Optimal Point Spread Function Research Articles

MINDS: JWST/NIRCam imaging of the protoplanetary disk PDS 70

Context. Two protoplanets have recently been discovered within the PDS 70 protoplanetary disk. JWST/NIRCam offers a unique opportunity to characterize them and their birth environment at wavelengths that are difficult to access from the ground. Aims. We image the circumstellar environment of PDS 70 at 1.87 μm and 4.83 μm, assess the presence of Pa-α emission due to accretion onto the protoplanets, and probe any IR excess indicative of heated circumplanetary material. Methods. We obtained noncoronagraphic JWST/NIRCam images of PDS 70 within the MIRI mid-INfrared Disk Survey (MINDS) program. We leveraged the Vortex Image Processing (VIP) package for data reduction, and we developed dedicated routines for optimal stellar point spread function subtraction, unbiased imaging of the disk, and protoplanet flux measurement in this type of dataset. A radiative transfer model of the disk was used to separate the contributions from the disk and the protoplanets. Results. We redetect both protoplanets and identify extended emission after subtracting a disk model, including a large-scale spiral-like feature. We interpret its signal in the direct vicinity of planet c as tracing the accretion stream that feeds its circumplanetary disk, while the outer part of the feature may rather reflect asymmetric illumination of the outer disk. We also report a bright signal that is consistent with a previously proposed protoplanet candidate enshrouded in dust near the 1:2:4 mean-motion resonance with planets b and c. The 1.87 μm flux of planet b is consistent with atmospheric model predictions, but the flux of planet c is not. We discuss potential origins for this discrepancy, including significant Pa-α line emission. The 4.83 μm fluxes of planets b and c suggest enshrouding dust or heated CO emission from their circumplanetary environment. Conclusions. The use of image-processing methods that are optimized for extended disk signals on high-sensitivity and high-stability from JWST can uniquely identify signatures of planet–disk interactions and enable accurate photometry of protoplanets at wavelengths that are difficult to probe from the ground. Our results indicate that more protoplanets can be identified and characterized in other JWST datasets.

Investigation of Deconvolution Method with Adaptive Point Spread Function Based on Scintillator Thickness in Wavelet Domain.

In recent years, indirect digital radiography detectors have been actively studied to improve radiographic image performance with low radiation exposure. This study aimed to achieve low-dose radiation imaging with a thick scintillation detector while simultaneously obtaining the resolution of a thin scintillation detector. The proposed method was used to predict the optimal point spread function (PSF) between thin and thick scintillation detectors by considering image quality assessment (IQA). The process of identifying the optimal PSF was performed on each sub-band in the wavelet domain to improve restoration accuracy. In the experiments, the edge preservation index (EPI) values of the non-blind deblurred image with a blurring sigma of σ = 5.13 pixels and the image obtained with optimal parameters from the thick scintillator using the proposed method were approximately 0.62 and 0.76, respectively. The coefficient of variation (COV) values for the two images were approximately 1.02 and 0.63, respectively. The proposed method was validated through simulations and experimental results, and its viability is expected to be verified on various radiological imaging systems.

Development of Adaptive Point-Spread Function Estimation Method in Various Scintillation Detector Thickness for X-ray Imaging.

An indirect conversion X-ray detector uses a scintillator that utilizes the proportionality of the intensity of incident radiation to the amount of visible light emitted. A thicker scintillator reduces the patient's dose while decreasing the sharpness. A thin scintillator has an advantage in terms of sharpness; however, its noise component increases. Thus, the proposed method converts the spatial resolution of radiographic images acquired from a normal-thickness scintillation detector into a thin-thickness scintillation detector. Note that noise amplification and artifacts were minimized as much as possible after non-blind deconvolution. To accomplish this, the proposed algorithm estimates the optimal point-spread function (PSF) when the structural similarity index (SSIM) and feature similarity index (FSIM) are the most similar between thick and thin scintillator images. Simulation and experimental results demonstrate the viability of the proposed method. Moreover, the deconvolution images obtained using the proposed scheme show an effective image restoration method in terms of the human visible system compared to that of the traditional PSF measurement technique. Consequently, the proposed method is useful for restoring degraded images using the adaptive PSF while preventing noise amplification and artifacts and is effective in improving the image quality in the present X-ray imaging system.

Field-dependent deep learning enables high-throughput whole-cell 3D super-resolution imaging.

Single-molecule localization microscopy in a typical wide-field setup has been widely used for investigating subcellular structures with super resolution; however, field-dependent aberrations restrict the field of view (FOV) to only tens of micrometers. Here, we present a deep-learning method for precise localization of spatially variant point emitters (FD-DeepLoc) over a large FOV covering the full chip of a modern sCMOS camera. Using a graphic processing unit-based vectorial point spread function (PSF) fitter, we can fast and accurately model the spatially variant PSF of a high numerical aperture objective in the entire FOV. Combined with deformable mirror-based optimal PSF engineering, we demonstrate high-accuracy three-dimensional single-molecule localization microscopy over a volume of ~180 × 180 × 5 μm3, allowing us to image mitochondria and nuclear pore complexes in entire cells in a single imaging cycle without hardware scanning; a 100-fold increase in throughput compared to the state of the art.

Towards optimal point spread function design for resolving closely spaced emitters in three dimensions.

The past decade has brought many innovations in optical design for 3D super-resolution imaging of point-like emitters, but these methods often focus on single-emitter localization precision as a performance metric. Here, we propose a simple heuristic for designing a point spread function (PSF) that allows for precise measurement of the distance between two emitters. We discover that there are two types of PSFs that achieve high performance for resolving emitters in 3D, as quantified by the Cramér-Rao bounds for estimating the separation between two closely spaced emitters. One PSF is very similar to the existing Tetrapod PSFs; the other is a rotating single-spot PSF, which we call the crescent PSF. The latter exhibits excellent performance for localizing single emitters throughout a 1-µm focal volume (localization precisions of 7.3 nm in x, 7.7 nm in y, and 18.3 nm in z using 1000 detected photons), and it distinguishes between one and two closely spaced emitters with superior accuracy (25-53% lower error rates than the best-performing Tetrapod PSF, averaged throughout a 1-µm focal volume). Our study provides additional insights into optimal strategies for encoding 3D spatial information into optical PSFs.

Deformable mirror based optimal PSF engineering for 3D super-resolution imaging.

Point spread function (PSF) engineering is an important technique to encode the properties (e.g., 3D positions, color, and orientation) of a single molecule in the shape of the PSF, often with the help of a programmable phase modulator. A deformable mirror (DM) is currently the most widely used phase modulator for fluorescence detection as it shows negligible photon loss. However, it relies on careful calibration for precise wavefront control. Therefore, design of an optimal PSF not only relies on the theoretical calculation of the maximum information content, but also the physical behavior of the phase modulator, which is often ignored during the optimization process. Here, we develop a framework for PSF engineering which could generate a device specific optimal PSF for 3D super-resolution imaging using a DM. We use our method to generate two types of PSFs with depths of field comparable to the widely used astigmatism and tetrapod PSFs, respectively. We demonstrate the superior performance of the DM specific optimal PSF over the conventional astigmatism and tetrapod PSF both theoretically and experimentally.

Spatially variant autofocus for circular synthetic aperture sonar.

Circular synthetic aperture sonar (CSAS) is a method for improving the resolution and target detection capabilities of a synthetic aperture sonar system. CSAS data are difficult to focus because of their large aperture sizes and elevation sensitivity. This difficulty has sometimes been addressed by using transponders or distributing isotropic scatterers in the field of view of the system; however, this comes at the cost of reduced practicality. As an alternative, map-drift based multipoint autofocus ("multilateration") was proposed by Cantalloube and Nahum [IEEE Trans. Geosci. Remote Sens. 49, 3730-37 (2011)] for autofocusing analogous circular synthetic aperture radar data. Multilateration also solves the problem of aberration spatial variance by providing a three-dimensional navigation correction. In circular synthetic aperture focusing problems, though, correcting aberrations is a joint navigation and elevation estimation problem, and the present work extends the multilateration approach to simultaneously solve both a navigation solution and coordinate corrections for the multilateration control patches. Additionally, methods for addressing the stability and behavior of the inverse problem are addressed, and an adaptive weighting scheme for reducing the influence of outliers is presented. The field results demonstrate near optimal point-spread functions on distributions of natural isotropic scatterers and robustness in regions with bathymetric variability.

DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning.

Localization microscopy is an imaging technique in which the positions of individual point emitters (e.g. fluorescent molecules) are precisely determined from their images. This is a key ingredient in single/multiple-particle-tracking and super-resolution microscopy. Localization in three-dimensions (3D) can be performed by modifying the image that a point-source creates on the camera, namely, the point-spread function (PSF). The PSF is engineered to vary distinctively with emitter depth, using additional optical elements. However, localizing multiple adjacent emitters in 3D poses a significant algorithmic challenge, due to the lateral overlap of their PSFs. Here, we train a neural network to localize multiple emitters with densely overlapping PSFs over a large axial range. Furthermore, we then use the network to design the optimal PSF for the multi-emitter case. We demonstrate our approach experimentally with super-resolution reconstructions of mitochondria and volumetric imaging of fluorescently labeled telomeres in cells.

Optimal PSF Estimation for Simple Optical System Using a Wide-Band Sensor Based on PSF Measurement.

Simple optical system imaging is a method to simplify optical systems by removing aberrations using image deconvolution. The point spread function (PSF) used in deconvolution is an important factor that affects the image quality. However, it is difficult to obtain optimal PSFs. The blind estimation of PSFs relies heavily on the information in the image. Measured PSFs are often misused because real sensors are wide-band. We present an optimal PSF estimation method based on PSF measurements. Narrow-band PSF measurements at a single depth are used to calibrate the optical system; these enable the simulation of real lenses. Then, we simulate PSFs in the wavelength pass range of each color channel all over the field. The optimal PSFs are computed according to these simulated PSFs. The results indicated that the use of the optimal PSFs significantly reduces the artifacts caused by misuse of PSFs, and enhances the image quality.

Why harmonization is needed when using FDG PET/CT as a prognosticator: demonstration with EARL-compliant SUV as an independent prognostic factor in lung cancer.

To determine EARL-compliant prognostic SUV thresholds in a mature cohort of patients with locally advanced NSCLC, and to demonstrate how detrimental it is to use a threshold determined on an older-generation PET system with a newer PET/CT machine, and vice versa, or to use such a threshold with non-harmonized multicentre pooled data. This was a single-centre retrospective study including 139 consecutive stage IIIA-IIIB patients. PET data were acquired as per the EANM guidelines and reconstructed with unfiltered point spread function (PSF) reconstruction. Subsequently, a 6.3mm Gaussian filter was applied using the EQ.PET (Siemens Healthineers) methodology to meet the EANM/EARL harmonizing standards (PSFEARL). A multicentre study including non-EARL-compliant systems was simulated by randomly creating four groups of patients whose images were reconstructed with unfiltered PSF and PSF with Gaussian post-filtering of 3, 5, and 10mm. Identification of optimal SUV thresholds was based on a two-fold cross-validation process that partitioned the overall sample into learning and validation subsamples. Proportional Cox hazards models were used to estimate age-adjusted and multivariable-adjusted hazard ratios (HRs) and their 95% confidence intervals. Kaplan-Meier curves were compared using the log rank test. Median follow-up was 28months (1-104months). For the whole population, the estimated overall survival rate at 36months was 0.39 [0.31-0.47]. The optimal SUVmax cutoff value was 25.43 (95% CI: 23.41-26.31) and 8.47 (95% CI: 7.23-9.31) for the PSF and for the EARL-compliant dataset respectively. These SUVmax cutoff values were both significantly and independently associated with lung cancer mortality; HRs were 1.73 (1.05-2.84) and 1.92 (1.16-3.19) for the PSF and the EARL-compliant dataset respectively. When (i) applying the optimal PSF SUVmax cutoff on an EARL-compliant dataset and the optimal EARL SUVmax cutoff on a PSF dataset or (ii) applying the optimal EARL compliant SUVmax cutoff to a simulated multicentre dataset, the tumour SUVmax was no longer significantly associated with lung cancer mortality. The present study provides the PET community with an EARL-compliant SUVmax as an independent prognosticator for advanced NSCLC that should be confirmed in a larger cohort, ideally at other EARL accredited centres, and highlights the need to harmonize PET quantitative metrics when using them for risk stratification of patients.

Modelling space-based integral-field spectrographs and their application to Type Ia supernova cosmology

We present the parameterized simulation of an integral-field unit (IFU) slicer spectrograph and its applications in spectroscopic studies, namely, for probing dark energy with type Ia supernovae. The simulation suite is called the fast-slicer IFU simulator (FISim). The data flow of FISim realistically models the optics of the IFU along with the propagation effects, including cosmological, zodiacal, instrumentation and detector effects. FISim simulates the spectrum extraction by computing the error matrix on the extracted spectrum. The applications for Type Ia supernova spectroscopy are used to establish the efficacy of the simulator in exploring the wider parametric space, in order to optimize the science and mission requirements. The input spectral models utilize the observables such as the optical depth and velocity of the Si ii absorption feature in the supernova spectrum as the measured parameters for various studies. Using FISim, we introduce a mechanism for preserving the complete state of a system, called the |$\mathrm{\partial} \mathbf{p}/\mathrm{\partial} \mathbf{f}$| matrix, which allows for compression, reconstruction and spectrum extraction, we introduce a novel and efficient method for spectrum extraction, called super-optimal spectrum extraction, and we conduct various studies such as the optimal point spread function, optimal resolution, parameter estimation, etc. We demonstrate that for space-based telescopes, the optimal resolution lies in the region near R ∼ 117 for read noise of 1 e− and 7 e− using a 400 km s−1 error threshold on the Si ii velocity.

Super-resolution of hyperspectral images using sparse representation and Gabor prior

Super-resolution (SR) as a postprocessing technique is quite useful in enhancing the spatial resolution of hyperspectral (HS) images without affecting its spectral resolution. We present an approach to increase the spatial resolution of HS images by making use of sparse representation and Gabor prior. The low-resolution HS observations consisting of large number of bands are represented as a linear combination of a small number of basis images using principal component analysis (PCA), and the significant components are used in our work. We first obtain initial estimates of SR on this reduced dimension by using compressive sensing-based method. Since SR is an ill-posed problem, the final solution is obtained by using a regularization framework. The novelty of our approach lies in: (1) estimation of optimal point spread function in the form of decimation matrix, and (2) using a new prior called “Gabor prior” to super-resolve the significant PCA components. Experiments are conducted on two different HS datasets namely, 31-band natural HS image set collected under controlled laboratory environment and a set of 224-band real HS images collected by airborne visible/infrared imaging spectrometer remote sensing sensor. Visual inspections and quantitative comparison confirm that our method enhances spatial information without introducing significant spectral distortion. Our conclusions include: (1) incorporate the sensor characteristics in the form of estimated decimation matrix for SR, and (2) preserve various frequencies in super-resolved image by making use of Gabor prior.

Super-Resolution of Hyperspectral Images: Use of Optimum Wavelet Filter Coefficients and Sparsity Regularization

Hyperspectral images (HSIs) have high spectral resolution, but they suffer from low spatial resolution. In this paper, a new learning-based approach for super-resolution (SR) using the discrete wavelet transform (DWT) is proposed. The novelty of our approach lies in designing application-specific wavelet basis (filter coefficients). An initial estimate of SR is obtained by using these filter coefficients while learning the high-frequency details in the wavelet domain. The final solution is obtained using a sparsity-based regularization framework, in which image degradation and the sparseness of SR are estimated using the estimated wavelet filter coefficients (EWFCs) and the initial SR estimate, respectively. The advantage of the proposed algorithm lies in 1) the use of EWFCs to represent an optimal point spread function to model image acquisition process; 2) use of sparsity prior to preserve neighborhood dependencies in SR image; and 3) avoiding the use of registered images while learning the initial estimate. Experiments are conducted on three different kinds of images. Visual and quantitative comparisons confirm the effectiveness of the proposed method.

Optimal point spread function design for 3D imaging.

To extract from an image of a single nanoscale object maximum physical information about its position, we propose and demonstrate a framework for pupil-plane modulation for 3D imaging applications requiring precise localization, including single-particle tracking and superresolution microscopy. The method is based on maximizing the information content of the system, by formulating and solving the appropriate optimization problem--finding the pupil-plane phase pattern that would yield a point spread function (PSF) with optimal Fisher information properties. We use our method to generate and experimentally demonstrate two example PSFs: one optimized for 3D localization precision over a 3 μm depth of field, and another with an unprecedented 5 μm depth of field, both designed to perform under physically common conditions of high background signals.

Ruling out unresolved binaries in five transitional disks

Aims. We aim at detecting the presence of companions inside the inner hole/gap region of a sample of five well known transitional disks using spatially-resolved imaging in the near-IR with the VLT/NACO/S13 camera, which probes projected distances from the primary of typically 0.1 to 7 arcsec. The sample includes the stars DoAr 21, HD 135344B (SAO 206462), HR 4796A, T Cha, and TW Hya, spanning ages of less than 1 to 10 Myr, spectral types of A0 to K7, and hole/gap outer radii of 4 to 100 AU. Methods. In order to enhance the contrast and to avoid saturation at the core of the point-spread function (PSF), we use narrow-band filters at 1.75 and 2.12 {\mu}m. The "locally optimized combination of images" (LOCI) algorithm is applied for an optimal speckle noise removal and PSF subtraction, providing an increase of 0.5-1.5 mag in contrast over the classic method. Results. With the proviso that we could have missed companions owing to unfavorable projections, the VLT/NACO observations rule out the presence of unresolved companions down to an inner radius of about 0".1 from the primary in all five transitional disks and with a detection limit of 2 to 5 mag in contrast. In the disk outer regions the detection limits typically reach 8 to 9 mag in contrast and 4.7 mag for T Cha. Hence, the NACO images resolve part of the inner hole/gap region of all disks with the exception of TW Hya, for which the inner hole is only 4 AU. The 5{\sigma} sensitivity profiles, together with a selected evolutionary model, allow to discard stellar companions within the inner hole/gap region of T Cha, and down to the substellar regime for HD 135344B and HR 4796A. DoAr 21 is the only object from the sample of five disks for which the NACO images are sensitive enough for a detection of objects less massive than \sim 13 MJup that is, potential giant planets or low-mass brown dwarfs at radii larger than \sim 76 AU (0".63).

Fully digital auto-focusing system with automatic focusing region selection and point spread function estimation

We present a fully digital auto-focusing (FDAF) system with automatic focusing region selection and a priori estimated dataset of circularly symmetric point-spread functions (PSFs). The proposed approach provides realistic, unsupervised PSF estimation by analyzing the entropy and edge information in the automatically selected focusing region. The main advantage of the proposed system is the fast and robust estimation of a defocusing PSF due to simply selecting the optimal PSF in small, homogeneous region-ofinterest. The proposed FDAF system consists of functional units; i) focusing region selection, ii) PSF selection by generating the major step response in the region from the blurred input image, and iii) image restoration using the selected PSF. Experimental results show the proposed focusing region selection method is more effective than the traditional methods, and the resulting image of the FDAF system provides high visual quality with appropriately amplified details in the image. For this reason, the proposed algorithm can realize low-cost, intelligent focusing function for various image acquisition devices, such as digital cameras, mobile phone cameras, and consumer's camcorders.

High order statistics based blind deconvolution of bi-level images with unknown intensity values

We propose a novel linear blind deconvolution method for bi-level images. The proposed method seeks an optimal point spread function and two parameters that maximize a high order statistics based objective function. Unlike existing minimum entropy deconvolution and least squares minimization methods, the proposed method requires neither unrealistic assumption that the pixel values of a bi-level image are independently identically distributed samples of a random variable nor tuning of regularization parameters.We demonstrate the effectiveness of the proposed method in simulations and experiments.

Optimal point spread function modeling for weak lensing: complexity and sparsity

We investigate the impact of point spread function (PSF) fitting errors on cosmic shear measurements using the concepts of complexity and sparsity. Complexity, introduced in a previous paper, characterizes the number of degrees of freedom of the PSF. For instance, fitting an underlying PSF with a model with low complexity will lead to small statistical errors on the model parameters, however these parameters could suffer from large biases. Alternatively, fitting with a large number of parameters will tend to reduce biases at the expense of statistical errors. We perform an optimisation of scatters and biases by studying the mean squared error of a PSF model. We also characterize a model sparsity, which describes how efficiently the model is able to represent the underlying PSF using a limited number of free parameters. We present the general case and illustrate it for a realistic example of PSF fitted with shapelet basis sets. We derive the relation between complexity and sparsity of the PSF model, signal-to-noise ratio of stars and systematic errors on cosmological parameters. With the constraint of maintaining the systematics below the statistical uncertainties, this lead to a relation between the required number of stars to calibrate the PSF and the sparsity. We discuss the impact of our results for current and future cosmic shear surveys. In the typical case where the biases can be represented as a power law of the complexity, we show that current weak lensing surveys can calibrate the PSF with few stars, while future surveys will require hard constraints on the sparsity in order to calibrate the PSF with 50 stars.

MR-Bildgebung und MR-Spektroskopie zur Charakterisierung von Kardiomyopathien bei Jugendlichen - erste Ergebnisse

Cardiomyopathy is a rare but life-threatening disease in children and adolescents. Recent studies reported morphological, functional or metabolic alterations of the heart. We discuss a combined MR imaging and (31)P MR spectroscopy (MRS) protocol allowing the analysis of interdependencies between these parameters. Since normal values of cardiac MR parameters in this age group are not available, we included studies of age-matched healthy adolescents. 2D-CINE was used to assess left ventricular (LV) parameters. Additional 3D-Chemical Shift Imaging (3D-CSI) and Spectral Localization with Optimal Pointspread Function (SLOOP) reconstruction allowed quantification of the cardiac energy metabolism. Patients (n = 4; all male; age 16.8 +/- 2.9 years) were included on the basis of an echocardiographic diagnosis of possible cardiomyopathy. The same protocol was applied to healthy young volunteers (n = 4; 1 female, 3 male; age 15.5 +/- 0.6 years). The patients had a significantly higher LV mass index compared to the control group (147 +/- 41 g/m (2) versus 97 +/- 16 g/m2; p = 0.04). The other LV parameters (including LV EF with 59 +/- 22 % versus 67 +/- 10 %) showed no significant differences. The phosphocreatine to adenosine triphosphate ratio (PCr/ATP-ratio) of the patients was reduced to 1.71 +/- 0.40 versus 2.44 +/- 0.30 (p = 0.01), combined with a tendency towards decreased PCr concentrations of 9.1 +/- 2.5 versus 7.9 +/- 1.0 mmol/kg. The combination of (31)P MR spectroscopy and MR imaging allows quantitative determination of morphologic, functional and metabolic alterations in adolescents with suspected cardiomyopathy in one examination procedure. The reduction of energy metabolism combined with unaltered global function may indicate a primary role of metabolism in the pathogenesis of cardiomyopathies in adolescents.

Extreme Zoom Surveillance: System Design and Image Restoration

Applications of digital imaging with extreme zoom are traditionally found in astronomy and wild life monitoring. More recently, the need for such capabilities has extended to long range surveillance and wide area monitoring such as forest fires, harbors, and waterways. In this paper, we present a number of sensor arrangements for such applications, focusing on optical setups, auto-focusing mechanisms, and image deblurring techniques. Considering both the speed of convergence and robustness to image degradations induced by high system magnifications and long observation distances, we introduce an auto-focusing algorithm based on sequential search with a variable step size. We derive the transition criteria following maximum likelihood (ML) estimation for the selection of suitable step sizes. The efficiency of the proposed algorithm is illustrated in real-time auto-focusing and tracking of faces from distances of 50m~300m. We also develop an image restoration algorithm for high magnification imaging systems, where an adaptive sharpness measure is employed as a cost function to guide the fine search for an optimal point spread function (PSF) for image deblurring. Experimental results demonstrate a considerably enhanced robustness to image noise and artifacts and ability to select the optimum PSF, producing superior restored images.