Spatiotemporal denoising of low-dose cardiac CT image sequences using RecycleGAN.

Electrocardiogram (ECG)-gated multi-phase computed tomography angiography (MP-CTA) is frequently used for diagnosis of coronary artery disease. Radiation dose may become a potential concern as the scan needs to cover a wide range of cardiac phases during a heart cycle. A common method to reduce radiation is to limit the full-dose acquisition to a predefined range of phases while reducing the radiation dose for the rest.

Calibration of spatially modulated snapshot imaging polarimeter based on phase-shift interferometry.

The snapshot imaging polarimeters (SIPs) using spatial modulation have gained increasing popularity due to their capability of obtaining all four Stokes parameters in a single measurement. However, the existing reference beam calibration techniques cannot extract the modulation phase factors of the spatially modulated system. In this paper, a calibration technique based on a phase-shift interference (PSI) theory is proposed to address this issue.

Snapshot imaging Mueller matrix polarimeter using modified Savart polariscopes.

We present a snapshot imaging Mueller matrix polarimeter using modified Savart polariscopes (MSP-SIMMP). The MSP-SIMMP contains both the polarizing optics and the analyzing optics encoding all Mueller matrix components of the sample into the interferogram by the spatial modulation technique. An interference model and the methods of reconstruction and calibration are discussed.

Texture transformer super-resolution for low-dose computed tomography.

Computed tomography (CT) is widely used to diagnose many diseases. Low-dose CT has been actively pursued to lower the ionizing radiation risk. A relatively smoother kernel is typically used in low-dose CT to suppress image noise, which may sacrifice spatial resolution.

Prediction of dMRI signals with neural architecture search.

Background: There is growing interest in the neuroscience community in estimating and mapping microscopic properties of brain tissue non-invasively using magnetic resonance measurements. Machine learning methods are actively investigated to predict the signals measured in diffusion magnetic resonance imaging (dMRI).

New Method: We applied the neural architecture search (NAS) to train a recurrent neural network to generate a multilayer perceptron to predict the dMRI data of unknown signals based on the different acquisition parameters and training data.

General simultaneous motion estimation and image reconstruction (G-SMEIR).

To achieve better performance for 4D multi-frame reconstruction with the parametric motion model (MF-PMM), a general simultaneous motion estimation and image reconstruction (G-SMEIR) method is proposed. In G-SMEIR, projection domain motion estimation and image domain motion estimation are performed alternatively to achieve better 4D reconstruction. This method can mitigate the local optimum trapping problem in either domain.

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: Chronicles of the MEMENTO challenge.

Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties.

Carbon-11 and Carbon-12 beam range verifications through prompt gamma and annihilation gamma measurements: Monte Carlo simulations.

Range uncertainty remains a big concern in particle therapy, as it may cause target dose degradation and normal tissue overdosing. Positron emission tomography (PET) and prompt gamma imaging (PGI) are two promising modalities for range verification. However, the relatively long acquisition time of PET and the relatively low yield of PGI pose challenges for real-time range verification.

Investigation of Low-Dose CT Image Denoising Using Unpaired Deep Learning Methods.

Low-dose computed tomography (LDCT) is desired due to prevalence and ionizing radiation of CT, but suffers elevated noise. To improve LDCT image quality, an image-domain denoising method based on cycle-consistent generative adversarial network ("CycleGAN") is developed and compared with two other variants, IdentityGAN and GAN-CIRCLE. Different from supervised deep learning methods, these unpaired methods can effectively learn image translation from the low-dose domain to the full-dose (FD) domain without the need of aligning FDCT and LDCT images.

HRSPET: a 0.5 mm resolution high-sensitivity small-animal PET scanner, a simulation study.

With the goal of developing a total-body small-animal PET system with a high spatial resolution of ∼0.5 mm and a high sensitivity >10% for mouse/rat studies, we simulated four scanners using the graphical processing unit-based Monte Carlo simulation package (gPET) and compared their performance in terms of spatial resolution and sensitivity. We also investigated the effect of depth-of-interaction (DOI) resolution on the spatial resolution.

Single source precursor synthesized CuS nanoparticles for NIR phototherapy of cancer and photodegradation of organic carcinogen.

Herein, we report cost effective and body compatible CuS nanoparticles (NPs) derived from a single source precursor as photothermal agent for healing deep cancer and photocatalytic remediation of organic carcinogens. These NPs efficiently kill MCF7 cells (both in vivo and in vitro) under NIR irradiation by raising the temperature of tumor cells. Such materials can be used for the treatment of deep cancer as they can produce a heating effect using high wavelength and deeply penetrating NIR radiation.

gPET: a GPU-based, accurate and efficient Monte Carlo simulation tool for PET.

Monte Carlo (MC) simulation method plays an essential role in the refinement and development of positron emission tomography (PET) systems. However, most existing MC simulation packages suffer from long execution time for practical PET simulations. To fully address this issue, we developed and validated gPET, a graphics processing unit (GPU)-based MC simulation tool for PET.

Thermal Image Enhancement through the Deconvolution Methods for Low-Cost Infrared Cameras.

An infrared (IR) thermal camera may provide a tool for real-time temperature monitoring for precise disease treatment using heat generated by light-induced photosensitisers, i.e. photothermal/ablation therapies.

4D cone-beam computed tomography (CBCT) using a moving blocker for simultaneous radiation dose reduction and scatter correction.

Four-dimensional (4D) x-ray cone-beam computed tomography (CBCT) is important for a precise radiation therapy for lung cancer. Due to the repeated use and 4D acquisition over a course of radiotherapy, the radiation dose becomes a concern. Meanwhile, the scatter contamination in CBCT deteriorates image quality for treatment tasks.

Acquisition of a full-resolution image and aliasing reduction for a spatially modulated imaging polarimeter with two snapshots.

A snapshot imaging polarimeter using spatial modulation can encode four Stokes parameters allowing instantaneous polarization measurement from a single interferogram. However, the reconstructed polarization images could suffer a severe aliasing signal if the high-frequency component of the intensity image is prominent and occurs in the polarization channels, and the reconstructed intensity image also suffers reduction of spatial resolution due to low-pass filtering. In this work, a method using two anti-phase snapshots is proposed to address the two problems simultaneously.

Predication of different stages of Alzheimer's disease using neighborhood component analysis and ensemble decision tree.

Background: There is a spectrum of the progression from healthy control (HC) to mild cognitive impairment (MCI) without conversion to Alzheimer's disease (AD), to MCI with conversion to AD (cMCI), and to AD. This study aims to predict the different disease stages using brain structural information provided by magnetic resonance imaging (MRI) data.

New Method: The neighborhood component analysis (NCA) is applied to select most powerful features for prediction.

Robust moving-blocker scatter correction for cone-beam computed tomography using multiple-view information.

Scatter contamination is one of the main sources of decreasing the image quality in cone-beam computed tomography (CBCT). The moving blocker method is economic and effective for scatter correction (SC), which can simultaneously estimate scatter and reconstruct the complete volume within the field of view (FOV) from a single CBCT scan. However, at the regions with large intensity transition in the projection images along the axial blocker moving direction, the estimation of scatter signal from blocked regions in a single projection view can produce large error and cause significant artifacts in reconstructed images and null the usability of these regions.

Fast alternating projection methods for constrained tomographic reconstruction.

The alternating projection algorithms are easy to implement and effective for large-scale complex optimization problems, such as constrained reconstruction of X-ray computed tomography (CT). A typical method is to use projection onto convex sets (POCS) for data fidelity, nonnegative constraints combined with total variation (TV) minimization (so called TV-POCS) for sparse-view CT reconstruction. However, this type of method relies on empirically selected parameters for satisfactory reconstruction and is generally slow and lack of convergence analysis.

Respiratory motion correction in 4D-PET by simultaneous motion estimation and image reconstruction (SMEIR).

In conventional 4D positron emission tomography (4D-PET), images from different frames are reconstructed individually and aligned by registration methods. Two issues that arise with this approach are as follows: (1) the reconstruction algorithms do not make full use of projection statistics; and (2) the registration between noisy images can result in poor alignment. In this study, we investigated the use of simultaneous motion estimation and image reconstruction (SMEIR) methods for motion estimation/correction in 4D-PET.

Reconstruction of sparse-view X-ray computed tomography using adaptive iterative algorithms.

In this paper, we propose two reconstruction algorithms for sparse-view X-ray computed tomography (CT). Treating the reconstruction problems as data fidelity constrained total variation (TV) minimization, both algorithms adapt the alternate two-stage strategy: projection onto convex sets (POCS) for data fidelity and non-negativity constraints and steepest descent for TV minimization. The novelty of this work is to determine iterative parameters automatically from data, thus avoiding tedious manual parameter tuning.

4D reconstruction for low-dose cardiac gated SPECT.

Purpose: Due to the combination of high-frequency use and relatively high diagnostic radiation dose (>9 mSv for one scan), there is a need to lower the radiation dose used in myocardial perfusion imaging (MPI) studies in cardiac gated single photon emission computed tomography (GSPECT) in order to reduce its population based cancer risk. The aim of this study is to assess quantitatively the potential utility of advanced 4D reconstruction for GSPECT for significantly lowered imaging dose.

Methods: For quantitative evaluation, Monte Carlo simulation with the 4D NURBS-based cardiac-torso (NCAT) phantom is used for GSPECT imaging at half and quarter count levels in the projections emulating lower injected activity (dose) levels.

The smoothing artifact of spatially constrained canonical correlation analysis in functional MRI.

A wide range of studies show the capacity of multivariate statistical methods for fMRI to improve mapping of brain activations in a noisy environment. An advanced method uses local canonical correlation analysis (CCA) to encompass a group of neighboring voxels instead of looking at the single voxel time course. The value of a suitable test statistic is used as a measure of activation.

Optimizing the performance of local canonical correlation analysis in fMRI using spatial constraints.

The benefits of locally adaptive statistical methods for fMRI research have been shown in recent years, as these methods are more proficient in detecting brain activations in a noisy environment. One such method is local canonical correlation analysis (CCA), which investigates a group of neighboring voxels instead of looking at the single voxel time course. The value of a suitable test statistic is used as a measure of activation.

A within-subject ERP and fMRI investigation of orientation-specific recognition memory for pictures.

Despite a large body of recognition memory research, its temporal, measured with ERPs, and spatial, measured with fMRI, substrates have never been investigated in the same subjects. In the present study, we obtained this information in parallel sessions, in which subjects studied and recognized images of visual objects and their orientation. The results showed that ERP-familiarity processes between 240 and 440 ms temporally preceded recollection processes and were structurally associated with prefrontal brain regions.

Extending local canonical correlation analysis to handle general linear contrasts for FMRI data.

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism.