Motion-compensated scheme for sequential scanned statistical iterative dual-energy CT reconstruction.

Objective.Dual-energy computed tomography (DECT) has been widely used to reconstruct numerous types of images due its ability to better discriminate tissue properties. Sequential scanning is a popular dual-energy data acquisition method as it requires no specialized hardware. However, patient motion between two sequential scans may lead to severe motion artifacts in DECT statistical iterative reconstructions (SIR) images. The objective is to reduce the motion artifacts in such reconstructions.Approach.We propose a motion-compensation scheme that incorporates a deformation vector field into any DECT SIR. The deformation vector field is estimated via the multi-modality symmetric deformable registration method. The precalculated registration mapping and its inverse or adjoint are then embedded into each iteration of the iterative DECT algorithm.Main results.Results from a simulated and clinical case show that the proposed framework is capable of reducing motion artifacts in DECT SIRs. Percentage mean square errors in regions of interest in the simulated and clinical cases were reduced from 4.6% to 0.5% and 6.8% to 0.8%, respectively. A perturbation analysis was then performed to determine errors in approximating the continuous deformation by using the deformation field and interpolation. Our findings show that errors in our method are mostly propagated through the target image and amplified by the inverse matrix of the combination of the Fisher information and Hessian of the penalty term.Significance.We have proposed a novel motion-compensation scheme to incorporate a 3D registration method into the joint statistical iterative DECT algorithm in order to reduce motion artifacts caused by inter-scan motion, and successfully demonstrate that interscan motion corrections can be integrated into the DECT SIR process, enabling accurate imaging of radiological quantities on conventional SECT scanners, without significant loss of either computational efficiency or accuracy.

Towards subpercentage uncertainty proton stopping-power mapping via dual-energy CT: Direct experimental validation and uncertainty analysis of a statistical iterative image reconstruction method.

PURPOSE: To assess the potential of a joint dual-energy computerized tomography (CT) reconstruction process (statistical image reconstruction method built on a basis vector model (JSIR-BVM)) implemented on a 16-slice commercial CT scanner to measure high spatial resolution stopping-power ratio (SPR) maps with uncertainties of less than 1%. METHODS: JSIR-BVM was used to reconstruct images of effective electron density and mean excitation energy from dual-energy CT (DECT) sinograms for 10 high-purity samples of known density and atomic composition inserted into head and body phantoms. The measured DECT data consisted of 90 and 140 kVp axial sinograms serially acquired on a Philips Brilliance Big Bore CT scanner without beam-hardening corrections. The corresponding SPRs were subsequently measured directly via ion chamber measurements on a MEVION S250 superconducting synchrocyclotron and evaluated theoretically from the known sample compositions and densities. Deviations of JSIR-BVM SPR values from their theoretically calculated and directly measured ground-truth values were evaluated for our JSIR-BVM method and our implementation of the Hünemohr-Saito (H-S) DECT image-domain decomposition technique for SPR imaging. A thorough uncertainty analysis was then performed for five different scenarios (comparison of JSIR-BVM stopping-power ratio/stopping power (SPR/SP) to International Commission on Radiation Measurements and Units benchmarks; comparison of JSIR-BVM SPR to measured benchmarks; and uncertainties in JSIR-BVM SPR/SP maps for patients of unknown composition) per the Joint Committee for Guides in Metrology and the Guide to Expression of Uncertainty in Measurement, including the impact of uncertainties in measured photon spectra, sample composition and density, photon cross section and I-value models, and random measurement uncertainty. Estimated SPR uncertainty for three main tissue groups in patients of unknown composition and the weighted proportion of each tissue type for three proton treatment sites were then used to derive a composite range uncertainty for our method. RESULTS: Mean JSIR-BVM SPR estimates deviated by less than 1% from their theoretical and directly measured ground-truth values for most inserts and phantom geometries except for high-density Delrin and Teflon samples with SPR error relative to proton measurements of 1.1% and -1.0% (head phantom) and 1.1% and -1.1% (body phantom). The overall root-mean-square (RMS) deviations over all samples were 0.39% and 0.52% (head phantom) and 0.43% and 0.57% (body phantom) relative to theoretical and directly measured ground-truth SPRs, respectively. The corresponding RMS (maximum) errors for the image-domain decomposition method were 2.68% and 2.73% (4.68% and 4.99%) for the head phantom and 0.71% and 0.87% (1.37% and 1.66%) for the body phantom. Compared to H-S SPR maps, JSIR-BVM yielded 30% sharper and twofold sharper images for soft tissues and bone-like surrogates, respectively, while reducing noise by factors of 6 and 3, respectively. The uncertainty (coverage factor k = 1) of the DECT-to-benchmark values comparison ranged from 0.5% to 1.5% and is dominated by scanning-beam photon-spectra uncertainties. An analysis of the SPR uncertainty for patients of unknown composition showed a JSIR-BVM uncertainty of 0.65%, 1.21%, and 0.77% for soft-, lung-, and bony-tissue groups which led to a composite range uncertainty of 0.6-0.9%. CONCLUSIONS: Observed JSIR-BVM SPR estimation errors were all less than 50% of the estimated k = 1 total uncertainty of our benchmarking experiment, demonstrating that JSIR-BVM high spatial resolution, low-noise SPR mapping is feasible and is robust to variations in the geometry of the scanned object. In contrast, the much larger H-S SPR estimation errors are dominated by imaging noise and residual beam-hardening artifacts. While the uncertainties characteristic of our current JSIR-BVM implementation can be as large as 1.5%, achieving < 1% total uncertainty is feasible by improving the accuracy of scanner-specific scatter-profile and photon-spectrum estimates. With its robustness to beam-hardening artifact, image noise, and variations in phantom size and geometry, JSIR-BVM has the potential to achieve high spatial-resolution SPR mapping with subpercentage accuracy and estimated uncertainty in the clinical setting.

Experimental implementation of a joint statistical image reconstruction method for proton stopping power mapping from dual-energy CT data.

PURPOSE: To experimentally commission a dual-energy CT (DECT) joint statistical image reconstruction (JSIR) method, which is built on a linear basis vector model (BVM) of material characterization, for proton stopping power ratio (SPR) estimation. METHODS: The JSIR-BVM method builds on the relationship between the energy-dependent photon attenuation coefficients and the proton stopping power via a pair of BVM component weights. The two BVM component images are simultaneously reconstructed from the acquired DECT sinograms and then used to predict the electron density and mean excitation energy (I-value), which are required by the Bethe equation for SPR computation. A post-reconstruction image-based DECT method, which utilizes the two separate CT images reconstructed via the scanner's software, was implemented for comparison. The DECT measurement data were acquired on a Philips Brilliance scanner at 90 and 140 kVp for two phantoms of different sizes. Each phantom contains 12 different soft and bony tissue surrogates with known compositions. The SPR estimation results were compared to the reference values computed from the known compositions. The difference of the computed water equivalent path lengths (WEPL) across the phantoms between the two methods was also compared. RESULTS: The overall root-mean-square (RMS) of SPR estimation error of the JSIR-BVM method are 0.33% and 0.37% for the head- and body-sized phantoms, respectively, and all SPR estimates of the test samples are within 0.7% of the reference ground truth. The image-based method achieves overall RMS errors of 2.35% and 2.50% for the head- and body-sized phantoms, respectively. The JSIR-BVM method also reduces the pixel-wise random variation by 4-fold to 6-fold within homogeneous regions compared to the image-based method. The average differences between the JSIR-BVM method and the image-based method are 0.54% and 1.02% for the head- and body-sized phantoms, respectively. CONCLUSION: By taking advantage of an accurate polychromatic CT data model and a model-based DECT statistical reconstruction algorithm, the JSIR-BVM method accounts for both systematic bias and random noise in the acquired DECT measurement data. Therefore, the JSIR-BVM method achieves good accuracy and precision on proton SPR estimation for various tissue surrogates and object sizes. In contrast, the experimentally achievable accuracy of the image-based method may be limited by the uncertainties in the image formation process. The result suggests that the JSIR-BVM method has the potential for more accurate SPR prediction compared to post-reconstruction image-based methods in clinical settings.

Multi-GPU Acceleration of Branchless Distance Driven Projection and Backprojection for Clinical Helical CT.

Model-based image reconstruction (MBIR) techniques have the potential to generate high quality images from noisy measurements and a small number of projections which can reduce the x-ray dose in patients. These MBIR techniques rely on projection and backprojection to refine an image estimate. One of the widely used projectors for these modern MBIR based technique is called branchless distance driven (DD) projection and backprojection. While this method produces superior quality images, the computational cost of iterative updates keeps it from being ubiquitous in clinical applications. In this paper, we provide several new parallelization ideas for concurrent execution of the DD projectors in multi-GPU systems using CUDA programming tools. We have introduced some novel schemes for dividing the projection data and image voxels over multiple GPUs to avoid runtime overhead and inter-device synchronization issues. We have also reduced the complexity of overlap calculation of the algorithm by eliminating the common projection plane and directly projecting the detector boundaries onto image voxel boundaries. To reduce the time required for calculating the overlap between the detector edges and image voxel boundaries, we have proposed a pre-accumulation technique to accumulate image intensities in perpendicular 2D image slabs (from a 3D image) before projection and after backprojection to ensure our DD kernels run faster in parallel GPU threads. For the implementation of our iterative MBIR technique we use a parallel multi-GPU version of the alternating minimization (AM) algorithm with penalized likelihood update. The time performance using our proposed reconstruction method with Siemens Sensation 16 patient scan data shows an average of 24 times speedup using a single TITAN X GPU and 74 times speedup using 3 TITAN X GPUs in parallel for combined projection and backprojection.

Quantitative Computed Tomography Classification of Lung Nodules: Initial Comparison of 2- and 3-Dimensional Analysis.

OBJECTIVE: The aim of this study was to compare the performance of 2- (2D) and 3-dimensional (3D) quantitative computed tomography (CT) methods for classifying lung nodules as lung cancer, metastases, or benign. METHODS: Using semiautomated software and computerized analysis, we analyzed more than 50 quantitative CT features of 96 solid nodules in 94 patients, in 2D from a single slice and in 3D from the entire nodule volume. Multivariable logistic regression was used to classify nodule types. Model performance was assessed by the area under the receiver operating characteristic curve (AUC) using leave-one-out cross-validation. RESULTS: The AUC for distinguishing 53 primary lung cancers from 18 benign nodules and 25 metastases ranged from 0.79 to 0.83 and was not significantly different for 2D and 3D analyses (P = 0.29-0.78). Models distinguishing metastases from benign nodules were statistically significant only by 3D analysis (AUC = 0.84). CONCLUSIONS: Three-dimensional CT methods did not improve discrimination of lung cancer, but may help distinguish benign nodules from metastases.

Line Integral Alternating Minimization Algorithm for Dual-Energy X-Ray CT Image Reconstruction.

We propose a new algorithm, called line integral alternating minimization (LIAM), for dual-energy X-ray CT image reconstruction. Instead of obtaining component images by minimizing the discrepancy between the data and the mean estimates, LIAM allows for a tunable discrepancy between the basis material projections and the basis sinograms. A parameter is introduced that controls the size of this discrepancy, and with this parameter the new algorithm can continuously go from a two-step approach to the joint estimation approach. LIAM alternates between iteratively updating the line integrals of the component images and reconstruction of the component images using an image iterative deblurring algorithm. An edge-preserving penalty function can be incorporated in the iterative deblurring step to decrease the roughness in component images. Images from both simulated and experimentally acquired sinograms from a clinical scanner were reconstructed by LIAM while varying the regularization parameters to identify good choices. The results from the dual-energy alternating minimization algorithm applied to the same data were used for comparison. Using a small fraction of the computation time of dual-energy alternating minimization, LIAM achieves better accuracy of the component images in the presence of Poisson noise for simulated data reconstruction and achieves the same level of accuracy for real data reconstruction.

Technical Note: Measurement of bow tie profiles in CT scanners using radiochromic film.

PURPOSE: To provide a noninvasive technique to measure the intensity profile of the fan beam in a computed tomography (CT) scanner that is cost effective and easily implemented without the need to access proprietary scanner information or service modes. METHODS: The fabrication of an inexpensive aperture is described, which is used to expose radiochromic film in a rotating CT gantry. A series of exposures is made, each of which is digitized on a personal computer document scanner, and the resulting data set is analyzed to produce a self-consistent calibration of relative radiation exposure. The bow tie profiles were analyzed to determine the precision of the process and were compared to two other measurement techniques, direct measurements from CT gantry detectors and a dynamic dosimeter. RESULTS: The radiochromic film method presented here can measure radiation exposures with a precision of ∼ 6% root-mean-square relative error. The intensity profiles have a maximum 25% root-mean-square relative error compared with existing techniques. CONCLUSIONS: The proposed radiochromic film method for measuring bow tie profiles is an inexpensive (∼$100 USD + film costs), noninvasive method to measure the fan beam intensity profile in CT scanners.

Measurement of bow tie profiles in CT scanners using a real-time dosimeter.

PURPOSE: Several areas of computed tomography (CT) research require knowledge about the intensity profile of the x-ray fan beam that is introduced by a bow tie filter. This information is considered proprietary by CT manufacturers, so noninvasive measurement methods are required. One method using real-time dosimeters has been proposed in the literature. A commercially available dosimeter was used to apply that method, and analysis techniques were developed to extract fan beam profiles from measurements. METHODS: A real-time ion chamber was placed near the periphery of an empty CT gantry and the dose rate versus time waveform was recorded as the x-ray source rotated about the isocenter. In contrast to previously proposed analysis methods that assumed a pointlike detector, the finite-size ion chamber received varying amounts of coverage by the collimated x-ray beam during rotation, precluding a simple relationship between the source intensity as a function of fan beam angle and measured intensity. A two-parameter model for measurement intensity was developed that included both effective collimation width and source-to-detector distance, which then was iteratively solved to minimize the error between duplicate measurements at corresponding fan beam angles, allowing determination of the fan beam profile from measured dose-rate waveforms. Measurements were performed on five different scanner systems while varying parameters such as collimation, kVp, and bow tie filters. On one system, direct measurements of the bow tie profile were collected for comparison with the real-time dosimeter technique. RESULTS: The data analysis method for a finite-size detector was found to produce a fan beam profile estimate with a relative error between duplicate measurement intensities of <5%. It was robust over a wide range of collimation widths (e.g., 1-40 mm), producing fan beam profiles that agreed with a relative error of 1%-5%. Comparison with a direct measurement technique on one system produced agreement with a relative error of 2%-6%. Fan beam profiles were found to differ for different filter types on a given system and between different vendors. CONCLUSIONS: A commercially available real-time dosimeter probe was found to be a convenient and accurate instrument for measuring fan beam profiles. An analysis method was developed that could handle a wide range of collimation widths by explicitly considering the finite width of the ion chamber. Relative errors in the profiles were found to be less than 5%. Measurements of five different clinical scanners demonstrate the variation in bow tie designs, indicating that generic bow tie models will not be adequate for CT system research.

Characterization of calibration curves and energy dependence GafChromic™ XR-QA2 model based radiochromic film dosimetry system.

PURPOSE: The authors investigated the energy response of XR-QA2 GafChromic™ film over a broad energy range used in diagnostic radiology examinations. The authors also made an assessment of the most suitable functions for both reference and relative dose measurements. METHODS: Pieces of XR-QA2 film were irradiated to nine different values of air kerma in air, following reference calibration of a number of beam qualities ranging in HVLs from 0.16 to 8.25 mm Al, which corresponds to effective energy range from 12.7 keV to 56.3 keV. For each beam quality, the authors tested three functional forms (rational, linear exponential, and power) to assess the most suitable function by fitting the delivered air kerma in air as a function of film response in terms of reflectance change. The authors also introduced and tested a new parameter χ = netΔR·e(m netΔR) that linearizes the inherently nonlinear response of the film. RESULTS: The authors have found that in the energy range investigated, the response of the XR-QA2 based radiochromic film dosimetry system ranges from 0.222 to 0.420 in terms of netΔR at K(air)(air) = 8 cGy. For beam qualities commonly used in CT scanners (4.03-8.25 mm Al), the variation in film response (netΔR at K(air)(air) = 8 cGy) amounts to ± 5%, while variation in K(air)(air) amounts to ± 14%. CONCLUSIONS: Results of our investigation revealed that the use of XR-QA2 GafChromic™ film is accompanied by a rather pronounced energy dependent response for beam qualities used for x-ray based diagnostic imaging purposes. The authors also found that the most appropriate function for the reference radiochromic film dosimetry would be the power function, while for the relative dosimetry one may use the exponential response function that can be easily linearized.

Prospects for in vivo estimation of photon linear attenuation coefficients using postprocessing dual-energy CT imaging on a commercial scanner: comparison of analytic and polyenergetic statistical reconstruction algorithms.

PURPOSE: Accurate patient-specific photon cross-section information is needed to support more accurate model-based dose calculation for low energy photon-emitting modalities in medicine such as brachytherapy and kilovoltage x-ray imaging procedures. A postprocessing dual-energy CT (pDECT) technique for noninvasive in vivo estimation of photon linear attenuation coefficients has been experimentally implemented on a commercial CT scanner and its accuracy assessed in idealized phantom geometries. METHODS: Eight test materials of known composition and density were used to compare pDECT-estimated linear attenuation coefficients to NIST reference values over an energy range from 10 keV to 1 MeV. As statistical image reconstruction (SIR) has been shown to reconstruct images with less random and systematic error than conventional filtered backprojection (FBP), the pDECT technique was implemented with both an in-house polyenergetic SIR algorithm, alternating minimization (AM), as well as a conventional FBP reconstruction algorithm. Improvement from increased spectral separation was also investigated by filtering the high-energy beam with an additional 0.5 mm of tin. The law of propagated uncertainty was employed to assess the sensitivity of the pDECT process to errors in reconstructed images. RESULTS: Mean pDECT-estimated linear attenuation coefficients for the eight test materials agreed within 1% of NIST reference values for energies from 1 MeV down to 30 keV, with mean errors rising to between 3% and 6% at 10 keV, indicating that the method is unbiased when measurement and calibration phantom geometries are matched. Reconstruction with FBP and AM algorithms conferred similar mean pDECT accuracy. However, single-voxel pDECT estimates reconstructed on a 1 × 1 × 3 mm(3) grid are shown to be highly sensitive to reconstructed image uncertainty; in some cases pDECT attenuation coefficient estimates exhibited standard deviations on the order of 20% around the mean. Reconstruction with the statistical AM algorithm led to standard deviations roughly 40% to 60% less than FBP reconstruction. Additional tin filtration of the high energy beam exhibits similar pDECT estimation accuracy as the unfiltered beam, even when scanning with only 25% of the dose. Using the law of propagated uncertainty, low Z materials are found to be more sensitive to image reconstruction errors than high Z materials. Furthermore, it is estimated that reconstructed CT image uncertainty must be limited to less than 0.25% to achieve a target linear-attenuation coefficient estimation uncertainty of 3% at 28 keV. CONCLUSIONS: That pDECT supports mean linear attenuation coefficient measurement accuracies of 1% of reference values for energies greater than 30 keV is encouraging. However, the sensitivity of the pDECT measurements to noise and systematic errors in reconstructed CT images warrants further investigation in more complex phantom geometries. The investigated statistical reconstruction algorithm, AM, reduced random measurement uncertainty relative to FBP owing to improved noise performance. These early results also support efforts to increase DE spectral separation, which can further reduce the pDECT sensitivity to measurement uncertainty.

Image gently campaign back to basics initiative: ten steps to help manage radiation dose in pediatric digital radiography.

OBJECTIVE: The purpose of this review is to summarize 10 steps a practice can take to manage radiation exposure in pediatric digital radiography. CONCLUSION: The Image Gently campaign raises awareness of opportunities for lowering radiation dose while maintaining diagnostic quality of images of children. The newest initiative in the campaign, Back to Basics, addresses methods for standardizing the approach to pediatric digital radiography, highlighting challenges related to the technology in imaging of patients of widely varying body sizes.

Experimental implementation of a polyenergetic statistical reconstruction algorithm for a commercial fan-beam CT scanner.

PURPOSE: To present a framework for characterizing the data needed to implement a polyenergetic model-based statistical reconstruction algorithm, Alternating Minimization (AM), on a commercial fan-beam CT scanner and a novel method for assessing the accuracy of the commissioned data model. METHODS: The X-ray spectra for three tube potentials on the Philips Brilliance CT scanner were estimated by fitting a semi-empirical X-ray spectrum model to transmission measurements. Spectral variations due to the bowtie filter were computationally modeled. Eight homogeneous cylinders of PMMA, Teflon and water with varying diameters were scanned at each energy. Central-axis scatter was measured for each cylinder using a beam-stop technique. AM reconstruction with a single-basis object-model matched to the scanned cylinder's composition allows assessment of the accuracy of the AM algorithm's polyenergetic data model. Filtered-backprojection (FBP) was also performed to compare consistency metrics such as uniformity and object-size dependence. RESULTS: The spectrum model fit measured transmission curves with residual root-mean-square-error of 1.20%-1.34% for the three scanning energies. The estimated spectrum and scatter data supported polyenergetic AM reconstruction of the test cylinders to within 0.5% of expected in the matched object-model reconstruction test. In comparison to FBP, polyenergetic AM exhibited better uniformity and less object-size dependence. CONCLUSIONS: Reconstruction using a matched object-model illustrate that the polyenergetic AM algorithm's data model was commissioned to within 0.5% of an expected ground truth. These results support ongoing and future research with polyenergetic AM reconstruction of commercial fan-beam CT data for quantitative CT applications.

New exposure indicators for digital radiography simplified for radiologists and technologists.

OBJECTIVE: The purpose of this article is to educate radiologists and technologists about the clinically relevant portion of the new digital radiography standards. CONCLUSION: Both the International Electrotechnical Commission (IEC standard 62494-1) and the American Association of Physicists in Medicine (AAPM Task Group 116) have developed similar standards for monitoring exposure in digital radiography to eliminate proprietary and confusing terminology. Radiologists and technologists will need to learn three new terms--exposure index, target exposure index, and deviation index--to understand the new standards.

Accuracy of computed tomography detection of superior canal dehiscence.

HYPOTHESIS: High-resolution temporal bone computed tomography (CT) may erroneously demonstrate a superior semicircular canal dehiscence (SSCD) where none exists and inaccurately display the size of a dehiscence. BACKGROUND: CT is an integral component of the diagnosis of SSCD. The prevalence of dehiscence as measured on computed tomographic scan is approximately eightfold higher than that on histologic studies, suggesting that CT may have a relatively low specificity for identifying canal dehiscence. This, in turn, can lead to an inappropriate diagnosis and treatment plan. METHODS: We quantified the accuracy of CT in identifying a dehiscence of the superior semicircular canal in a cadaver model using microCT as a gold standard. The superior canals of 11 cadaver heads were blue lined. Twelve of the 22 ears were further drilled to create fenestrations of varying sizes. Heads were imaged using medical CT, followed by microCT scans of the temporal bones at 18-µm resolution. Diagnosis of dehiscence and measurements of dehiscence size were performed on clinical CT and compared with that of microCT. RESULTS: Clinical CT identified 7 of 8 intact canals as dehiscent and tended to overestimate the size of smaller fenestrations, particularly those surrounded by thin bone. CONCLUSION: These findings confirm that medical CT cannot be used as the exclusive gold standard for SSCD and that, particularly for small dehiscences on CT, clinical symptoms must be clearly indicative of a dehiscence before surgical treatment is undertaken. Preoperative counseling for small dehiscences may need to include the possibility that no dehiscence may be found despite radiologic evidence for it.

Noise-resolution tradeoffs in x-ray CT imaging: a comparison of penalized alternating minimization and filtered backprojection algorithms.

PURPOSE: In comparison with conventional filtered backprojection (FBP) algorithms for x-ray computed tomography (CT) image reconstruction, statistical algorithms directly incorporate the random nature of the data and do not assume CT data are linear, noiseless functions of the attenuation line integral. Thus, it has been hypothesized that statistical image reconstruction may support a more favorable tradeoff than FBP between image noise and spatial resolution in dose-limited applications. The purpose of this study is to evaluate the noise-resolution tradeoff for the alternating minimization (AM) algorithm regularized using a nonquadratic penalty function. METHODS: Idealized monoenergetic CT projection data with Poisson noise were simulated for two phantoms with inserts of varying contrast (7%-238%) and distance from the field-of-view (FOV) center (2-6.5 cm). Images were reconstructed for the simulated projection data by the FBP algorithm and two penalty function parameter values of the penalized AM algorithm. Each algorithm was run with a range of smoothing strengths to allow quantification of the noise-resolution tradeoff curve. Image noise is quantified as the standard deviation in the water background around each contrast insert. Modulation transfer functions (MTFs) were calculated from six-parameter model fits to oversampled edge-spread functions defined by the circular contrast-insert edges as a metric of local resolution. The integral of the MTF up to 0.5 1p/mm was adopted as a single-parameter measure of local spatial resolution. RESULTS: The penalized AM algorithm noise-resolution tradeoff curve was always more favorable than that of the FBP algorithm. While resolution and noise are found to vary as a function of distance from the FOV center differently for the two algorithms, the ratio of noises when matching the resolution metric is relatively uniform over the image. The ratio of AM-to-FBP image variances, a predictor of dose-reduction potential, was strongly dependent on the shape of the AM's nonquadratic penalty function and was also strongly influenced by the contrast of the insert for which resolution is quantified. Dose-reduction potential, reported here as the fraction (%) of FBP dose necessary for AM to reconstruct an image with comparable noise and resolution, for one penalty parameter value of the AM algorithm was found to vary from 70% to 50% for low-contrast and high-contrast structures, respectively, and from 70% to 10% for the second AM penalty parameter value. However, the second penalty, AM-700, was found to suffer from poor low-contrast resolution when matching the high-contrast resolution metric with FBP. CONCLUSIONS: The results of this simulation study imply that penalized AM has the potential to reconstruct images with similar noise and resolution using a fraction (10%-70%) of the FBP dose. However, this dose-reduction potential depends strongly on the AM penalty parameter and the contrast magnitude of the structures of interest. In addition, the authors' results imply that the advantage of AM can be maximized by optimizing the nonquadratic penalty function to the specific imaging task of interest. Future work will extend the methods used here to quantify noise and resolution in images reconstructed from real CT data.

Effects of CT section thickness and reconstruction kernel on emphysema quantification relationship to the magnitude of the CT emphysema index.

RATIONALE AND OBJECTIVES: Computed tomography (CT) section thickness and reconstruction kernel each influence CT measurements of emphysema. This study was performed to assess whether their effects are related to the magnitude of the measurement. MATERIALS AND METHODS: Low-radiation-dose multidetector CT was performed in 21 subjects representing a wide range of emphysema severity. Images were reconstructed using 20 different combinations of section thickness and reconstruction kernel. Emphysema index values were determined as the percentage of lung pixels having attenuation lower than multiple thresholds ranging from -960 HU to -890 HU. The index values obtained from the different thickness-kernel combinations were compared by repeated measures analysis of variance and Bland-Altman plots of mean versus difference in all subjects, and correlated with quantitative histology (mean linear intercept, Lm) in a subset of resected lung specimens. RESULTS: The effects of section thickness and reconstruction kernel on the emphysema index were significant (P < .001) and diminished as the index attenuation threshold was raised. The changes in index values from changing the thickness-kernel combination were largest for subjects with intermediate index values (10%-30%), and became progressively smaller for those with lower and higher index values. This pattern was consistent regardless of the thickness-kernel combinations compared and the HU threshold used. Correlations between the emphysema index values obtained with each thickness-kernel combination and Lm ranged from r = 0.55-0.68 (P = .007-.03). CONCLUSION: The effects of CT section thickness and kernel on emphysema index values varied systematically with the magnitude of the emphysema index. All reconstruction techniques provided significant correlations with quantitative histology.

Validation of CT dose-reduction simulation.

The objective of this research was to develop and validate a custom computed tomography dose-reduction simulation technique for producing images that have an appearance consistent with the same scan performed at a lower mAs (with fixed kVp, rotation time, and collimation). Synthetic noise is added to projection (sinogram) data, incorporating a stochastic noise model that includes energy-integrating detectors, tube-current modulation, bowtie beam filtering, and electronic system noise. Experimental methods were developed to determine the parameters required for each component of the noise model. As a validation, the outputs of the simulations were compared to measurements with cadavers in the image domain and with phantoms in both the sinogram and image domain, using an unbiased root-mean-square relative error metric to quantify agreement in noise processes. Four-alternative forced-choice (4AFC) observer studies were conducted to confirm the realistic appearance of simulated noise, and the effects of various system model components on visual noise were studied. The "just noticeable difference (JND)" in noise levels was analyzed to determine the sensitivity of observers to changes in noise level. Individual detector measurements were shown to be normally distributed (p > 0.54), justifying the use of a Gaussian random noise generator for simulations. Phantom tests showed the ability to match original and simulated noise variance in the sinogram domain to within 5.6% +/- 1.6% (standard deviation), which was then propagated into the image domain with errors less than 4.1% +/- 1.6%. Cadaver measurements indicated that image noise was matched to within 2.6% +/- 2.0%. More importantly, the 4AFC observer studies indicated that the simulated images were realistic, i.e., no detectable difference between simulated and original images (p = 0.86) was observed. JND studies indicated that observers' sensitivity to change in noise levels corresponded to a 25% difference in dose, which is far larger than the noise accuracy achieved by simulation. In summary, the dose-reduction simulation tool demonstrated excellent accuracy in providing realistic images. The methodology promises to be a useful tool for researchers and radiologists to explore dose reduction protocols in an effort to produce diagnostic images with radiation dose "as low as reasonably achievable".

Role of electrode placement as a contributor to variability in cochlear implant outcomes.

HYPOTHESIS: Suboptimal cochlear implant (CI) electrode array placement may reduce presentation of coded information to the central nervous system and, consequently, limit speech recognition. BACKGROUND: Generally, mean speech reception scores for CI recipients are similar across different CI systems, yet large outcome variation is observed among recipients implanted with the same device. These observations suggest significant recipient-dependent factors influence speech reception performance. This study examines electrode array insertion depth and scalar placement as recipient-dependent factors affecting outcome. METHODS: Scalar location and depth of insertion of intracochlear electrodes were measured in 14 patients implanted with Advanced Bionics electrode arrays and whose word recognition scores varied broadly. Electrode position was measured using computed tomographic images of the cochlea and correlated with stable monosyllabic word recognition scores. RESULTS: Electrode placement, primarily in terms of depth of insertion and scala tympani versus scala vestibuli location, varies widely across subjects. Lower outcome scores are associated with greater insertion depth and greater number of contacts being located in scala vestibuli. Three patterns of scalar placement are observed suggesting variability in insertion dynamics arising from surgical technique. CONCLUSION: A significant portion of variability in word recognition scores across a broad range of performance levels of CI subjects is explained by variability in scalar location and insertion depth of the electrode array. We suggest that this variability in electrode placement can be reduced and average speech reception improved by better selection of cochleostomy sites, revised insertion approaches, and control of insertion depth during surgical placement of the array.

Use of computed tomography scans for cochlear implants.

While 3-dimensional (3D) imaging by computed tomography has long been desirable for research and treatment of cochlear-implant patients, technical challenges have limited its wide application. Recent developments in scanner hardware and image processing techniques now allow image quality improvements that make clinical applications feasible. Validation experiments were performed to characterize a new methodology and its imaging performance.

In vivo estimates of the position of advanced bionics electrode arrays in the human cochlea.

OBJECTIVES: A new technique for determining the position of each electrode in the cochlea is described and applied to spiral computed tomography data from 15 patients implanted with Advanced Bionics HiFocus I, Ij, or Helix arrays. METHODS: ANALYZE imaging software was used to register 3-dimensional image volumes from patients' preoperative and postoperative scans and from a single body donor whose unimplanted ears were scanned clinically, with micro computed tomography and with orthogonal-plane fluorescence optical sectioning (OPFOS) microscopy. By use of this registration, we compared the atlas of OPFOS images of soft tissue within the body donor's cochlea with the bone and fluid/ tissue boundary available in patient scan data to choose the midmodiolar axis position and judge the electrode position in the scala tympani or scala vestibuli, including the distance to the medial and lateral scalar walls. The angular rotation 0 degrees start point is a line joining the midmodiolar axis and the middle of the cochlear canal entry from the vestibule. RESULTS: The group mean array insertion depth was 477 degrees (range, 286 degrees to 655 degrees). The word scores were negatively correlated (r = -0.59; p = .028) with the number of electrodes in the scala vestibuli. CONCLUSIONS: Although the individual variability in all measures was large, repeated patterns of suboptimal electrode placement were observed across subjects, underscoring the applicability of this technique.