality of spiral CT images. Since the discrepancy measure used to quantify the edge and noise effects is not symmetric, there are several ways to formulate the edge-to-noise ratio. This article is to investigate the performance of those ratios with phantom and patient data. In the phantom study, it is shown that all the ratios share similar properties, validating the blind deblurring algorithm. The image fidelity improvement varies from 29% to 33% for different ratios, according to the root mean square error (RMSE) criterion; the optimal iteration number determined for each ratio varies from 25 to 35. Those ratios that are associated with most satisfactory performance are singled out for the image fidelity improvement of about 33% in the numerical simulation. After automatic blind deblurring with the selected ratios, the spatial resolution of CT is substantially refined in all the cases tested.

Three-dimensional geometric modeling of the cochlea using helico-spiral approximation

In this paper, the three-dimensional geometry of the human cochlea is modeled by the helico-spiral seashell model. The 3-D helico-spiral model, the generalized representation of the Archimedian spiral model, provides a framework for measuring cochlear features based on consistent estimation of model parameters. Nonlinear least square minimization based algorithms are developed for the identification of rotation, center and intrinsic parameters of the helico-spiral representation. Two algorithms are designed for the rotation axis aligned to the modiolar axis: one is more susceptible in the presence of noise, while the other allows applicability to two-dimensional data sets. The estimated center and intrinsic parameters allow the calculation of length, height and angular positions needed for frequency mapping of multichannel cochlear implant electrodes. Model performance is evaluated with numerically synthesized curves with different levels of added random noise, histologic data and real human cochlear spiral computed tomography data.

Three-dimensional modeling and visualization of the cochlea on the Internet

Three-dimensional (3-D) modeling and visualization of the cochlea using the World Wide Web (WWW) is an effective way of sharing anatomic information for cochlear implantation over the Internet, particularly for morphometry-based research and resident training in otolaryngology and neuroradiology. In this paper, 3-D modeling, visualization, and animation techniques are integrated in an interactive and platform-independent manner and implemented over the WWW. Cohen's template shape with mean cross-sectional areas of the human cochlea is extended into a 3-D geometrical model. Also, spiral computer tomography data of a patient's cochlea is digitally segmented and geometrically represented. The cochlear electrode array is synthesized according to its specification. Then, cochlear implantation is animated with both idealized and real cochlear models. Insertion length, angular position, and characteristic frequency of individual electrodes are estimated online during the virtual insertion. The optimization of the processing parameters is done to demonstrate the feasibility of this technology for clinical applications.

Phantom sensations in a patient with cervical nerve root avulsion

This case study reports detailed phantom sensations in a 35-yr.-old man who had his C5 and C6 cervical nerve roots avulsed from the cord during a motorcycle accident at the age of 22 years. The subject, who was left with a paralyzed right deltoid muscle, anesthetic sensation along the upper lateral portion of the right arm, and absent right biceps reflex, became aware of phantom right arm and hand sensations a few months after the original injury. This finding--which has important implications for understanding the process involved in bodily perception as well as the development of these perceptions--provides evidence of a distributed neural representation of the body that has both genetic and experiential determinants. The implications of these findings are discussed with reference to recent concepts of phantom limb experiences and related phenomena.

Modeling and optimization of rotational C-arm stereoscopic X-ray angiography

Stereoscopy can be an effective method for obtaining three-dimensional (3-D) spatial information from two-dimensional (2-D) projection X-ray images, without the need for tomographic reconstruction. This much-needed information is missed in many X-ray diagnostic and interventional procedures, such as the treatment of vascular aneurysms. Fast C-arm X-ray systems can obtain multiple angle sequences of stereoscopic image pairs from a single contrast injection and a single breath hold. To advance this solution, we developed a model of stereo angiography, performed perception experiments and related results to optimal acquisition. The model described horizontal disparity for the C-arm geometry that agreed very well with measurements from a geometric phantom. The perceptual accommodation-convergence conflict and geometry limited the effective stereoscopic field of view (SFOV). For a typical large image intensifier system, it was 28 cm x 31 cm at the center of rotation (COR). In the model, blurring from finite focal-spot size and C-arm motion reduced depth resolution on the digital display. Near the COR, the predicted depth resolution was 3-11 mm for a viewing angle of 7 degrees , which agreed favorably with results from recently published studies. The model also described how acquisition parameters affected spatial warping of curves of equal apparent depth. Pincushioning and the difference between the acquisition and display geometry were found to introduce additional distortions to stereo displays. Preference studies on X-ray angiograms indicated that the ideal viewing angle should be small (1-2 degrees), which agreed with some previously published work. Perceptual studies indicated that stereo angiograms should have high artery contrast and that digital processing to increase contrast improved stereopsis. Digital subtraction angiograms, with different motion errors between the left and right-eye views, gave artifacts that confused stereopsis. The addition of background to subtracted images reduced this effect and provided other features for improved depth perception. Using the modeling results and typical clinical angiography requirements, we recommend acquisition protocols and engineering specifications that are achievable on current high-end systems.