Non-Isocentric Geometry for Next-Generation Tomosynthesis With Super-Resolution

Our lab at the University of Pennsylvania (UPenn) is investigating novel designs for digital breast tomosynthesis. We built a next-generation tomosynthesis system with a non-isocentric geometry (superior-to-inferior detector motion). This paper examines four metrics of image quality affected by this design. First, aliasing was analyzed in reconstructions prepared with smaller pixelation than the detector. Aliasing was assessed with a theoretical model of r -factor, a metric calculating amplitudes of alias signal relative to input signal in the Fourier transform of the reconstruction of a sinusoidal object. Aliasing was also assessed experimentally with a bar pattern (illustrating spatial variations in aliasing) and 360°-star pattern (illustrating directional anisotropies in aliasing). Second, the point spread function (PSF) was modeled in the direction perpendicular to the detector to assess out-of-plane blurring. Third, power spectra were analyzed in an anthropomorphic phantom developed by UPenn and manufactured by Computerized Imaging Reference Systems (CIRS), Inc. (Norfolk, VA). Finally, calcifications were analyzed in the CIRS Model 020 BR3D Breast Imaging Phantom in terms of signal-to-noise ratio (SNR); i.e., mean calcification signal relative to background-tissue noise. Image quality was generally superior in the non-isocentric geometry: Aliasing artifacts were suppressed in both theoretical and experimental reconstructions prepared with smaller pixelation than the detector. PSF width was also reduced at most positions. Anatomic noise was reduced. Finally, SNR in calcification detection was improved. (A potential trade-off of smaller-pixel reconstructions was reduced SNR; however, SNR was still improved by the detector-motion acquisition.) In conclusion, the non-isocentric geometry improved image quality in several ways.

Revisiting aliasing noise to build more robust sparsity in nonuniform sampling 2D-NMR

A continuing priority is to better understand and resolve the barriers to using nonuniform sampling (NUS) in challenging small molecule 2D NMR with subsampling of the Nyquist grid (a.k.a. coverage) below 50%. Possible causes for artifacts, often termed sampling noise, in 1D-NUS of 2D-NMR are revisited here, where weak aliasing artifacts are a growing concern as NUS becomes sparser. As NUS schedules become sparser, repeat sequences are shown to occur in the dense sampling regions early in the sampling schedule, causing aliasing artifacts in resulting spectra. An intuitive screening approach that detects patterns in sampling schedules based on a convolutional filter was implemented. Sampling schedules that have low proportions of repeat sequences show significantly reduced artifacts. Another route to remediate early repeat sequences is a short period of uniform sampling at the beginning of the schedule, which also leads to a significant suppression of unwanted sampling noise. Combining the repeat sequence filter with a survey of HSQC and LR-HSQMBC experiments, it is shown that very short initial uniform regions of about 2%-4% of the sampling space can ameliorate repeat sequences in sparser NUS and lead to robust spectral reconstructions by iterative soft thresholding (IST), even when the point spread function is unchanged. Using the principles developed here, a suite of 'one-click' schedules was developed for broader use.

A Methodological Checklist for Studies of Pleasure and Enjoyment Responses to High-Intensity Interval Training: Part II. Intensity, Timing of Assessments, Data Modeling, and Interpretation

Recent studies have concluded that high-intensity interval training should be seen as a "viable alternative" to, and may be more enjoyable than, moderate-intensity continuous exercise. If true, these claims have the potential to revolutionize the science and practice of exercise, establishing high-intensity interval training as not only a physiologically effective exercise modality but also a potentially sustainable one. However, these claims stand in contrast to voluminous evidence according to which high levels of exercise intensity are typically experienced as less pleasant than moderate levels. To help researchers, peer reviewers, editors, and critical readers appreciate possible reasons for the apparently conflicting results, we present a checklist that identifies crucial methodological elements in studies investigating the effects of high-intensity interval training on affect and enjoyment. This second installment covers how "high-intensity" and "moderate-intensity" experimental conditions are defined, the timing of assessments of affect, the modeling of affective responses, and data interpretation.

Understanding aliasing effects and their removal in SPEN MRI: A k-space perspective

PURPOSE

To characterize the mechanism of formation and the removal of aliasing artifacts and edge ghosts in spatiotemporally encoded (SPEN) MRI within a k-space theoretical framework.

METHODS

SPEN's quadratic phase modulation can be described in k-space by a convolution matrix whose coefficients derive from Fourier relations. This k-space model allows us to pose SPEN's reconstruction as a deconvolution process from which aliasing and edge ghost artifacts can be quantified by estimating the difference between a full sampling and reconstructions resulting from undersampled SPEN data.

RESULTS

Aliasing artifacts in SPEN MRI reconstructions can be traced to image contributions corresponding to high-frequency k-space signals. The k-space picture provides the spatial displacements, phase offsets, and linear amplitude modulations associated to these artifacts, as well as routes to removing these from the reconstruction results. These new ways to estimate the artifact priors were applied to reduce SPEN reconstruction artifacts on simulated, phantom, and human brain MRI data.

CONCLUSION

A k-space description of SPEN's reconstruction helps to better understand the signal characteristics of this MRI technique, and to improve the quality of its resulting images.

Nyquist Sampling Conditions of Some Diffraction Algorithms with Adjustable Magnification

Diffraction algorithms with adjustable magnification are dominant in holographic projection and imaging. However, the algorithms are limited by the Nyquist sampling conditions, and simulation results with inappropriate parameters sometimes appear with aliasing. At present, many diffraction algorithms have been proposed and improved, but there is a need for an overall analysis of their sampling conditions. In this paper, some classical diffraction algorithms with adjustable magnification are summarized, and their sampling conditions in the case of plane wave or spherical wave illumination are analyzed and compared, which helps to select the appropriate diffraction algorithm according to the specific parameter conditions of the simulation to avoid aliasing.

Network organization of resting-state cerebral hemodynamics and their aliasing contributions measured by functional near-infrared spectroscopy

Objective. Spontaneous fluctuations of cerebral hemodynamics measured by functional magnetic resonance imaging (fMRI) are widely used to study the network organization of the brain. The temporal correlations among the ultra-slow, <0.1 Hz fluctuations across the brain regions are interpreted as functional connectivity maps and used for diagnostics of neurological disorders. However, despite the interest narrowed in the ultra-slow fluctuations, hemodynamic activity that exists beyond the ultra-slow frequency range could contribute to the functional connectivity, which remains unclear.Approach. In the present study, we have measured the brain-wide hemodynamics in the human participants with functional near-infrared spectroscopy (fNIRS) in a whole-head, cap-based and high-density montage at a sampling rate of 6.25 Hz. In addition, we have acquired resting state fMRI scans in the same group of participants for cross-modal evaluation of the connectivity maps. Then fNIRS data were deliberately down-sampled to a typical fMRI sampling rate of ∼0.5 Hz and the resulted differential connectivity maps were subject to a k-means clustering.Main results. Our diffuse optical topographical analysis of fNIRS data have revealed a default mode network (DMN) in the spontaneous deoxygenated and oxygenated hemoglobin changes, which remarkably resemble the same fMRI network derived from participants. Moreover, we have shown that the aliased activities in the down-sampled optical signals have altered the connectivity patterns, resulting in a network organization of aliased functional connectivity in the cerebral hemodynamics.Significance.The results have for the first time demonstrated that fNIRS as a broadly accessible modality can image the resting-state functional connectivity in the posterior midline, prefrontal and parietal structures of the DMN in the human brain, in a consistent pattern with fMRI. Further empowered by the fast sampling rate of fNIRS, our findings suggest the presence of aliased connectivity in the current understanding of the human brain organization.

Usefulness of aliasing phenomenon for diagnosing venous valve stenosis of arteriovenous fistula in a hemodialysis patient

We present venous valve stenosis, which is an uncommon cause of arteriovenous fistula (AVF) dysfunction. Owing to the thin structure in echography, venous valves are challenging to observe; however, we have found that the aliasing phenomenon is useful for diagnosing venous valve stenosis.

The Debiased Spatial Whittle likelihood

We provide a computationally and statistically efficient method for estimating the parameters of a stochastic covariance model observed on a regular spatial grid in any number of dimensions. Our proposed method, which we call the Debiased Spatial Whittle likelihood, makes important corrections to the well-known Whittle likelihood to account for large sources of bias caused by boundary effects and aliasing. We generalize the approach to flexibly allow for significant volumes of missing data including those with lower-dimensional substructure, and for irregular sampling boundaries. We build a theoretical framework under relatively weak assumptions which ensures consistency and asymptotic normality in numerous practical settings including missing data and non-Gaussian processes. We also extend our consistency results to multivariate processes. We provide detailed implementation guidelines which ensure the estimation procedure can be conducted in O ( n log n ) operations, where n is the number of points of the encapsulating rectangular grid, thus keeping the computational scalability of Fourier and Whittle-based methods for large data sets. We validate our procedure over a range of simulated and realworld settings, and compare with state-of-the-art alternatives, demonstrating the enduring practical appeal of Fourier-based methods, provided they are corrected by the procedures developed in this paper.

Achieving Isotropic Super-Resolution with a Non-Isocentric Acquisition Geometry in a Next-Generation Tomosynthesis System

We have constructed a prototype next-generation tomosynthesis (NGT) system that supports a non-isocentric acquisition geometry for digital breast tomosynthesis (DBT). In this geometry, the detector gradually descends in the superior-to-inferior direction. The aim of this work is to demonstrate that this geometry offers isotropic super-resolution (SR), unlike clinical DBT systems which are characterized by anisotropies in SR. To this end, a theoretical model of a sinusoidal test object was developed with frequency exceeding the alias frequency of the detector. We simulated two geometries: (1) a conventional geometry with a stationary detector, and (2) a non-isocentric geometry. The input frequency was varied over the full 360° range of angles in the plane of the object. To investigate whether SR was achieved, we calculated the Fourier transform of the reconstruction. The amplitude of the tallest peak below the alias frequency was measured relative to the peak at the input frequency. This ratio (termed the r-factor) should approach zero to achieve high-quality SR. In the conventional geometry, the r-factor was minimized (approaching zero) if the orientation of the frequency was parallel with the source motion, yet exceeded unity (prohibiting SR) in the orientation perpendicular to the source motion. However, in the non-isocentric geometry, the r-factor was minimized (approaching zero) for all orientations of the frequency, meaning SR was achieved isotropically. In summary, isotropic SR in DBT can be achieved using the non-isocentric acquisition geometry supported by the NGT system.

Aliasing-free reduced field-of-view parallel imaging

PURPOSE

To reconstruct aliasing-free full field-of-view (FOV) images for reduced FOV (rFOV) parallel imaging (PI) with Cartesian and Wave sampling, which suffers from aliasing artifacts using existing PI methods.

THEORY AND METHODS

The sensitivity encoding method (SENSE) was extended to the Soft-SENSE models supporting multiple-set coil sensitivity maps (CSM) and point spread functions (PSF) for Cartesian and Wave sampled rFOV PI, respectively. The multiple-set CSM and PSF were created from full FOV CSM and PSF according to the image folding process induced by rFOV sampling. The Soft-SENSE reconstructions could be solved by the same algorithms for the conventional full FOV SENSE reconstruction.

RESULTS

Soft-SENSE using multiple-set full FOV CSM and PSF successfully reconstruct aliasing-free full FOV image from rFOV PI data with Cartesian and Wave sampling. The proposed rFOV PI enables flexible control of the aliasing and achieves comparable geometry factors as the standard full FOV PI with the same net acceleration factor. Reduced FOV PI improves the computational efficiency of iterative compressed sensing (CS) and PI reconstruction, especially for high-resolution volumetric imaging, thanks to the reduced fast Fourier transform (FFT) size. Moreover, rFOV PI reconstruction provides a potential alternative to the phase oversampling for the FOV aliasing problem.

CONCLUSION

The proposed Soft-SENSE using full FOV CSM and PSF could reconstruct aliasing-free full FOV image for rFOV PI, and make it a viable solution enabling more flexible PI acceleration and effectively improving the computational efficiency of iterative CSPI reconstruction.

A Reconstruction Algorithm for Temporally Aliased Seismic Signals Recorded by the InSight Mars Lander

In December 2018, the NASA InSight lander successfully placed a seismometer on the surface of Mars. Alongside, a hammering device was deployed at the landing site that penetrated into the ground to attempt the first measurements of the planetary heat flow of Mars. The hammering of the heat probe generated repeated seismic signals that were registered by the seismometer and can potentially be used to image the shallow subsurface just below the lander. However, the broad frequency content of the seismic signals generated by the hammering extends beyond the Nyquist frequency governed by the seismometer's sampling rate of 100 samples per second. Here, we propose an algorithm to reconstruct the seismic signals beyond the classical sampling limits. We exploit the structure in the data due to thousands of repeated, only gradually varying hammering signals as the heat probe slowly penetrates into the ground. In addition, we make use of the fact that repeated hammering signals are sub-sampled differently due to the unsynchronized timing between the hammer strikes and the seismometer recordings. This allows us to reconstruct signals beyond the classical Nyquist frequency limit by enforcing a sparsity constraint on the signal in a modified Radon transform domain. In addition, the proposed method reduces uncorrelated noise in the recorded data. Using both synthetic data and actual data recorded on Mars, we show how the proposed algorithm can be used to reconstruct the high-frequency hammering signal at very high resolution.

Development and Evaluation of the Fourier Spectral Distortion Metric

A spatial resolution metric is presented for tomosynthesis. The Fourier spectral distortion metric (FSD) was developed to evaluate specific resolution properties of different imaging techniques for digital tomosynthesis using a star pattern image to plot modulation in the frequency domain. The FSD samples the spatial resolution of a star-pattern image tangentially over an acute angle and for a range of spatial frequencies in a 2D image or 3D image reconstruction slice. The FSD graph portrays all frequencies present in a star pattern quadrant. In addition to the fundamental input frequency of the star pattern, the FSD graph shows spectral leakage, square wave harmonics, and residual noise. The contrast transfer function (CTF) is obtained using the FSD graph. The CTF is analogous to the modulation transfer function (MTF), but it is not normalized to unity at zero spatial frequency. Unlike the MTF, this metric separates the fundamental input-frequency from the other signals in the Fourier domain. This metric helps determine optimal image reconstruction parameters, the in-plane limit of spatial resolution with respect to aliased signals, and a threshold criterion for an image to support super resolution and reduce aliasing artifacts. Various sampling parameters were evaluated to optimize this metric and ascertain measurement accuracy. The FSD adequately compares resolution properties of 2D images and 3D image reconstruction slices for various x ray imaging modes without suppressing aliased signals.

Development of Magnification Tomosynthesis for Superior Resolution in Diagnostic Mammography

Our previous work showed that digital breast tomosynthesis (DBT) supports super-resolution (SR). Clinical systems are not yet designed to optimize SR; this can be demonstrated with a high-frequency line-resolution pattern. SR is achieved if frequencies are oriented laterally, but not if frequencies are oriented in the perpendicular direction; i.e., the posteroanterior (PA) direction. We are developing a next-generation tomosynthesis (NGT) prototype with new trajectories for the x-ray source. This system is being designed to optimize SR not just for screening, but also for diagnostic mammography; specifically, for magnification DBT (M-DBT). SR is not achieved clinically in magnification mammography, since the acquisition is 2D. The aim of this study is to investigate SR in M-DBT, and analyze how anisotropies differ from screening DBT (S-DBT). We have a theoretical model of a high-frequency sinusoidal test object. First, a conventional scanning motion (directed laterally) was simulated. In the PA direction, SR was not achieved in either S-DBT or M-DBT. Next, the scanning motion was angled relative to the lateral direction. This motion introduces submillimeter offsets in source positions in the PA direction. Theoretical modeling demonstrated that SR was achieved in M-DBT, but not in S-DBT, in the PA direction. This work shows that, with the use of magnification, anisotropies in SR are more sensitive to small offsets in the source motion, leading to insights into how to design M-DBT systems.

Confounding factors in breast magnetic resonance fingerprinting: B 1 + , slice profile, and diffusion effects

PURPOSE

Magnetic resonance fingerprinting (MRF) offers rapid quantitative imaging but may be subject to confounding effects (CE) if these are not included in the model-based reconstruction. This study characterizes the influence of in-plane B 1 + , slice profile and diffusion effects on T₁ and T₂ estimation in the female breast at 1.5T.

METHODS

Simulations were used to predict the influence of each CE on the accuracy of MRF and to investigate the influence of electronic noise and spiral aliasing artefacts. The experimentally observed bias in regions of fibroglandular tissue (FGT) and fatty tissue (FT) was analyzed for undersampled spiral breast MRF data of 6 healthy volunteers by performing MRF reconstruction with and without a CE.

RESULTS

Theoretic analysis predicts T₁ under-/T₂ overestimation if the nominal flip angles are underestimated and inversely, T₁ under-/T₂ overestimation if omitting slice profile correction, and T₁ under-/T₂ underestimation if omitting diffusion in the signal model. Averaged over repeated signal simulations, including spiral aliasing artefacts affected precision more than accuracy. Strong in-plane B 1 + effects occurred in vivo, causing T₂ left-right inhomogeneity between both breasts. Their correction decreased the T₂ difference from 29 to 5 ms in FGT and from 29 to 9 ms in FT. Slice profile correction affected FGT T₂ most strongly, resulting in -22% smaller values. For the employed spoiler gradient strengths, diffusion did not affect the parameter maps, corresponding well with theoretic predictions.

CONCLUSION

Understanding CEs and their relative significance for an MRF sequence is important when defining an MRF signal model for accurate parameter mapping.

Retinal Image Formation and Sampling in a Three-Dimensional World

In this review, I develop an empirically based model of optical image formation by the human eye, followed by neural sampling by retinal ganglion cells, to demonstrate the perceptual effects of blur, aliasing, and distortion of visual space in the brain. The optical model takes account of ocular aberrations and their variation across the visual field, in addition to variations of defocus due to variation of target vergence in three-dimensional scenes. Neural sampling by retinal ganglion cells with receptive field size and spacing that increases with eccentricity is used to visualize the neural image carried by the optic nerve to the brain. Anatomical parameters are derived from psychophysical studies of sampling-limited visual resolution of sinusoidal interference fringes. Retinotopic projection of the neural image onto brainstem nuclei reveals features of the neural image in a perceptually uniform brain space where location and size of visual objects may be measured by counting neurons.

[Color Doppler Sonography: Device Settings Determine the Quality of Diagnosis and the Occurrence of Artifacts]

Color Doppler Sonography: Device Settings Determine the Quality of Diagnosis and the Occurrence of Artifacts Abstract. The major goal of the ultrasound examination is to find the right diagnosis. The correct setting of the device parameters is crucial for a reliable diagnosis. The examiner's knowledge of possible artifacts is helpful in evaluating the ultrasound images. Artifacts are not only present in B-mode sonography, but also in color Doppler sonography. Terms such as blooming, aliasing and twinkling refer to classic artifacts in color Doppler sonography. The causes of these artifacts and the possibilities of influencing them by device technology are presented and discussed. Exemplary images document the most important artifacts in color Doppler sonography.

Sampling Rate Effects on Resting State fMRI Metrics

Low image sampling rates used in resting state functional magnetic resonance imaging (rs-fMRI) may cause aliasing of the cardiorespiratory pulsations over the very low frequency (VLF) BOLD signal fluctuations which reflects to functional connectivity (FC). In this study, we examine the effect of sampling rate on currently used rs-fMRI FC metrics. Ultra-fast fMRI magnetic resonance encephalography (MREG) data, sampled with TR 0.1 s, was downsampled to different subsampled repetition times (sTR, range 0.3–3 s) for comparisons. Echo planar k-space sampling (TR 2.15 s) and interleaved slice collection schemes were also compared against the 3D single shot trajectory at 2.2 s sTR. The quantified connectivity metrics included stationary spatial, time, and frequency domains, as well as dynamic analyses. Time domain methods included analyses of seed-based functional connectivity, regional homogeneity (ReHo), coefficient of variation, and spatial domain group level probabilistic independent component analysis (ICA). In frequency domain analyses, we examined fractional and amplitude of low frequency fluctuations. Aliasing effects were spatially and spectrally analyzed by comparing VLF (0.01–0.1 Hz), respiratory (0.12–0.35 Hz) and cardiac power (0.9–1.3 Hz) FFT maps at different sTRs. Quasi-periodic pattern (QPP) of VLF events were analyzed for effects on dynamic FC methods. The results in conventional time and spatial domain analyses remained virtually unchanged by the different sampling rates. In frequency domain, the aliasing occurred mainly in higher sTR (1–2 s) where cardiac power aliases over respiratory power. The VLF power maps suffered minimally from increasing sTRs. Interleaved data reconstruction induced lower ReHo compared to 3D sampling (p < 0.001). Gradient recalled echo-planar imaging (EPI BOLD) data produced both better and worse metrics. In QPP analyses, the repeatability of the VLF pulse detection becomes linearly reduced with increasing sTR. In conclusion, the conventional resting state metrics (e.g., FC, ICA) were not markedly affected by different TRs (0.1–3 s). However, cardiorespiratory signals showed strongest aliasing in central brain regions in sTR 1–2 s. Pulsatile QPP and other dynamic analyses benefit linearly from short TR scanning.

Making the Environment an Informative Place: A Conceptual Analysis of Epistemic Policies and Sensorimotor Coordination

How do living organisms decide and act with limited and uncertain information? Here, we discuss two computational approaches to solving these challenging problems: a "cognitive" and a "sensorimotor" enrichment of stimuli, respectively. In both approaches, the key notion is that agents can strategically modulate their behavior in informative ways, e.g., to disambiguate amongst alternative hypotheses or to favor the perception of stimuli providing the information necessary to later act appropriately. We discuss how, despite their differences, both approaches appeal to the notion that actions must obey both epistemic (i.e., information-gathering or uncertainty-reducing) and pragmatic (i.e., goal- or reward-maximizing) imperatives and balance them. Our computationally-guided analysis reveals that epistemic behavior is fundamental to understanding several facets of cognitive processing, including perception, decision making, and social interaction.

Letter to the Editor

Hutson and Vexler (2018) demonstrate an example of aliasing with the beta and normal distribution. This letter presents another illustration of aliasing using the beta and normal distributions via an infinite mixture model, inspired by the problem of modeling placebo response.

A Deep Learning Based Anti-aliasing Self Super-resolution Algorithm for MRI

High resolution magnetic resonance (MR) images are desired in many clinical applications, yet acquiring such data with an adequate signal-to-noise ratio requires a long time, making them costly and susceptible to motion artifacts. A common way to partly achieve this goal is to acquire MR images with good in-plane resolution and poor through-plane resolution (i.e., large slice thickness). For such 2D imaging protocols, aliasing is also introduced in the through-plane direction, and these high-frequency artifacts cannot be removed by conventional interpolation. Super-resolution (SR) algorithms which can reduce aliasing artifacts and improve spatial resolution have previously been reported. State-of-the-art SR methods are mostly learning-based and require external training data consisting of paired low resolution (LR) and high resolution (HR) MR images. However, due to scanner limitations, such training data are often unavailable. This paper presents an anti-aliasing (AA) and self super-resolution (SSR) algorithm that needs no external training data. It takes advantage of the fact that the in-plane slices of those MR images contain high frequency information. Our algorithm consists of three steps: 1) We build a self AA (SAA) deep network followed by 2) an SSR deep network, both of which can be applied along different orientations within the original images, and 3) recombine the multiple orientations output from Steps 1 and 2 using Fourier burst accumulation. We perform our SAA+SSR algorithm on a diverse collection of MR data without modification or preprocessing other than N4 inhomogeneity correction, and demonstrate significant improvement compared to competing SSR methods.

Ultrasound imaging artifacts: How to recognize them and how to avoid them

Echocardiography has become a critical tool in clinical cardiology in evaluating cardiac physiology and diagnosing cardiac disease states. However, imaging artifacts are commonly encountered and often lead to misdiagnoses of life-threatening diseases, such as aortic dissection and ventricular thrombus. It is, thus, critical for clinicians to understand these artifacts to avoid these misdiagnoses and protect patients from undue intervention. Artifacts can be broken down into two categories: those from violation of ultrasound system assumptions and those from interference by external equipment and devices. This review article discusses the most commonly encountered artifacts by category, explains their physical mechanisms, elaborates on their most common presentations, and instructs clinicians on how to avoid their misinterpretation.

STEM moiré analysis for 2D strain measurements

A moiré pattern is created in a scanning transmission electron microscope (STEM) when the scan step is close to a crystalline periodicity. Usually, fringes are visible in only one direction, corresponding to a single set of lattice planes, but fringes can be formed in two directions or more. Using an accurate independent calibration, the strains in silicon devices have been determined from the spacing and orientation of one-directional STEM moiré fringes. In this report, we first discuss the origin of the STEM moiré, and then we show how an accurate calibration of the scan step can be obtained from the STEM moiré pattern itself, providing that we know initially only an approximate scan step and the planar spacing. The new calibration scheme also makes the STEM moiré experiments easier, since it can be applied for the moiré where the scan direction is not precisely aligned with the crystalline lattice. Finally, we show how the two-dimensional strain information will be readily extracted from two one-directional moiré patterns using the concept of geometric phase.

Using blocked fractional factorial designs to construct discrete choice experiments for healthcare studies

Discrete choice experiments (DCEs) are increasingly used for studying and quantifying subjects preferences in a wide variety of healthcare applications. They provide a rich source of data to assess real-life decision-making processes, which involve trade-offs between desirable characteristics pertaining to health and healthcare and identification of key attributes affecting healthcare. The choice of the design for a DCE is critical because it determines which attributes' effects and their interactions are identifiable. We apply blocked fractional factorial designs to construct DCEs and address some identification issues by utilizing the known structure of blocked fractional factorial designs. Our design techniques can be applied to several situations including DCEs where attributes have different number of levels. We demonstrate our design methodology using two healthcare studies to evaluate (i) asthma patients' preferences for symptom-based outcome measures and (ii) patient preference for breast screening services. Copyright © 2016 John Wiley & Sons, Ltd.

Diffusion in realistic biophysical systems can lead to aliasing effects in diffusion spectrum imaging

PURPOSE

Diffusion spectrum imaging (DSI) is an imaging technique that has been successfully applied to resolve white matter crossings in the human brain. However, its accuracy in complex microstructure environments has not been well characterized.

THEORY AND METHODS

Here we have simulated different tissue configurations, sampling schemes, and processing steps to evaluate DSI performances' under realistic biophysical conditions. A novel approach to compute the orientation distribution function (ODF) has also been developed to include biophysical constraints, namely integration ranges compatible with axial fiber diffusivities.

RESULTS

Performed simulations identified several DSI configurations that consistently show aliasing artifacts caused by fast diffusion components for both isotropic diffusion and fiber configurations. The proposed method for ODF computation showed some improvement in reducing such artifacts and improving the ability to resolve crossings, while keeping the quantitative nature of the ODF.

CONCLUSION

In this study, we identified an important limitation of current DSI implementations, specifically the presence of aliasing due to fast diffusion components like those from pathological tissues, which are not well characterized, and can lead to artifactual fiber reconstructions. To minimize this issue, a new way of computing the ODF was introduced, which removes most of these artifacts and offers improved angular resolution. Magn Reson Med 76:1837-1847, 2016. © 2015 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Rapid and robust pulmonary proton ZTE imaging in the mouse

Pulmonary MRI is challenging because of the low proton density and rapid transverse relaxation in the lung associated with microscopic magnetic field inhomogeneities caused by tissue-air interfaces. Therefore, low signal is obtained in gradient and spin echo proton images. Alternatively, non-proton MRI using hyperpolarized gases or radial techniques with ultrashort or zero TE have been proposed to image the lung. Also with the latter approach, the general challenge remains to provide full coverage of the lung at sufficient spatial resolution, signal-to-noise ratio (SNR) and image quality within a reasonable scan time. This task is further aggravated by physiological motion and is particularly demanding in small animals, such as mice. In this work, three-dimensional (3D) zero echo time (ZTE) imaging is employed for efficient pulmonary MRI. Four protocols with different averaging and respiratory triggering schemes are developed and compared with respect to image quality and SNR. To address the critical issue of background signal in ZTE images, a subtraction approach is proposed, providing images virtually free of disturbing signal from nearby hardware parts. The protocols are tested for pulmonary MRI in six mice at 4.7 T, consistently providing images of high quality with a 3D isotropic resolution of 313 µm and SNR values in the lung between 8.0 and 18.5 within scan times between 1 min 21 s and 4 min 44 s. A generally high robustness of the ZTE approach against motion is observed, whilst respiratory triggering further improves the SNR and visibility of image details. The developed techniques are expected to enable efficient preclinical animal studies in the lung and will also be of importance for human applications. Further improvements are expected from radiofrequency (RF) coils with increased SNR and reduced background signal.

A new perceptual difference model for diagnostically relevant quantitative image quality evaluation: a preliminary study

PURPOSE

Most objective image quality metrics average over a wide range of image degradations. However, human clinicians demonstrate bias toward different types of artifacts. Here, we aim to create a perceptual difference model based on Case-PDM that mimics the preference of human observers toward different artifacts.

METHOD

We measured artifact disturbance to observers and calibrated the novel perceptual difference model (PDM). To tune the new model, which we call Artifact-PDM, degradations were synthetically added to three healthy brain MR data sets. Four types of artifacts (noise, blur, aliasing or "oil painting" which shows up as flattened, over-smoothened regions) of standard compressed sensing (CS) reconstruction, within a reasonable range of artifact severity, as measured by both PDM and visual inspection, were considered. After the model parameters were tuned by each synthetic image, we used a functional measurement theory pair-comparison experiment to measure the disturbance of each artifact to human observers and determine the weights of each artifact's PDM score. To validate Artifact-PDM, human ratings obtained from a Double Stimulus Continuous Quality Scale experiment were compared to the model for noise, blur, aliasing, oil painting and overall qualities using a large set of CS-reconstructed MR images of varying quality. Finally, we used this new approach to compare CS to GRAPPA, a parallel MRI reconstruction algorithm.

RESULTS

We found that, for the same Artifact-PDM score, the human observer found incoherent aliasing to be the most disturbing and noise the least. Artifact-PDM results were highly correlated to human observers in both experiments. Optimized CS reconstruction quality compared favorably to GRAPPA's for the same sampling ratio.

CONCLUSIONS

We conclude our novel metric can faithfully represent human observer artifact evaluation and can be useful in evaluating CS and GRAPPA reconstruction algorithms, especially in studying artifact trade-offs.

On removing interpolation and resampling artifacts in rigid image registration

We show that image registration using conventional interpolation and summation approximations of continuous integrals can generally fail because of resampling artifacts. These artifacts negatively affect the accuracy of registration by producing local optima, altering the gradient, shifting the global optimum, and making rigid registration asymmetric. In this paper, after an extensive literature review, we demonstrate the causes of the artifacts by comparing inclusion and avoidance of resampling analytically. We show the sum-of-squared-differences cost function formulated as an integral to be more accurate compared with its traditional sum form in a simple case of image registration. We then discuss aliasing that occurs in rotation, which is due to the fact that an image represented in the Cartesian grid is sampled with different rates in different directions, and propose the use of oscillatory isotropic interpolation kernels, which allow better recovery of true global optima by overcoming this type of aliasing. Through our experiments on brain, fingerprint, and white noise images, we illustrate the superior performance of the integral registration cost function in both the Cartesian and spherical coordinates, and also validate the introduced radial interpolation kernel by demonstrating the improvement in registration.

Formalism for hypercomplex multidimensional NMR employing partial-component subsampling

Multidimensional NMR spectroscopy typically employs phase-sensitive detection, which results in hypercomplex data (and spectra) when utilized in more than one dimension. Nonuniform sampling approaches have become commonplace in multidimensional NMR, enabling dramatic reductions in experiment time, increases in sensitivity and/or increases in resolution. In order to utilize nonuniform sampling optimally, it is necessary to characterize the relationship between the spectrum of a uniformly sampled data set and the spectrum of a subsampled data set. In this work we construct an algebra of hypercomplex numbers suitable for representing multidimensional NMR data along with partial-component nonuniform sampling (i.e. the hypercomplex components of data points are subsampled). This formalism leads to a modified DFT-Convolution relationship involving a partial-component, hypercomplex point-spread function set. The framework presented here is essential for the continued development and appropriate characterization of partial-component nonuniform sampling.