Tools to simulate lower dose, noisy computed tomography (CT) images from existing data enable protocol optimization by quantifying the trade-off between patient dose and image quality. Many studies have developed and validated noise insertion techniques; however, most of these tools operate on proprietary projection data which can be difficult to access and can be time consuming when a large number of realizations is needed. In response, this work aims to develop and validate an image domain approach to accurately insert CT noise and simulate low dose scans. In this framework, information from the image is utilized to estimate the variance map and local noise power spectra (NPS). Normally distributed noise is filtered within small patches in the image domain using the inverse Fourier transform of the square root of the estimated local NPS to generate noise with the appropriate spatial correlation. The patches are overlapped and element-wise multiplied by the standard deviation map to produce locally varying, spatially correlated noise. The resulting noise image is scaled based on the relationship between the initial and desired dose and added to the original image. The results demonstrate excellent agreement between traditional projection domain methods and the proposed method, both for simulated and real data sets. This new framework is not intended to replace projection domain methods; rather, it fills a gap in CT noise simulation tools and is an accurate alternative when projection domain methods are not practical, for example, in large scale repeatability or detectability studies.

Charge sharing, scatter, and fluorescence events in a photon counting detector can result in counting of a single incident photon in multiple neighboring pixels, each at a fraction of the true energy. This causes energy distortion and correlation of data across energy bins in neighboring pixels (spatio-energy correlation), with the severity depending on the detector pixel size and detector material. If a "macro-pixel" is formed by combining the counts from multiple adjacent small pixels, it will exhibit correlations across its energy bins. Understanding these effects can be crucial for detector design and for model-based imaging applications. This paper investigates the impact of these effects in basis material and effective monoenergetic estimates using the Cramér-Rao Lower Bound. To do so, we derive a correlation model for the multi-counting events. CdTe detectors with grids of pixels with side length of $250~\mu \text{m}$ , $500~\mu \text{m}$ , and 1 mm were compared, with binning of $4\times4$ , $2\times2$ , and $1\times1$ pixels, respectively, to keep the same net 1 mm2 aperture constant. The same flux was applied to each. The mean and covariance matrix of measured photon counts were derived analytically using spatio-energy response functions precomputed from Monte Carlo simulations. Our results show that a 1 mm2 macro-pixel with $250\times 250\,\,\mu \text{m}^{\textsf {2}}$ sub-pixels shows 35% higher standard deviation than a single 1 mm2 pixel for material-specific imaging, while the penalty for effective monoenergetic imaging is <10% compared with a single 1 mm $^{\textsf {2}}$ pixel. Potential benefits of sub-pixels (higher spatial resolution and lower pulse pile-up effects) are important but were not investigated here.

Digital tomosynthesis is a three-dimensional imaging technique with a lower radiation dose than computed tomography (CT). Due to the missing data in tomosynthesis systems, out-of-plane structures in the depth direction cannot be completely removed by the reconstruction algorithms. In this work, we analyzed the impulse responses of common tomosynthesis systems on a plane-to-plane basis and proposed a fast and accurate convolution-based blur-and-add (BAA) model to simulate the backprojected images. In addition, the analysis formalism describing the impulse response of out-of-plane structures can be generalized to both rotating and parallel gantries. We implemented a ray tracing forward projection and backprojection (ray-based model) algorithm and the convolution-based BAA model to simulate the shift-and-add (backproject) tomosynthesis reconstructions. The convolution-based BAA model with proper geometry distortion correction provides reasonably accurate estimates of the tomosynthesis reconstruction. A numerical comparison indicates that the simulated images using the two models differ by less than 6% in terms of the root-mean-squared error. This convolution-based BAA model can be used in efficient system geometry analysis, reconstruction algorithm design, out-of-plane artifacts suppression, and CT-tomosynthesis registration.

Energy-discriminating, photon counting (EDPC) detectors have high potential in spectral imaging applications but exhibit degraded performance when the incident count rate approaches or exceeds the characteristic count rate of the detector. In order to reduce the requirements on the detector, we explore the strategy of modulating the X-ray flux field using a recently proposed dynamic, piecewise-linear attenuator. A previous paper studied this modulation for photon counting detectors but did not explore the impact on spectral applications. In this work, we modeled detection with a bipolar triangular pulse shape (Taguchi et al., 2011) and estimated the Cramer-Rao lower bound (CRLB) of the variance of material selective and equivalent monoenergetic images, assuming deterministic errors at high flux could be corrected. We compared different materials for the dynamic attenuator and found that rare earth elements, such as erbium, outperformed previously proposed materials such as iron in spectral imaging. The redistribution of flux reduces the variance or dose, consistent with previous studies on benefits with conventional detectors. Numerical simulations based on DICOM datasets were used to assess the impact of the dynamic attenuator for detectors with several different characteristic count rates. The dynamic attenuator reduced the peak incident count rate by a factor of 4 in the thorax and 44 in the pelvis, and a 10 Mcps/mm (2) EDPC detector with dynamic attenuator provided generally superior image quality to a 100 Mcps/mm (2) detector with reference bowtie filter for the same dose. The improvement is more pronounced in the material images.

It is well known that the energy dependence of X-ray attenuation can be used to characterize materials. Yet, even with energy discriminating photon counting X-ray detectors, it is still unclear how to best form energy dependent measurements for spectral imaging. Common ideas include binning photon counts based on their energies and detectors with both photon counting and energy integrating electronics. These approaches can be generalized to energy weighted measurements, which we prove can form a sufficient statistic for spectral X-ray imaging if the weights used, which we term μ-weights, are basis attenuation functions that can also be used for material decomposition. To study the performance of these different methods, we evaluate the Cramér-Rao lower bound (CRLB) of material estimates in the presence of quantum noise. We found that the choice of binning and weighting schemes can greatly affect the performance of material decomposition. Even with optimized thresholds, binning condenses information but incurs penalties to decomposition precision and is not robust to changes in the source spectrum or object size, although this can be mitigated by adding more bins or removing photons of certain energies from the spectrum. On the other hand, because μ-weighted measurements form a sufficient statistic for spectral imaging, the CRLB of the material decomposition estimates is identical to the quantum noise limited performance of a system with complete energy information of all photons. Finally, we show that μ-weights lead to increased conspicuity over other methods in a simulated calcium contrast experiment.

Image-guided minimally invasive procedures have made a substantial impact in improving patient management, reducing the cost, morbidity, and mortality of treatments and making therapies available to patients who would otherwise have no option. X-ray fluoroscopy and magnetic resonance imaging (MRI) are two powerful tools for guiding interventional procedures but with very different strengths and weaknesses. X-ray fluoroscopy offers very high spatial and temporal resolution and is excellent for guiding and deploying devices. MRI offers tomographic imaging with complete freedom of plane orientation, outstanding soft tissue discrimination, and the ability to portray physiological responses during treatment. We have shown that it is feasible to fully integrate an X-ray fluoroscopy system into the bore of an interventional MR scanner to provide a single congruent field of view, with integration requiring minor modifications to the flat-panel digital detector, and using a static-anode X-ray tube. Given the limited availability of the MR scanner platform (0.5T GE Signa SP magnet), and the X-ray fluence limitations of the static-anode X-ray tube, we are now investigating the technology developments required to place a rotating-anode digital flat-panel X-ray system immediately adjacent to a closed-bore MRI system. These types of hybrid systems could have enormous impact in the diagnosis and treatment of oncologic, cardiovascular, and other disorders.

Many imaging experiments involve acquiring a time series of images. To improve imaging speed, several "data-sharing" methods have been proposed, which collect one (or a few) high-resolution reference(s) and a sequence of reduced data sets. In image reconstruction, two methods, known as "Keyhole" and reduced-encoding imaging by generalized-series reconstruction (RIGR), have been used. Keyhole fills in the unmeasured high-frequency data simply with those from the reference data set(s), whereas RIGR recovers the unmeasured data using a generalized series (GS) model, of which the basis functions are constructed based on the reference image(s). This correspondence presents a fast algorithm (and two extensions) for GS-based image reconstruction. The proposed algorithms have the same computational complexity as the Keyhole algorithm, but are more capable of capturing high-resolution dynamic signal changes.

We present a method (DMESH) for nonrigid cyclic motion analysis using a series of velocity images covering the cycle acquired, for example, from phase-contrast magnetic resonance imaging. The method is based on fitting a dynamic finite-element mesh model to velocity samples of an extended region, at all time frames. The model offers a flexible tradeoff between accuracy and reproducibility with controllable built-in spatiotemporal smoothing, which is determined by the fineness of the initially defined mesh and the richness of included Fourier harmonics. The method can further provide a prediction of the analysis reproducibility, along with the estimated motion and deformation quantities. Experiments have been conducted to validate the method and to verify the reproducibility prediction. Use of the method for motion analysis using displacement information (e.g., from magnetic resonance tagging) has also been explored.

Presents a method for nonrigid cyclic motion analysis using a series of velocity images, acquired for example, from phase contrast magnetic resonance imaging. The method is based on fitting a global spatiotemporal finite element mesh model, which has controllable builtin spatial/temporal smoothing that is determined by the finesse of the initially defined mesh and richness of included Fourier harmonics, to velocity samples of an extended region at all time frames. If desired, the method can further provide a prediction of the analysis reproducibility along with the estimated motion and deformation quantities. Experiments have been conducted to validate the method and verify the reproducibility prediction.

Phase contrast magnetic resonance imaging (MRI) can provide in vivo myocardial velocity field measurements. These data allow densely spaced material points to be tracked throughout the whole heart cycle using, for example, the Fourier tracking algorithm. To process the tracking results for myocardial deformation and strain quantification, we developed a method that is based on fitting the tracking results to an appropriate local deformation model. We further analyzed the accuracy and precision of the method and provided performance predictions for several local models. In order to validate the method and the theoretical performance analysis, we conducted controlled computer simulations and a phantom study. The results agreed well with expectations. Human heart data were also acquired and analyzed, and provided encouraging results. At the signal-to-noise ratio (SNR) level and spatial resolution expected in clinical settings, the study predicts strain quantification accuracy and precision that may allow the technique to become a practical and powerful noninvasive approach for the study of cardiac function, although clinically acceptable data acquisition strategies for three-dimensional (3-D) data are still a challenge.

The authors have developed a technique based on a solution of the Poisson equation to unwrap the phase in magnetic resonance (MR) phase images. The method is based on the assumption that the magnitude of the inter-pixel phase change is less than pi per pixel. Therefore, the authors obtain an estimate of the phase gradient by "wrapping" the gradient of the original phase image. The problem is then to obtain the absolute phase given the estimate of the phase gradient. The least-squares (LS) solution to this problem is shown to be a solution of the Poisson equation allowing the use of fast Poisson solvers. The absolute phase is then obtained by mapping the LS phase to the nearest multiple of 2 K from the measured phase. The proposed technique is evaluated using MR phase images and is proven to be robust in the presence of noise. An application of the proposed method to the 3-point Dixon technique for water and fat separation is demonstrated.