Point Spread Function Correction Research Articles

Phantom and clinical evaluation of the Bayesian penalised likelihood reconstruction algorithm Q.Clear without PSF correction in amyloid PET images

PurposeBayesian penalised likelihood (BPL) reconstruction, which incorporates point-spread-function (PSF) correction, provides higher signal-to-noise ratios and more accurate quantitation than conventional ordered subset expectation maximization (OSEM) reconstruction. However, applying PSF correction to brain PET imaging is controversial due to Gibbs artefacts that manifest as unpredicted cortical uptake enhancement. The present study aimed to validate whether BPL without PSF would be useful for amyloid PET imaging.MethodsImages were acquired from Hoffman 3D brain and cylindrical phantoms for phantom study and 71 patients administered with [18F]flutemetamol in clinical study using a Discovery MI. All images were reconstructed using OSEM, BPL with PSF correction, and BPL without PSF correction. Count profile, %contrast, recovery coefficients (RCs), and image noise were calculated from the images acquired from the phantoms. Amyloid β deposition in patients was visually assessed by two physicians and quantified based on the standardised uptake value ratio (SUVR).ResultsThe overestimated radioactivity in profile curves was eliminated using BPL without PSF correction. The %contrast and image noise decreased with increasing β values in phantom images. Image quality and RCs were better using BPL with, than without PSF correction or OSEM. An optimal β value of 600 was determined for BPL without PSF correction. Visual evaluation almost agreed perfectly (κ = 0.91–0.97), without depending on reconstruction methods. Composite SUVRs did not significantly differ between reconstruction methods.ConclusionGibbs artefacts disappeared from phantom images using the BPL without PSF correction. Visual and quantitative evaluation of [18F]flutemetamol imaging was independent of the reconstruction method. The BPL without PSF correction could be the standard reconstruction method for amyloid PET imaging, despite being qualitatively inferior to BPL with PSF correction for [18F]flutemetamol amyloid PET imaging.

SPICE point spread function correction: General framework and capability demonstration

We present a new method of removing point spread function (PSF) artifacts and improving the resolution of multidimensional data sources, including imagers and spectrographs. Rather than deconvolution, which is translationally invariant, the method we present is based on sparse matrix solvers. This allows it to be applied to spatially varying PSFs as well as to combined observations from instruments with radically different spatial, spectral, or thermal response functions (e.g., SDO/AIA and RHESSI). The method was developed to correct PSF artifacts in Solar Orbiter Spectral Imaging of the Coronal Environment, so the motivation, presentation of the method, and the results revolve around this type of application. However, it can be used as a more robust (e.g., with respect to spatially varying PSFs) alternative to deconvolution of 2D image data, as well as similar problems, and is also relevant to more general linear inversion problems.

Impact of list-mode reconstruction and image-space point spread function correction on PET image contrast and quantitative value using SiPM-based PET/CT system.

We evaluate the effects of list-mode reconstruction and the image-space point spread function (iPSF) on the contrast and quantitative values of positron emission tomography (PET) images using a SiPM-PET/CT system. The evaluation is conducted on an NEMA body phantom and clinical images using a Cartesion Prime SiPM-PET/CT system. The signal-to-background ratio (SBR) of the phantom is set to 2, 4, 6, and 8, and all the PET image data are obtained and reconstructed using 3D-OSEM, time-of-flight, iPSF (-/ +), and a 4-mm Gaussian filter with several iterations. The evaluation criteria include % background variability (NB,10mm), % contrast (QH,10mm), iPSF change in QH,10mm (ΔQH,10mm) for edge artifact evaluation, profile curves, visual evaluation of edge artifacts, clinical imaging for the standardized uptake value (SUV) of lung nodules, and SNRliver. NB,10mm demonstrates no significant difference in all SBRs with and without iPSF, whereas QH,10mm is higher based on the SBR with and without iPSF. ΔQH,10mm indicates increased iterations and a larger rate of change (> 5%) for small spheres of < 17mm. The profile curves portrayed almost real concentrations, except for the 10-mm sphere of SBR2 without iPSF; however, with iPSF, an overshoot was observed in the 13-mm sphere of all SBRs. The degree of overshoot increased with increasing iteration and SBR. Edge artifacts were detected at values ≥ 17-22mm in SBRs other than SBR2 with iPSF. Irrespective of the nodal size, SUV and SNRliver improved considerably after iPSF adjustment. Therefore, the effects of list-mode reconstruction and iPSF on PET image contrast were limited, and the overcorrection of the quantitative values was validated using iPSF.

Variability in PET image quality and quantification measured with a permanently filled 68Ge-phantom: a multi-center study

BackgroundThis study evaluated, as a snapshot, the variability in quantification and image quality (IQ) of the clinically utilized PET [18F]FDG whole-body protocols in Finland using a NEMA/IEC IQ phantom permanently filled with 68Ge.MethodsThe phantom was imaged on 14 PET-CT scanners, including a variety of models from two major vendors. The variability of the recovery coefficients (RCmax, RCmean and RCpeak) of the hot spheres as well as percent background variability (PBV), coefficient of variation of the background (COVBG) and accuracy of corrections (AOC) were studied using images from clinical and standardized protocols with 20 repeated measurements. The ranges of the RCs were also compared to the limits of the EARL 18F standards 2 accreditation (EARL2). The impact of image noise on these parameters was studied using averaged images (AVIs).ResultsThe largest variability in RC values of the routine protocols was found for the RCmax with a range of 68% and with 10% intra-scanner variability, decreasing to 36% when excluding protocols with suspected cross-calibration failure or without point-spread-function (PSF) correction. The RC ranges of individual hot spheres in routine or standardized protocols or AVIs fulfilled the EARL2 ranges with two minor exceptions, but fulfilling the exact EARL2 limits for all hot spheres was variable. RCpeak was less dependent on averaging and reconstruction parameters than RCmax and RCmean. The PBV, COVBG and AOC varied between 2.3–11.8%, 9.6–17.8% and 4.8–32.0%, respectively, for the routine protocols. The RC ranges, PBV and COVBG were decreased when using AVIs. With AOC, when excluding routine protocols without PSF correction, the maximum value dropped to 15.5%.ConclusionThe maximum variability of the RC values for the [18F]FDG whole-body protocols was about 60%. The RC ranges of properly cross-calibrated scanners with PSF correction fitted to the EARL2 RC ranges for individual sphere sizes, but fulfilling the exact RC limits would have needed further optimization. RCpeak was the most robust RC measure. Besides COVBG, also RCs and PVB were sensitive to image noise.

Effects of point spread function correction and time-of-flight on visual contrast level and pixel values in brain PET images.

Point spread function (PSF) correction and time-of-flight (TOF) can improve the quality of PET images. None have directly assessed the visual effects of these methods in brain PET images and evaluated the image quality from these methods based on the relationship between the number of updates and noise level. The present study aimed to clarify the effects of PSF and TOF on the visual contrast level and pixel values of brain PET images using an experimental phantom. The visual contrast level was evaluated based on the sum of edge strengths. In addition, the effects of PSF, TOF, and a combination of them on pixel values were evaluated after anatomical standardization of brain images, in which the whole brain was divided into 18 segments. These were evaluated using images reconstructed with the number of updates set to achieve the same noise level. Combined application of the point spread function and TOF resulted in the greatest increase in the sum of edge strengths (32%), followed by PSF (21%) and TOF (6%). The maximum increase in pixel values occurred in the thalamic area (17%). Although PSF and TOF can increase the visual contrast level by increasing the sum of edge strengths, they may affect the results of software-based analysis using pixel values. Nonetheless, using these methods may improve the ability to visualize areas of hypoaccumulation, such as epileptic foci.

Effect of Tumor-Pixel Positioning on the Variability of SUV Measurements in PET Images.

The aim of this study was to investigate the effect on standardized uptake value (SUV) measurement variability of the positional relationship between objects of different sizes and the pixel of a positron emission tomography (PET) image. We used a NEMA IEC body phantom comprising six spheres with diameters of 10, 13, 17, 22, 28, and 37 mm. The phantom was filled with 18F solution and contained target-to-background ratios (TBRs) of 2, 4, and 8. The PET data were acquired for 30 min using a SIGNA PET/MR scanner. The PET images were reconstructed with the ordered subsets expectation maximization (OSEM) algorithm with and without point-spread function (PSF) correction (OSEM + PSF + Filter and OSEM + Filter, respectively). A Gaussian filter of 4 mm full width at half maximum was applied in all reconstructions, except for one model (OSEM + PSF + no Filter). The matrix sizes were 128×128, 192×192, 256×256 and 384×384. Reconstruction was performed by shifting the reconstruction center position by 1 mm in the range 0 to 3 mm in the upward or rightward direction for each parameter. For all reconstructed images, the SUVmax of each hot sphere was measured. To investigate the resulting variation in the SUVmax, the coefficient of variation (CV) of each SUVmax was calculated. The CV of the SUVmax increased as the matrix size and the diameter of the hot sphere decreased in all reconstruction settings. With PSF correction, the CV of SUVmax increased as the TBR increased except when the TBR was 2. The CV of the SUVmax measured in the OSEM + PSF + no Filter images were larger than those measured in the OSEM + PSF + Filter images. The amount of this increase was higher for smaller spheres and larger matrix sizes and was independent of TBR. Shifting the reconstruction center position of the PET image causes variability in SUVmax measurements. To reduce the variability of SUV measurements, it is necessary to use sufficient matrix sizes to satisfy sampling criterion and appropriate filters.

Improved Point Spread Function Correction for Aerosol Measurements Using Wide-field Stellar Photometry

When using wide-field stellar photometry to measure Vertical Aerosol Optical Depth (VAOD), we model the dependence of measured stellar fluxes on the star color and position within the field of view of the imaging system in order to control systematic uncertainties introduced through those dependencies. In wide-field imagers, the Point Spread Function (PSF) varies significantly across the instrument’s field of view (FOV) as the deformation of star images increases with the distance from the center of the FOV. While such dependence may be compensated using a synthetic flat-field correction created through the simultaneous analysis of many images, such an approach fails to account for the image-to-image changes in this correction due to minute changes in focus over time. This effect is believed to be the main reason for fluctuations in the conversion factor between measured photometric flux and actual star brightness (also known as zero-point) as determined by self-calibration scans, which is the dominant source of uncertainty for single-image VAOD measurements. We study the possibilities and limitations of using the PSFEx (PSF Extractor) code to extract the precise model of the PSF from wide-field images and compare the modeled PSF with the actual star shapes and search for optimal choices of global PSF extraction methods, including dividing the frame into smaller segments, across which the PSF is more stable.

Comparison of PMT-based TF64 and SiPM-based Vereos PET/CT systems for 90 Y imaging and dosimetry optimization: A quantitative study.

Selective internal radiotherapy based on transarterial radioembolization (TARE) with yttrium-90 (90 Y) microspheres is an established treatment for primary or metastatic liver disease. The objective of this work is to optimize the dosimetry of patients treated with 90 Y TARE, using positron emission tomography (PET) images. The NEMA 2012 PET phantom was filled with nearly 3.9GBq of 90 Y activity and acquired at days 0, 3, 5, 7, and 9 on a classic time-of-flight PET/computed tomography (CT) scanner (Philips TF64) and on a silicon photomultiplier (SiPM)-based PET/CT scanner (Philips Vereos). Acquisitions were carried on following the guidelines proposed in a previously published multicentric trial and images were reconstructed by varying and combining the available parameters. Comparisons were performed to identify the best set(s) of parameters leading to the most accurate 90 Y-PET image(s), in terms of activity distribution. Then, for both scanners, the best images were analyzed with Simplicit90 Y, a personalized dosimetry software using multicompartmental Medical Internal Radiation Dose model. The comparison between measured and true doses allowed to identify the image granting the most consistent dose estimations and, therefore, to designate the set of parameters to be applied on patients' data for the reconstruction of optimized clinical images. Posttreatment dosimetry of four patients was then realized with Simplicit90 Y using optimized imaging datasets. Based on activity distribution comparisons and dose estimations over phantom and patients data, the SiPM-based PET/CT system appeared more suitable than the photomultiplier tube-based TF64 for 90 Y-PET imaging. With the SiPM-based PET/CT system, reconstructed images with a 2-mm voxel size combined with the application of the point spread function correction led to the most accurate results for quantitative 90 Y measures. For the SiPM-based PET/CT scanner, an optimized set of reconstruction parameters has been identified and applied on patients' data in order to generate the most accurate image to be used for an improved personalized 90 Y-PET dosimetry, ensuring a reliable evaluation of the delivered doses.

3D Reconstruction of cellular images from microfabricated imagers using fully-adaptive deep neural networks

Millimeter-scale multi-cellular level imagers enable various applications, ranging from intraoperative surgical navigation to implantable sensors. However, the tradeoffs for miniaturization compromise resolution, making extracting 3D cell locations challenging—critical for tumor margin assessment and therapy monitoring. This work presents three machine-learning-based modules that extract spatial information from single image acquisitions using custom-made millimeter-scale imagers. The neural networks were trained on synthetically-generated (using Perlin noise) cell images. The first network is a convolutional neural network estimating the depth of a single layer of cells, the second is a deblurring module correcting for the point spread function (PSF). The final module extracts spatial information from a single image acquisition of a 3D specimen and reconstructs cross-sections, by providing a layered “map” of cell locations. The maximum depth error of the first module is 100 µm, with 87% test accuracy. The second module’s PSF correction achieves a least-square-error of only 4%. The third module generates a binary “cell” or “no cell” per-pixel labeling with an accuracy ranging from 89% to 85%. This work demonstrates the synergy between ultra-small silicon-based imagers that enable in vivo imaging but face a trade-off in spatial resolution, and the processing power of neural networks to achieve enhancements beyond conventional linear optimization techniques.

3D Imaging with Double-Helix Point Spread Function and Dynamic Aberration Correction Using a Deformable Mirror

Imaging-based measurements through fluctuating phase boundaries such as free water surfaces or droplets have usually a higher uncertainty due to the changing refraction of light at the boundary. In this paper, a novel measurement system is presented for both 3D imaging with only one camera and aberration correction of light propagation through one boundary. 3D imaging is achieved by introducing a Double-Helix Point Spread Function (DH-PSF). The dynamically introduced aberrations of the phase boundary are measured with a Fresnel Guide Star (FGS) and are corrected with a deformable mirror in a closed-loop system with low latency. The measurement system and the adaptive optics image correction were characterized by means of a linear actuator and a demonstration measurement through an oscillating droplet. For the latter, the uncertainty of a reference flow could be lowered by 58 % using adaptive optics. The technique has the potential to measure complex three-dimensional flows that are optically difficult to access without modification of the experimental setup. Possible applications are optimizations of fuel cells for reducing the consumption of fossil energy.

Phantom Validation of a Conservation of Activity-Based Partial Volume Correction Method for Arterial Input Function in Dynamic PET Imaging

Dynamic PET (dPET) imaging can be utilized to perform kinetic modelling of various physiologic processes, which are exploited by the constantly expanding range of targeted radiopharmaceuticals. To date, dPET remains primarily in the research realm due to a number of technical challenges, not least of which is addressing partial volume effects (PVE) in the input function. We propose a series of equations for the correction of PVE in the input function and present the results of a validation study, based on a purpose built phantom. 18F-dPET experiments were performed using the phantom on a set of flow tubes representing large arteries, such as the aorta (1” 2.54 cm ID), down to smaller vessels, such as the iliac arteries and veins (1/4” 0.635 cm ID). When applied to the dPET experimental images, the PVE correction equations were able to successfully correct the image-derived input functions by as much as 59 ± 35% in the presence of background, which resulted in image-derived area under the curve (AUC) values within 8 ± 9% of ground truth AUC. The peak heights were similarly well corrected to within 9 ± 10% of the scaled DCE-CT curves. The same equations were then successfully applied to correct patient input functions in the aorta and internal iliac artery/vein. These straightforward algorithms can be applied to dPET images from any PET-CT scanner to restore the input function back to a more clinically representative value, without the need for high-end Time of Flight systems or Point Spread Function correction algorithms.

Exploration of the total-body PET/CT reconstruction protocol with ultra-low 18F-FDG activity over a wide range of patient body mass indices

PurposeThe purpose of this study was to investigate the image quality and diagnostic performance of different reconstructions over a wide range of patient body mass indices (BMIs) obtained by total-body PET/CT with ultra-low 18F-FDG activity (0.37 MBq/kg).MethodsA total of 63 patients who underwent total-body PET/CT with ultra-low activity (0.37 MBq/kg) 18F-FDG were enrolled. Patients were grouped by their BMIs. Images were reconstructed with the following two algorithms: the ordered subset expectation maximization (OSEM) algorithm (2, 3 iterations), both with time of flight (TOF) and point spread function (PSF) corrections (hereinafter referred as OSEM2, OSEM3) and HYPER Iterative algorithm (β-values of 0.3, 0.4, 0.5, 0.6) embedded TOF and PSF technologies (hereinafter referred as HYPER0.3, HYPER0.4, HYPER0.5 and HYPER0.6, respectively). Subjective image quality was assessed by two experienced nuclear medicine physicians according to the Likert quintile, including overall image quality, image noise and lesion conspicuity. The standard deviation (SD) and signal-to-noise ratio (SNR) of the liver, and maximum standard uptake value (SUVmax), peak standard uptake value (SUVpeak), tumour background ratio (T/N) and the largest diameter of lesions were quantitatively analysed by a third reader who did not participate in the subjective image assessment.ResultsIncreased noise was associated with increased BMI in all reconstruction groups. Significant differences occurred in the liver SNR among BMI categories of OSEM reconstructions (P < 0.001) but no difference was seen in the HYPER Iterative reconstructions between any of the BMI categories (P > 0.05). With the increase in BMI, overall image quality and image noise scores decreased significantly in all reconstructions, but there was no statistically significant difference of lesion conspicuity. The overall image quality score of the obese group was not qualified (score = 2.7) in OSEM3, while the others were qualified. The lesion conspicuity scores were significantly higher in HYPER Iterative reconstructions and lower in OSEM2 than in OSEM3 (all P < 0.05). The values of SUVmax, SUVpeak and T/N in HYPER0.3, HYPER0.4 and HYPER0.5 were higher than those in OSEM3. In different reconstructions, there was a correlation between lesion size (median, 1.55 cm; range, 0.7–11.0 cm) and SUVpeak variation rate compared to OSEM3 (r = 0.388, − 0.515, − 0.495, − 0.464, and − 0.423, respectively, and all P < 0.001).ConclusionConsidering the image quality and lesion analysis in 18F-FDG total-body PET/CT with ultra-low activity injection, OSEM reconstructions with 3 iterations meet the clinical requirements in patients with BMI < 30. In patients with BMI ≥ 30, it is recommended that the HYPER Iterative algorithm (β-value of 0.3–0.5) be used to ensure consistent visual image quality and quantitative assessment.

Physics-Based Modelling and Simulation of Multibeam Echosounder Perception for Autonomous Underwater Manipulation

One of the key distinguishing aspects of underwater manipulation tasks is the perception challenges of the ocean environment, including turbidity, backscatter, and lighting effects. Consequently, underwater perception often relies on sonar-based measurements to estimate the vehicle’s state and surroundings, either standalone or in concert with other sensing modalities, to support the perception necessary to plan and control manipulation tasks. Simulation of the multibeam echosounder, while not a substitute for in-water testing, is a critical capability for developing manipulation strategies in the complex and variable ocean environment. Although several approaches exist in the literature to simulate synthetic sonar images, the methods in the robotics community typically use image processing and video rendering software to comply with real-time execution requirements. In addition to a lack of physics-based interaction model between sound and the scene of interest, several basic properties are absent in these rendered sonar images–notably the coherent imaging system and coherent speckle that cause distortion of the object geometry in the sonar image. To address this deficiency, we present a physics-based multibeam echosounder simulation method to capture these fundamental aspects of sonar perception. A point-based scattering model is implemented to calculate the acoustic interaction between the target and the environment. This is a simplified representation of target scattering but can produce realistic coherent image speckle and the correct point spread function. The results demonstrate that this multibeam echosounder simulator generates qualitatively realistic images with high efficiency to provide the sonar image and the physical time series signal data. This synthetic sonar data is a key enabler for developing, testing, and evaluating autonomous underwater manipulation strategies that use sonar as a component of perception.

Usefulness of semi-automatic harmonization strategy of standardized uptake values for multicenter PET studies

This study assessed the possibility of semi-automatic harmonization of standardized uptake values (SUVs) in multicenter studies. Phantom data were acquired using 16 PET/CT scanners (including 3 PET/CT scanners with a silicon photomultiplier detector). PET images obtained using 30-min/bed scans for optimum harmonization filter calculations and using 90–180-s/bed scans for SUV validation under clinical conditions were obtained. Time of flight and a reconstruction method with point-spread function correction were allowed. The optimal full width at half maximum of the 3D-Gaussian filter that minimizes the root mean square error with the median value of the JSNM harmonization range was calculated semi-automatically. The SUVmax and the SUVpeak of the hot spheres were measured, and the inter-scanner coefficient of variation (COV) was calculated before and after harmonization. The harmonization filter was applied to 11 of the 15 PET/CT scanners in which the SUV calibration accuracy had been verified, but not in the remaining 4 scanners. Under noiseless conditions before harmonization, the inter-scanner COVs of the SUVmax and the SUVpeak were as high as 21.57% and 12.20%, respectively, decreasing to 8.79% and 5.73% after harmonization, respectively. Harmonization brought the SUVmax of all the hot spheres to within the harmonization range. Even under clinical conditions affected by image noise, the inter-scanner COVs for the SUVmax and SUVpeak were as high as 8.83% and 5.18% after harmonization, respectively. By applying an optimal harmonization filter that is calculated semi-automatically, the harmonization of SUVs according to the JSNM strategy is possible in multicenter studies, thereby reducing inter-scanner COVs.

The role of activity, scan duration and patient\u2019s body mass index in the optimization of FDG imaging protocols on a TOF-PET/CT scanner

BackgroundTime-of-flight (TOF) PET technology determines a reduction in the noise and improves the reconstructed image quality in low count acquisitions, such as in overweight patients, allowing a reduction of administered activity and/or imaging time. However, international guidelines and recommendations on the 18F-fluoro-2-deoxyglucose (FDG) activity administration scheme are old or only partially account for TOF technology and advanced reconstruction modalities.The aim of this study was to optimize FDG whole-body studies on a TOF-PET/CT scanner by using a multivariate approach to quantify how physical figures of merit related to image quality change with acquisition/reconstruction/patient-dependent parameters in a phantom experiment.MethodsThe NEMA-IQ phantom was used to evaluate contrast recovery coefficient (CRC), background variability (BV) and contrast-to-noise ratio (CNR) as a function of changing emission scan duration (ESD), activity concentration (AC), target internal diameter (ID), target-background activity ratio (TBR) and body mass index (BMI). The phantom was filled with an average concentration of 5.3 kBq/ml of FDG solution and the spheres with TBR of 21.2, 8.8 and 5.0 in 3 different sessions. Images were acquired at varying background activity concentration from 5.1 to 1.3 kBq/ml, and images were reconstructed for ESD of 30–151 s per bed position with and without point spread function (PSF) correction. The parameters were all considered in a single analysis using multiple linear regression methods.ResultsAs expected, CRC depended only on sphere ID and on PSF application, while BV depended on sphere ID, ESD, AC and BMI of the phantom, in order of decreasing relevance. Noteworthy, ESD and AC resulted as the most significant predictors of CNR variability with a similar relevance, followed by the BMI of the patient and TBR of the lesion.ConclusionsAC and ESD proved to be effective tools in modulating CNR. ESD could be increased rather than AC to improve image quality in overweight/obese patients to fulfil ALARA principles.

Neural network enhanced 3D turbo spin echo for MR intracranial vessel wall imaging

PurposeTo improve the signal-to-noise ratio (SNR) and image sharpness for whole brain isotropic 0.5 mm three-dimensional (3D) T1 weighted (T1w) turbo spin echo (TSE) intracranial vessel wall imaging (IVWI) at 3 T. MethodsThe variable flip angle (VFA) method enables useful optimization across scan efficiency, SNR and relaxation induced point spread function (PSF) for TSE imaging. A convolutional neural network (CNN) was developed to retrospectively enhance the acquired TSE image with PSF blurring. The previously developed VFA method to increase SNR at the expense of blur can be combined with the presented PSF correction to yield long echo train length (ETL) scan while the acquired image remains high SNR and sharp. The overall approach can enable an optimized solution for accelerated whole brain high-resolution 3D T1w TSE IVWI. Its performance was evaluated on healthy volunteers and patients. ResultsThe PSF blurred image acquired by a long ETL scan can be enhanced by CNN to restore similar sharpness as a short ETL scan, which outperforms the traditional linear PSF enhancement approach. For accelerated whole brain IVWI on volunteers, the optimized isotropic 0.5 mm 3D T1w TSE sequence with CNN based PSF enhancement provides sufficient flow suppression and improved image quality. Preliminary results on patients further demonstrated its improved delineation for intracranial vessel wall and plaque morphology. ConclusionThe CNN enhanced VFA TSE imaging enables an overall image quality improvement for high-resolution 3D T1w IVWI, and may provide a better tradeoff across scan efficiency, SNR and PSF for 3D TSE acquisitions.

A synthetic Roman Space Telescope High-Latitude Imaging Survey: simulation suite and the impact of wavefront errors on weak gravitational lensing

ABSTRACT The Nancy Grace Roman Space Telescope (Roman) mission is expected to launch in the mid-2020s. Its weak lensing program is designed to enable unprecedented systematics control in photometric measurements, including shear recovery, point spread function (PSF) correction, and photometric calibration. This will enable exquisite weak lensing science and allow us to adjust to and reliably contribute to the cosmological landscape after the initial years of observations from other concurrent Stage IV dark energy experiments. This potential requires equally careful planning and requirements validation as the mission prepares to enter its construction phase. We present a suite of image simulations based on galsim that are used to construct a complex, synthetic Roman weak lensing survey that incorporates realistic input galaxies and stars, relevant detector non-idealities, and the current reference 5-yr Roman survey strategy. We present a first study to empirically validate the existing Roman weak lensing requirements flowdown using a suite of 12 matched image simulations, each representing a different perturbation to the wavefront or image motion model. These are chosen to induce a range of potential static and low- and high-frequency time-dependent PSF model errors. We analyse the measured shapes of galaxies from each of these simulations and compare them to a reference, fiducial simulation to infer the response of the shape measurement to each of these modes in the wavefront model. We then compare this to existing analytic flowdown requirements, and find general agreement between the empirically derived response and that predicted by the analytic model.

An Enhanced Visualization of DBT Imaging Using Blind Deconvolution and Total Variation Minimization Regularization.

Digital Breast Tomosynthesis (DBT) presents out-of-plane artifacts caused by features of high intensity. Given observed data and knowledge about the point spread function (PSF), deconvolution techniques recover data from a blurred version. However, a correct PSF is difficult to achieve and these methods amplify noise. When no information is available about the PSF, blind deconvolution can be used. Additionally, Total Variation (TV) minimization algorithms have achieved great success due to its virtue of preserving edges while reducing image noise. This work presents a novel approach in DBT through the study of out-of-plane artifacts using blind deconvolution and noise regularization based on TV minimization. Gradient information was also included. The methodology was tested using real phantom data and one clinical data set. The results were investigated using conventional 2D slice-by-slice visualization and 3D volume rendering. For the 2D analysis, the artifact spread function (ASF) and Full Width at Half Maximum (FWHMMASF) of the ASF were considered. The 3D quantitative analysis was based on the FWHM of disks profiles at 90°, noise and signal to noise ratio (SNR) at 0° and 90°. A marked visual decrease of the artifact with reductions of FWHMASF (2D) and FWHM90° (volume rendering) of 23.8% and 23.6%, respectively, was observed. Although there was an expected increase in noise level, SNR values were preserved after deconvolution. Regardless of the methodology and visualization approach, the objective of reducing the out-of-plane artifact was accomplished. Both for the phantom and clinical case, the artifact reduction in the z was markedly visible.

Euclid: Nonparametric point spread function field recovery through interpolation on a graph Laplacian

Context. Future weak lensing surveys, such as the Euclid mission, will attempt to measure the shapes of billions of galaxies in order to derive cosmological information. These surveys will attain very low levels of statistical error, and systematic errors must be extremely well controlled. In particular, the point spread function (PSF) must be estimated using stars in the field, and recovered with high accuracy. Aims. The aims of this paper are twofold. Firstly, we took steps toward a nonparametric method to address the issue of recovering the PSF field, namely that of finding the correct PSF at the position of any galaxy in the field, applicable to Euclid. Our approach relies solely on the data, as opposed to parametric methods that make use of our knowledge of the instrument. Secondly, we studied the impact of imperfect PSF models on the shape measurement of galaxies themselves, and whether common assumptions about this impact hold true in an Euclid scenario. Methods. We extended the recently proposed resolved components analysis approach, which performs super-resolution on a field of under-sampled observations of a spatially varying, image-valued function. We added a spatial interpolation component to the method, making it a true 2-dimensional PSF model. We compared our approach to PSFEx, then quantified the impact of PSF recovery errors on galaxy shape measurements through image simulations. Results. Our approach yields an improvement over PSFEx in terms of the PSF model and on observed galaxy shape errors, though it is at present far from reaching the required Euclid accuracy. We also find that the usual formalism used for the propagation of PSF model errors to weak lensing quantities no longer holds in the case of an Euclid-like PSF. In particular, different shape measurement approaches can react differently to the same PSF modeling errors.

The value of Bayesian penalized likelihood reconstruction for improving lesion conspicuity of malignant lung tumors on 18F-FDG PET/CT: comparison with ordered subset expectation maximization reconstruction incorporating time-of-flight model and point spread function correction

To evaluate the value of Bayesian penalized likelihood (BPL) reconstruction for improving lesion conspicuity of malignant lung tumors on 18F-fluoro-2-deoxy-D-glucose (FDG) positron emission tomography computed tomography (PET/CT) as compared with the ordered subset expectation maximization (OSEM) reconstruction incorporating time-of-flight (TOF) model and point-spread-function (PSF) correction. Twenty-nine patients with primary or metastatic lung cancers who underwent 18F-FDG PET/CT were retrospectively studied. PET images were reconstructed with OSEM + TOF, OSEM + TOF + PSF, and BPL with noise penalty strength β-value of 200, 400, 600, and 800. The signal-to-noise ratio (SNR) was determined in normal liver parenchyma. Lung lesion conspicuity was evaluated in 50 lung lesions by using a 4-point scale (0, no visible; 1, poor; 2, good; 3, excellent conspicuity). Two observers were independently asked to choose the most preferred reconstruction for detecting the lung lesions on a per-patient level. The maximum standardized uptake value (SUVmax) was measured in each of the 50 lung lesions. Liver SNR on the images reconstructed by BPL with β-value of 600 and 800 (17.8 ± 3.7 and 22.5 ± 4.6, respectively) was significantly higher than that by OSEM + TOF + PSF (15.0 ± 3.4, p < 0.0001). BPL with β-value of 600 was chosen most frequently as the preferred reconstruction algorithm for lung lesion assessment by both observers. The conspicuity score of the lung lesions < 10mm in diameter on images reconstructed by BPL with β-value of 600 was significantly greater than that with OSEM + TOF + PSF (2.2 ± 0.8 vs 1.6 ± 0.9, p < 0.0001), while the conspicuity score of the lesions ≥ 10mm in diameter was not significantly different between BPL with β-value of 600 and OSEM + TOF + PSF. The mean SUVmax was increased by BPL with β-value of 600 for the lung lesions with < 10mm in diameter, compared to OSEM + TOF + PSF (3.4 ± 3.1 to 4.2 ± 3.5, p = 0.001). In contrast, BPL with β-value of 600 did not provide increased SUVmax for the lesions ≥ 10mm in diameter. BPL reconstruction significantly improves the detection of small inconspicuous malignant tumors in the lung, improving the diagnostic performance of PET/CT.