Diffusion Prior Regularized Iterative Reconstruction for Low-dose CT.

Computed tomography (CT) involves a patient's exposure to ionizing radiation. To reduce the radiation dose, we can either lower the X-ray photon count or down-sample projection views. However, either of the ways often compromises image quality.

Fluorescence molecular tomography for quantum yield and lifetime.

Fluorescence molecular tomography (FMT) is a promising modality for noninvasive imaging of internal fluorescence agents in biological tissues, especially in small animal models, with applications in diagnosis, therapy, and drug design. In this paper, we present a fluorescent reconstruction algorithm that combines time-resolved fluorescence imaging data with photon-counting microcomputed tomography (PCMCT) images to estimate the quantum yield and lifetime of fluorescent markers in a mouse model. By incorporating PCMCT images, a permissible region of interest of fluorescence yield and lifetime can be roughly estimated as prior knowledge, reducing the number of unknown variables in the inverse problem and improving the image reconstruction stability.

Tomographic Image Reconstruction Using an Advanced Score Function (ADSF).

Computed tomography (CT) reconstructs volumetric images using X-ray projection data acquired from multiple angles around an object. For low-dose or sparse-view CT scans, the classic image reconstruction algorithms often produce severe noise and artifacts. To address this issue, we develop a novel iterative image reconstruction method based on maximum a posteriori (MAP) estimation.

Fluorescence Molecular Tomography for Quantum Yield and Lifetime.

Fluorescence molecular tomography (FMT) is a promising modality for non-invasive imaging of internal fluorescence agents in biological tissues especially in small animal models, with applications in diagnosis, therapy, and drug design. In this paper, we present a new fluorescent reconstruction algorithm that combines time-resolved fluorescence imaging data with photon-counting micro-CT (PCMCT) images to estimate the quantum yield and lifetime of fluorescent markers in a mouse model. By incorporating PCMCT images, a permissible region of interest of fluorescence yield and lifetime can be roughly estimated as prior knowledge, reducing the number of unknown variables in the inverse problem and improving image reconstruction stability.

Preliminary landscape analysis of deep tomographic imaging patents.

Over recent years, the importance of the patent literature has become increasingly more recognized in the academic setting. In the context of artificial intelligence, deep learning, and data sciences, patents are relevant to not only industry but also academe and other communities. In this article, we focus on deep tomographic imaging and perform a preliminary landscape analysis of the related patent literature.

Estimating optical parameters of biological tissues with photon-counting micro-CT.

Wavelength-dependent absorption and scattering properties determine the fluorescence photon transport in biological tissues and image resolution of optical molecular tomography. Currently, these parameters are computed from optically measured data. For small animal imaging, estimation of optical parameters is a large-scale optimization problem, which is highly ill-posed.

Low-dimensional Manifold Constrained Disentanglement Network for Metal Artifact Reduction.

Deep neural network based methods have achieved promising results for CT metal artifact reduction (MAR), most of which use many synthesized paired images for supervised learning. As synthesized metal artifacts in CT images may not accurately reflect the clinical counterparts, an artifact disentanglement network (ADN) was proposed with unpaired clinical images directly, producing promising results on clinical datasets. However, as the discriminator can only judge if large regions semantically look artifact-free or artifact-affected, it is difficult for ADN to recover small structural details of artifact-affected CT images based on adversarial losses only without sufficient constraints.

Projection decomposition via univariate optimization for dual-energy CT.

Dual-energy computed tomography (DECT) acquires two x-ray projection datasets with different x-ray energy spectra, performs material-specific image reconstruction based on the energy-dependent non-linear integral model, and provides more accurate quantification of attenuation coefficients than single energy spectrum CT. In the diagnostic energy range, x-ray energy-dependent attenuation is mainly caused by photoelectric absorption and Compton scattering. Theoretically, these two physical components of the x-ray attenuation mechanism can be determined from two projection datasets with distinct energy spectra.

Stabilizing deep tomographic reconstruction: Part A. Hybrid framework and experimental results.

A recent PNAS paper reveals that several popular deep reconstruction networks are unstable. Specifically, three kinds of instabilities were reported: (1) strong image artefacts from tiny perturbations, (2) small features missed in a deeply reconstructed image, and (3) decreased imaging performance with increased input data. Here, we propose an analytic compressed iterative deep (ACID) framework to address this challenge.

Stabilizing deep tomographic reconstruction: Part B. Convergence analysis and adversarial attacks.

Due to lack of the kernel awareness, some popular deep image reconstruction networks are unstable. To address this problem, here we introduce the bounded relative error norm (BREN) property, which is a special case of the Lipschitz continuity. Then, we perform a convergence study consisting of two parts: (1) a heuristic analysis on the convergence of the analytic compressed iterative deep (ACID) scheme (with the simplification that the CS module achieves a perfect sparsification), and (2) a mathematically denser analysis (with the two approximations: [1] A is viewed as an inverse A in the perspective of an iterative reconstruction procedure and [2] a pseudo-inverse is used for a total variation operator H).

Phase function estimation from a diffuse optical image via deep learning.

The phase function is a key element of a light propagation model for Monte Carlo (MC) simulation, which is usually fitted with an analytic function with associated parameters. In recent years, machine learning methods were reported to estimate the parameters of the phase function of a particular form such as the Henyey-Greenstein phase function but, to our knowledge, no studies have been performed to determine the form of the phase function.Here we design a convolutional neural network (CNN) to estimate the phase function from a diffuse optical image without any explicit assumption on the form of the phase function.

Feasibility analysis on simultaneous electron density and attenuation coefficient reconstruction.

Purpose: Creating a viable reconstruction method for Compton scatter tomography remains challenging. Accounting for scatter attenuation when the underlying attenuation map is not known is particularly challenging, and current mathematical approaches to this vary widely. This work explores a novel approach to joint scatter and attenuation image reconstruction, which leverages the underlying structural similarity between the two images and incorporates a deep learning model in an alternating iterative reconstruction scheme.

Deep Interactive Denoiser (DID) for X-Ray Computed Tomography.

Low-dose computed tomography (LDCT) is desirable for both diagnostic imaging and image-guided interventions. Denoisers are widely used to improve the quality of LDCT. Deep learning (DL)-based denoisers have shown state-of-the-art performance and are becoming mainstream methods.

Monochromatic image reconstruction via machine learning.

X-ray computed tomography (CT) is a nondestructive imaging technique to reconstruct cross-sectional images of an object using x-ray measurements taken from different view angles for medical diagnosis, therapeutic planning, security screening, and other applications. In clinical practice, the x-ray tube emits polychromatic x-rays, and the x-ray detector array operates in the energy-integrating mode to acquire energy intensity. This physical process of x-ray imaging is accurately described by an energy-dependent non-linear integral equation on the basis of the Beer-Lambert law.

Focused x-ray luminescence imaging system for small animals based on a rotary gantry.

Significance: The ability to detect and localize specific molecules through tissue is important for elucidating the molecular basis of disease and treatment. Unfortunately, most current molecular imaging tools in tissue either lack high spatial resolution (e.g.

X-ray luminescence imaging for small animals.

X-ray luminescence imaging emerged for about a decade and combines both the high spatial resolution of x-ray imaging with the high measurement sensitivity of optical imaging, which could result in a great molecular imaging tool for small animals. So far, there are two types of x-ray luminescence computed tomography (XLCT) imaging. One uses a pencil beam x-ray for high spatial resolution at a cost of longer measurement time.

Virtual Monoenergetic CT Imaging via Deep Learning.

Conventional single-spectrum computed tomography (CT) reconstructs a spectrally integrated attenuation image and reveals tissues morphology without any information about the elemental composition of the tissues. Dual-energy CT (DECT) acquires two spectrally distinct datasets and reconstructs energy-selective (virtual monoenergetic [VM]) and material-selective (material decomposition) images. However, DECT increases system complexity and radiation dose compared with single-spectrum CT.

Deep Efficient End-to-end Reconstruction (DEER) Network for Few-view Breast CT Image Reconstruction.

Breast CT provides image volumes with isotropic resolution in high contrast, enabling detection of small calcification (down to a few hundred microns in size) and subtle density differences. Since breast is sensitive to x-ray radiation, dose reduction of breast CT is an important topic, and for this purpose, few-view scanning is a main approach. In this article, we propose a Deep Efficient End-to-end Reconstruction (DEER) network for few-view breast CT image reconstruction.

Clinical Micro-CT Empowered by Interior Tomography, Robotic Scanning, and Deep Learning.

While micro-CT systems are instrumental in preclinical research, clinical micro-CT imaging has long been desired with cochlear implantation as a primary application. The structural details of the cochlear implant and the temporal bone require a significantly higher image resolution than that (about 0.2 ) provided by current medical CT scanners.

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.

CT IMAGE RECONSTRUCTION ON A LOW DIMENSIONAL MANIFOLD.

The patch manifold of a natural image has a low dimensional structure and accommodates rich structural information. Inspired by the recent work of the low-dimensional manifold model (LDMM), we apply the LDMM for regularizing X-ray computed tomography (CT) image reconstruction. This proposed method recovers detailed structural information of images, significantly enhancing spatial and contrast resolution of CT images.

A Reconfigurable energy-resolving method for a layered edge-on detector.

Currently, most of the x-ray spectral detectors can extract signals in a set number of energy bins, that inevitably reduces the dynamic range and energy resolution of the imaging system. Inspired by the idea of dynamic thresholding, we previously proposed a pixel architecture and an energy-resolving method for layered edge-on detector. However, the complicated energy exchange mechanism of x-rays in the detector that ultimately affects the practical applications of the layered detectors had not been previously considered.

Novel Detection Scheme for X-ray Small-Angle Scattering.

X-ray imaging techniques, including x-ray radiography and computed tomography, have been in use for decades and proven effective and indispensable in diagnosis and therapy due to their fine resolution and fast acquisition speed. However, the innate disadvantage of x-ray is the poor soft tissue contrast. Small-angle scattering signals were shown to provide unique information about the abnormality of soft tissues that is complementary to the traditional attenuation image.

Simultaneous Emission-Transmission Tomography in an MRI Hardware Framework.

Multi-modality imaging is essential for diagnosis and therapy in challenging cases. A Holy Grail of medical imaging is a hybrid imaging system combining computed tomography (CT), nuclear imaging, and magnetic resonance imaging (MRI) to deliver registered morphological, functional, and cellular/molecular information simultaneously and quantitatively for precision medicine. Recently, a unique imaging approach was demonstrated that combines nuclear imaging with polarized radiotracers and MRI-based spatial encoding.

3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network.

Low-dose computed tomography (LDCT) has attracted major attention in the medical imaging field, since CT-associated X-ray radiation carries health risks for patients. The reduction of the CT radiation dose, however, compromises the signal-to-noise ratio, which affects image quality and diagnostic performance. Recently, deep-learning-based algorithms have achieved promising results in LDCT denoising, especially convolutional neural network (CNN) and generative adversarial network (GAN) architectures.