Mask design, fabrication, and experimental ghost imaging applications for patterned X-ray illumination.

A set of non-configurable transversely-displaced masks has been designed and fabricated to generate high-quality X-ray illumination patterns for use in imaging techniques such as ghost imaging (GI), ghost projection, and speckle tracking. The designs include a range of random binary and orthogonal patterns, fabricated through a combination of photolithography and gold electroplating techniques. We experimentally demonstrated that a single wafer can be used as an illumination mask for GI, employing individual illumination patterns and also a mixture of patterns, using a laboratory X-ray source. The quality of the reconstructed X-ray ghost images has been characterized and evaluated through a range of metrics.

Magnetic resonance fingerprinting: from evolution to clinical applications.

In 2013, Magnetic Resonance Fingerprinting (MRF) emerged as a method for fast, quantitative Magnetic Resonance Imaging. This paper reviews the current status of MRF up to early 2020 and aims to highlight the advantages MRF can offer medical imaging professionals. By acquiring scan data as pseudorandom samples, MRF elicits a unique signal evolution, or 'fingerprint', from each tissue type. It matches 'randomised' free induction decay acquisitions against pre-computed simulated tissue responses to generate a set of quantitative images of T1 , T2 and proton density (PD) with co-registered voxels, rather than as traditional relative T1 - and T2 -weighted images. MRF numeric pixel values retain accuracy and reproducibility between 2% and 8%. MRF acquisition is robust to strong undersampling of k-space. Scan sequences have been optimised to suppress sub-sampling artefacts, while artificial intelligence and machine learning techniques have been employed to increase matching speed and precision. MRF promises improved patient comfort with reduced scan times and fewer image artefacts. Quantitative MRF data could be used to define population-wide numeric biomarkers that classify normal versus diseased tissue. Certification of clinical centres for MRF scan repeatability would permit numeric comparison of sequential images for any individual patient and the pooling of multiple patient images across large, cross-site imaging studies. MRF has to date shown promising results in early clinical trials, demonstrating reliable differentiation between malignant and benign prostate conditions, and normal and sclerotic hippocampal tissue. MRF is now undergoing small-scale trials at several sites across the world; moving it closer to routine clinical application.

Chaotic Sensing.

We propose a sparse imaging methodology called Chaotic Sensing (ChaoS) that enables the use of limited yet deterministic linear measurements through fractal sampling. A novel fractal in the discrete Fourier transform is introduced that always results in the artefacts being turbulent in nature. These chaotic artefacts have characteristics that are image independent, facilitating their removal through dampening (via image denoising) and obtaining the maximum likelihood solution. In contrast with existing methods, such as compressed sensing, the fractal sampling is based on digital periodic lines that form the basis of discrete projected views of the image without requiring additional transform domains. This allows the creation of finite iterative reconstruction schemes in recovering an image from its fractal sampling that is also new to discrete tomography. As a result, ChaoS supports linear measurement and optimisation strategies, while remaining capable of recovering a theoretically exact representation of the image. We apply the method to simulated and experimental limited magnetic resonance (MR) imaging data, where restrictions imposed by MR physics typically favour linear measurements for reducing acquisition time.

Comparison of different numerical treatments for x-ray phase tomography of soft tissue from differential phase projections.

X-ray imaging of soft tissue is made difficult by their low absorbance. The use of x-ray phase imaging and tomography can significantly enhance the detection of these tissues and several approaches have been proposed to this end. Methods such as analyzer-based imaging or grating interferometry produce differential phase projections that can be used to reconstruct the 3D distribution of the sample refractive index. We report on the quantitative comparison of three different methods to obtain x-ray phase tomography with filtered back-projection from differential phase projections in the presence of noise. The three procedures represent different numerical approaches to solve the same mathematical problem, namely phase retrieval and filtered back-projection. It is found that obtaining individual phase projections and subsequently applying a conventional filtered back-projection algorithm produces the best results for noisy experimental data, when compared with other procedures based on the Hilbert transform. The algorithms are tested on simulated phantom data with added noise and the predictions are confirmed by experimental data acquired using a grating interferometer. The experiment is performed on unstained adult zebrafish, an important model organism for biomedical studies. The method optimization described here allows resolution of weak soft tissue features, such as muscle fibers.

Recovering missing slices of the discrete Fourier transform using Ghosts.

The discrete Fourier transform (DFT) underpins the solution to many inverse problems commonly possessing missing or unmeasured frequency information. This incomplete coverage of the Fourier space always produces systematic artifacts called Ghosts. In this paper, a fast and exact method for deconvolving cyclic artifacts caused by missing slices of the DFT using redundant image regions is presented. The slices discussed here originate from the exact partitioning of the Discrete Fourier Transform (DFT) space, under the projective Discrete Radon Transform, called the discrete Fourier slice theorem. The method has a computational complexity of O(n log(2) n) (for an n=N×N image) and is constructed from a new cyclic theory of Ghosts. This theory is also shown to unify several aspects of work done on Ghosts over the past three decades. This paper concludes with an application to fast, exact, non-iterative image reconstruction from a highly asymmetric set of rational angle projections that give rise to sets of sparse slices within the DFT.

Tumor cell response to synchrotron microbeam radiation therapy differs markedly from cells in normal tissues.

PURPOSE: High-dose synchrotron microbeam radiation therapy (MRT) can be effective at destroying tumors in animal models while causing very little damage to normal tissues. The aim of this study was to investigate the cellular processes behind this observation of potential clinical importance. METHODS AND MATERIALS: MRT was performed using a lattice of 25 mum-wide, planar, polychromatic, kilovoltage X-ray microbeams, with 200-microm peak separation. Inoculated EMT-6.5 tumor and normal mouse skin tissues were harvested at defined intervals post-MRT. Immunohistochemical detection of gamma-H2AX allowed precise localization of irradiated cells, which were also assessed for proliferation and apoptosis. RESULTS: MRT significantly reduced tumor cell proliferation by 24 h post-irradiation (p = 0.002). An unexpected finding was that within 24 h of MRT, peak and valley irradiated zones were indistinguishable in tumors because of extensive cell migration between the zones. This was not seen in MRT-treated normal skin, which appeared to undergo a coordinated repair response. MRT elicited an increase in median survival times of EMT-6.5 and 67NR tumor-inoculated mice similar to that achieved with conventional radiotherapy, while causing markedly less normal tissue damage. CONCLUSIONS: This study provides evidence of a differential response at a cellular level between normal and tumor tissues after synchrotron MRT.

Changes in effective connectivity models in the presence of task-correlated motion: an fMRI study.

We investigated the effects of motion-correction strategy and time course selection method when structural equation modeling is applied to fMRI data in the presence of task-correlated motion. Three motion-correction methods were employed for a group of 12 subjects performing an orthographic lexical retrieval task: (1) a rigid body realignment as implemented in SPM99, (2) a rigid body realignment combined with the inclusion of motion parameters in the statistical model, and (3) the FLIRT motion correction followed by an ICA analysis aiming to identify and remove the motion-related components and the ghosting artifacts. For each motion correction, the time courses of the activated regions were selected in three ways: (1) using the voxels with the highest Z scores, (2) using the average across all the statistically significant voxels in the region of interest, and (3) using a within-region, across-subjects, singular value decomposition. The resulting models of effective connectivity were markedly different, although the activation pattern was not substantially altered by the motion-correction method. Higher values for the path coefficients were obtained for the models fitted to the covariance matrices based on the average time courses than for the covariance matrices based on a single voxel time course. Our results suggest caution with the interpretation of task-induced changes in effective connectivity since, for higher-order cognitive brain functions, multiple models can be fitted to a given data set and these models cannot be rejected on an anatomical or cognitive basis. Hum. Brain Mapping 21:49-63, 2004.

Density of states for vibrations of fractal drums.

Vibrations of membranes with fractal boundaries (fractal drums) are investigated. Numerical results are presented for Koch drums of fractal dimension D(f)=3/2 at prefractal generations 1-3, and for Koch snowflake drums (D(f)=ln 4/ln 3) at generations 3 and 4. The results show that the low-frequency integrated densities of states (IDOS's) of the drums are well approximated by a two-term asymptotic of the form given by the modified Weyl-Berry (MWB) conjecture, which predicts a correction of DeltaN(Omega) proportional, variant Omega(D(f)) to the leading-order Weyl term. In the high-frequency regime, where the half wavelength is smaller than the smallest features of the prefractal perimeter, the two-term Weyl asymptotic is applicable, with DeltaN(Omega) approximately Omega. The results also indicate that oscillations in DeltaN(Omega) arise due to localization of the wave amplitude near the prefractal perimeter. It is argued that for a self-similar fractal boundary, the amplitude of the oscillations is asymptotically proportional to Omega(D(f)), which implies an O(Omega(D(f))), rather than the conjectured o(Omega(D(f))), error term for the asymptotic IDOS given by the MWB conjecture.

Simulation of the effects of global normalization procedures in functional MRI.

We report on differences in sensitivity and false-positive rate across five methods of global normalization using resting-state fMRI data embedded with simulated activation. These methods were grand mean session scaling, proportional scaling, ANCOVA, a masking method, and an orthogonalization method. We found that global normalization by proportional scaling and ANCOVA decreased the sensitivity of the statistical analysis and induced artifactual deactivation even when the correlation between the global signal and the experimental paradigm was relatively low. The masking method and the orthogonalization method performed better from this perspective but are both restricted to certain experimental conditions. Based on the results of these simulations, we offer practical guidelines for the choice of global normalization method least likely to bias the experimental results.