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Background: Osteoporosis is the most common metabolic bone disease and can cause fragility fractures. Despite this, screening utilization rates for osteoporosis remain low among populations at risk. Automated bone mineral density (BMD) estimation using computed tomography (CT) can help bridge this gap and serve as an alternative screening method to dual-energy X-ray absorptiometry (DXA). Methods: The feasibility of an opportunistic and population agnostic screening method for osteoporosis using abdominal CT scans without bone densitometry phantom-based calibration was investigated in this retrospective study. A total of 268 abdominal CT-DXA pairs and 99 abdominal CT studies without DXA scores were obtained from an oncology specialty clinic in the Republic of Korea. The center axial CT slices from the L1, L2, L3, and L4 lumbar vertebrae were annotated with the CT slice level and spine segmentation labels for each subject. Deep learning models were trained to localize the center axial slice from the CT scan of the torso, segment the vertebral bone, and estimate BMD for the top four lumbar vertebrae. Results: Automated vertebra-level DXA measurements showed a mean absolute error (MAE) of 0.079, Pearson's r of 0.852 (P<0.001), and R² of 0.714. Subject-level predictions on the held-out test set had a MAE of 0.066, Pearson's r of 0.907 (P<0.001), and R² of 0.781. Conclusion: CT scans collected during routine examinations without bone densitometry calibration can be used to generate DXA BMD predictions. [ABSTRACT FROM AUTHOR]

Background: Chest X-ray (CXR) images are commonly used to show the internal structure of the human body without invasive intervention. The quality of CXR is an important factor as it affects the accuracy of a clinical diagnosis. Unfortunately, it is difficult to always get good quality CXR scans due to noises and scatters. Purpose: Recently, wavelet directional CycleGAN (WavCycleGAN) has shown promising results in image restoration tasks by removing noise and artifacts without sacrificing high-frequency components of the input image. Unfortunately, WavCycleGAN directly reconstructs wavelet directional images that require a wavelet transform in both the training and test phases, resulting in additional processing steps and unnatural artifacts originating from the wavelet domain image.In addition,WavCycleGAN can only process artifact-related subbands, so it is difficult to apply WavCycleGAN when different levels of artifacts are present in all subbands. To address this, here we present a novel unsupervised CXR image restoration scheme with similar or even better artifact removal performance than WavCycleGAN in spite of wavelet transform being only applied in the training phase. Methods: We introduce a novel wavelet subband discriminator which can be combined with CycleGAN or switchable CycleGAN, where wavelet transform is applied only in the training phase for discriminators to match the distribution of wavelet subband components. In our framework, the image restoration network can be still applied in the image domain to prevent unnatural artifacts of the wavelet domain image with the help of the image-domain cycle-consistency loss. In addition, using wavelet subband discriminator makes it possible to remove artifacts in all subbands by utilizing frequency-specific wavelet subband discriminators. Results: Through extensive experiments for noise and scatter removal in CXRs, we confirm that our method provides competitive performance compared to existing approaches without additional processing steps in the test phase. Furthermore, we show that our wavelet subband discriminator combined with the switchable CycleGAN can provide the flexibility by generating different levels of artifact removal. Conclusions: The proposed wavelet subband discriminator can be combined with the existing CycleGAN or switchable CycleGAN structures to construct an efficient unsupervised CXR image reconstruction. The advantage of our wavelet subband discriminator-based CXR image restoration is that, unlike traditional WavCycleGAN, it does not require any additional processing steps in the testing phase and does not generate unnatural artifacts originating from the wavelet domain image. We believe that our wavelet subband discriminator can be applied to various CXR image applications. [ABSTRACT FROM AUTHOR]

Ultrasound (US) imaging is a mature technology that has widespread applications especially in the healthcare sector. Despite its widespread use and popularity, it has an inherent disadvantage that ultrasound images are prone to speckle and other kinds of noise. The image quality in the low-cost ultrasound imaging systems is degraded due to the presence of such noise and low resolution of such ultrasound systems. Herein, we propose a method for image enhancement where, the overall quality of the US images is improved by simultaneous enhancement of US image resolution and noise suppression. To avoid over-smoothing and preserving structural/texture information, we devise texture compensation in our proposed method to retain the useful anatomical features. Moreover, we also utilize US image formation physics knowledge to generate augmentation datasets which can improve the training of our proposed method. Our experimental results showcase the performance of the proposed network as well as the effectiveness of the utilization of US physics knowledge to generate augmentation datasets. [ABSTRACT FROM AUTHOR]

To improve the performance of classical generative adversarial networks (GANs), Wasserstein generative adversarial networks (WGANs) were developed as a Kantorovich dual formulation of the optimal transport (OT) problem using Wasserstein-1 distance. However, it was not clear how CycleGANtype generative models can be derived from the OT theory. Here we show that a novel CycleGAN architecture can be derived as a Kantorovich dual OT formulation if a penalized least squares (PLS) cost with deep learning--based inverse path penalty is used as a transportation cost. One of the most important advantages of this formulation is that depending on the knowledge of the forward problem, distinct variations of CycleGAN architecture can be derived: for example, one with two pairs of generators and discriminators, and the other with only a single pair of generator and discriminator. Even for the two generator cases, we show that the structural knowledge of the forward operator can lead to a simpler generator architecture which significantly simplifies the neural network training. The new CycleGAN formulation, which we call the OT-CycleGAN, has been applied for various biomedical imaging problems, such as accelerated magnetic resonance imaging (MRI), super-resolution microscopy, and low-dose X-ray computed tomography (CT). Experimental results confirm the efficacy and flexibility of the theory. [ABSTRACT FROM AUTHOR]

Recently, deep learning approaches with various network architectures have achieved significant performance improvement over existing iterative reconstruction methods in various imaging problems. However, it is still unclear why these deep learning architectures work for specific inverse problems. Moreover, in contrast to the usual evolution of signal processing theory around the classical theories, the link between deep learning and the classical signal processing approaches, such as wavelets, nonlocal processing, and compressed sensing, are not yet well understood. To address these issues, here we show that the long-sought missing link is the convolution framelets for representing a signal by convolving local and nonlocal bases. The convolution framelets were originally developed to generalize the theory of low-rank Hankel matrix approaches for inverse problems, and this paper further extends this idea so as to obtain a deep neural network using multilayer convolution framelets with perfect reconstruction (PR) under rectified linear unit (ReLU) nonlinearity. Our analysis also shows that the popular deep network components such as residual blocks, redundant filter channels, and concatenated ReLU (CReLU) do indeed help to achieve PR, while the pooling and unpooling layers should be augmented with high-pass branches to meet the PR condition. Moreover, by changing the number of filter channels and bias, we can control the shrinkage behaviors of the neural network. This discovery reveals the limitations of many existing deep learning architectures for inverse problems, and leads us to propose a novel theory for a deep convolutional framelet neural network. Using numerical experiments with various inverse problems, we demonstrate that our deep convolutional framelets network shows consistent improvement over existing deep architectures. This discovery suggests that the success of deep learning stems not from a magical black box, but rather from the power of a novel signal representation using a nonlocal basis combined with a data-driven local basis, which is indeed a natural extension of classical signal processing theory. [ABSTRACT FROM AUTHOR]

BACKGROUND: Cone-beam computed tomography (CBCT) is widely used in various medical imaging applications, including dental examinations. Dental CBCT images often suffer from motion artifacts caused by involuntary rigid motion of patients. However, earlier motion compensation studies are not applicable for dental CBCT systems using truncated detectors. OBJECTIVE: This study proposes a novel motion correction algorithm that can be applied for truncated dental CBCT images. METHODS: We propose a two-step method for motion correction. First, we estimate the relative displacement of each pair of opposite projections by finding the motion vector that maximizes the two-dimensional correlation coefficients of the opposite projections. Second, we convert the relative displacement into the absolute coordinate motion that yields the highest image sharpness of the reconstruction image. Using the motion vectors in the absolute coordinate system, motion artifacts are then compensated by modifying the trajectory of the source and detector during the back-projection step of the image reconstruction process. RESULTS: In simulation, the proposed method successfully estimated the true relative displacement. After converting to the absolute coordinate motions, the motion-compensated image was close to the ground-truth image and exhibited a lower mean-square-error than that of the uncompensated image. The results from the real data experiment also confirmed that the proposed method successfully compensated for the motion artifacts. CONCLUSIONS: The experimental results confirmed that the proposed method was applicable to most dental CBCT systems using a truncated detector without any use of an additional motion tracking system nor prior knowledge. [ABSTRACT FROM AUTHOR]

A robust algorithm is proposed to reconstruct the spatial support and the Lamé parameters of multiple inclusions in a homogeneous background elastic material using a few measurements of the displacement field over a finite collection of boundary points. The algorithm does not require any linearization or iterative update of Green's function but still allows very accurate reconstruction. The breakthrough comes from a novel interpretation of Lippmann–Schwinger type integral representation of the displacement field in terms of unknown densities having common sparse support on the location of inclusions. Accordingly, the proposed algorithm consists of a two-step approach. First, the localization problem is recast as a joint sparse recovery problem that renders the densities and the inclusion support simultaneously. Then, a noise robust constrained optimization problem is formulated for the reconstruction of elastic parameters. An efficient algorithm is designed for numerical implementation using the Multiple Sparse Bayesian Learning (M-SBL) for joint sparse recovery problem and the Constrained Split Augmented Lagrangian Shrinkage Algorithm (C-SALSA) for the constrained optimization problem. The efficacy of the proposed framework is manifested through extensive numerical simulations. To the best of our knowledge, this is the first algorithm tailored for parameter reconstruction problems in elastic media using highly under-sampled data in the sense of Nyquist rate. [ABSTRACT FROM AUTHOR]

We present a methodology for the optimization of sampling schemes in diffuse optical tomography (DOT). The proposed method exploits singular value decomposition (SVD) of the sensitivity matrix, or weight matrix, in DOT. Two mathematical metrics are introduced to assess and determine the optimum source–detector measurement configuration in terms of data correlation and image space resolution. The key idea of the work is to weight each data measurement, or rows in the sensitivity matrix, and similarly to weight each unknown image basis, or columns in the sensitivity matrix, according to their contribution to the rank of the sensitivity matrix, respectively. The proposed metrics offer a perspective on the data sampling and provide an efficient way of optimizing the sampling schemes in DOT. We evaluated various acquisition geometries often used in DOT by use of the proposed metrics. By iteratively selecting an optimal sparse set of data measurements, we showed that one can design a DOT scanning protocol that provides essentially the same image quality at a much reduced sampling. [ABSTRACT FROM AUTHOR]

To address the classic interior tomography problem where projections at each view extend only to the shadow of a circular region completely interior to the subject being scanned, previously we showed that the exact recovery of two- and three-dimensional piecewise smooth images is guaranteed using a one-dimensional generalized total variation seminorm penalty which allows a much faster reconstruction. Tofurther accelerate the algorithm up to a level for clinical use, this paper proposes a novel multiscale reconstruction method by exploiting the Bedrosian identity of the Hilbert transform. More specifically, we show that the high frequency parts of the one-dimensional signals can be quickly recovered analytically with the Hilbert transform because of the Bedrosian identity. This implies that computationally expensive iterative reconstruction need only be applied to low resolution images in the downsampled domain, which significantly reduces the computational burden. Moreover, even for incomplete trajectories such as circular cone-beam geometry, we demonstrate that the proposed multiscale interior tomography approach can be combined with a novel spectral blending method in order to mitigate cone-beam artifacts from missing frequency regions. We show the efficacy of the proposed multiscale algorithm using circular fan-beam, helical cone-beam data, and circular conebeam geometry. With a graphics processing unit implementation, we demonstrate that the speed of the algorithm can be significantly accelerated up to the level for clinical use for various acquisition geometries. [ABSTRACT FROM AUTHOR]

Motivated by the interior tomography problem, we propose a method for exact reconstruction of a region of interest of a function from its local Radon transform in any number of dimensions. Our aim is to verify the feasibility of a one-dimensional reconstruction procedure that can provide the foundation for an efficient algorithm. For a broad class of functions, including piecewise polynomials and generalized splines, we prove that an exact reconstruction is possible by minimizing a generalized total variation seminorm along lines. The main difference with previous works is that our approach is inherently one-dimensional and that it imposes less constraints on the class of admissible signals. Within this formulation, we derive unique reconstruction results using properties of the Hilbert transform, and we present numerical examples of the reconstruction. [ABSTRACT FROM AUTHOR]

Super resolution microscopy such as STORM and (F)PALM is now a well known method for biological studies at the nanometer scale. However, conventional imaging schemes based on sparse activation of photo-switchable fluorescent probes have inherently slow temporal resolution which is a serious limitation when investigating live-cell dynamics. Here, we present an algorithm for high-density super-resolution microscopy which combines a sparsity-promoting formulation with a Taylor series approximation of the PSF. Our algorithm is designed to provide unbiased localization on continuous space and high recall rates for high-density imaging, and to have orders-of-magnitude shorter run times compared to previous high-density algorithms. We validated our algorithm on both simulated and experimental data, and demonstrated live-cell imaging with temporal resolution of 2.5 seconds by recovering fast ER dynamics. [ABSTRACT FROM AUTHOR]

Structured illumination microscopy (SIM) breaks the optical diffraction limit by illuminating a sample with a series of line-patterned light. Recently, in order to alleviate the requirement of precise knowledge of illumination patterns, structured illumination microscopy techniques using speckle patterns have been proposed. However, these methods require stringent assumptions of the speckle statistics: for example, speckle patterns should be nearly incoherent or their temporal average should be roughly homogeneous. Here, we present a novel speckle illumination microscopy technique that overcomes the diffraction limit by exploiting the minimal requirement that is common for all the existing super-resolution microscopy, i.e. that the fluorophore locations do not vary during the acquisition time. Using numerical and real experiments, we demonstrate that the proposed method can improve the resolution up to threefold. Because our proposed method succeeds for standard fluorescence probes and experimental protocols, it can be applied in routine biological experiments. [ABSTRACT FROM AUTHOR]

Compressed sensing has become an extensive research area in MR community because of the opportunity for unprecedented high spatio-temporal resolution reconstruction. Because dynamic magnetic resonance imaging (MRI) usually has huge redundancy along temporal direction, compressed sensing theory can be effectively used for this application. Historically, exploiting the temporal redundancy has been the main research topics in video compression technique. This article compares the similarity and differences of compressed sensing dynamic MRI and video compression and discusses what MR can learn from the history of video compression research. In particular, we demonstrate that the motion estimation and compensation in video compression technique can be also a powerful tool to reduce the sampling requirement in dynamic MRI. Theoretical derivation and experimental results are presented to support our view. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 81–98, 2010 [ABSTRACT FROM AUTHOR]

Recent theory of compressed sensing informs us that near-exact recovery of an unknown sparse signal is possible from a very limited number of Fourier samples by solving a convex L1 optimization problem. The main contribution of the present letter is a compressed sensing-based novel nonparametric shape estimation framework and a computational algorithm for binary star shape objects, whose radius functions belong to the space of bounded-variation functions. Specifically, in contrast with standard compressed sensing, the present approach involves directly reconstructing the shape boundary under sparsity constraint. This is done by converting the standard pixel-based reconstruction approach into estimation of a nonparametric shape boundary on a wavelet basis. This results in an L1 minimization under a nonlinear constraint, which makes the optimization problem nonconvex. We solve the problem by successive linearization and application of one-dimensional Lx minimization, which significantly reduces the number of sampling requirements as well as the computational burden. Fourier imaging simulation results demonstrate that high quality reconstruction can be quickly obtained from a very limited number of samples. Furthermore, the algorithm outperforms the standard compressed sensing reconstruction approach using the total variation norm. [ABSTRACT FROM AUTHOR]

This paper derives fundamental performance bounds for statistical estimation of parametric surfaces embedded in R3. Unlike conventional pixel-based image reconstruction approaches, our problem is reconstruction of the shape of binary or homogeneous objects. The fundamental uncertainty of such estimation problems can be represented by global confidence regions, which facilitate geometric inference and optimization of the imaging system. Compared to our previous work on global confidence region analysis for curves [two-dimensional (2-D) shapes], computation of the probability that the entire surface estimate lies within the confidence region is more challenging because a surface estimate is an inhomogeneous random field continuously indexed by a 2-D variable. We derive an asymptotic lower bound to this probability by relating it to the exceedence probability of a higher dimensional Gaussian random field, which can, in turn, be evaluated using the tube formula due to Sun. Simulation results demonstrate the tightness of the resulting bound and the usefulness of the three-dimensional global confidence region approach. [ABSTRACT FROM AUTHOR]

We address the problem of computing fundamental performance bounds for estimation of object boundaries from noisy measurements in inverse problems, when the boundaries are parameterized by a finite number of unknown variables. Our model applies to multiple unknown objects, each with its own unknown gray level, or color, and boundary parameterization, on an arbitrary known background. While such fundamental bounds on the performance of shape estimation algorithms can in principle be derived from the Cramér-Rao lower bounds, very few results have been reported due to the difficulty of computing the derivatives of a functional with respect to shape deformation. In this paper, we provide a general formula for computing CramérRao lower bounds in inverse problems where the observations are related to the object by a general linear transform, followed by a possibly nonlinear and noisy measurement system. As an illustration, we derive explicit formulas for computed tomography, Fourier imaging, and deconvolution problems. The bounds reveal that highly accurate parametric reconstructions are possible in these examples, using severely limited and noisy data. [ABSTRACT FROM AUTHOR]

Presents methods for computing performance limits for two-dimensional parametric shape estimation in nonlinear inverse scattering problems with an application to passive radar imaging. Analysis for nonlinear inverse scattering problems using Cramer-Rao lower bounds; Use of normalized Fourier descriptors for boundary parameterization; Extension of the global confidence region analysis using transverse electric polarized incident plane waves.

Presents a study which introduced confidence region techniques for analyzing and visualizing the performance of two-dimensional parametric shape estimators. Problem formulation; Results; Extensions and applications.

The purpose of this paper is to propose a non-iterative method for the inverse conductivity problem of recovering multiple small anomalies from the boundary measurements. When small anomalies are buried in a conducting object, the electric potential values inside the object can be expressed by integrals of densities with a common sparse support on the location of anomalies. Based on this integral expression, we formulate the reconstruction problem of small anomalies as a joint sparse recovery and present an efficient non-iterative recovery algorithm of small anomalies. Furthermore, we also provide a slightly modified algorithm to reconstruct an extended anomaly. We validate the effectiveness of the proposed algorithm over the linearized method and the multiple signal classification algorithm by numerical simulations. [ABSTRACT FROM AUTHOR]

Dynamic positron emission tomography (PET) is widely used to measure changes in the bio-distribution of radiopharmaceuticals within particular organs of interest over time. However, to retain sufficient temporal resolution, the number of photon counts in each time frame must be limited. Therefore, conventional reconstruction algorithms such as the ordered subset expectation maximization (OSEM) produce noisy reconstruction images, thus degrading the quality of the extracted time activity curves (TACs). To address this issue, many advanced reconstruction algorithms have been developed using various spatio-temporal regularizations. In this paper, we extend earlier results and develop a novel temporal regularization, which exploits the self-similarity of patches that are collected in dynamic images. The main contribution of this paper is to demonstrate that the correlation of patches can be exploited using a low-rank constraint that is insensitive to global intensity variations. The resulting optimization framework is, however, non-Lipschitz and non-convex due to the Poisson log-likelihood and low-rank penalty terms. Direct application of the conventional Poisson image deconvolution by an augmented Lagrangian (PIDAL) algorithm is, however, problematic due to its large memory requirements, which prevents its parallelization. Thus, we propose a novel optimization framework using the concave-convex procedure (CCCP) by exploiting the Legendre–Fenchel transform, which is computationally efficient and parallelizable. In computer simulation and a real in vivo experiment using a high-resolution research tomograph (HRRT) scanner, we confirm that the proposed algorithm can improve image quality while also extracting more accurate region of interests (ROI) based kinetic parameters. Furthermore, we show that the total reconstruction time for HRRT PET is significantly accelerated using our GPU implementation, which makes the algorithm very practical in clinical environments. [ABSTRACT FROM AUTHOR]