Degradation Of Spatial Resolution Research Articles

Unsupervised deep learning method for single image super-resolution of the thick pinhole imaging system using deep image prior

Thick pinhole imaging system is widely used for diagnosing intense pulsed radiation sources. However, owing to the trade-off among spatial resolution, field of view (FOV) and signal-to-noise ratio (SNR), the imaging system normally falls short in achieving high-precision spatial diagnosis. In this paper, we propose an unsupervised deep learning method for single image super-resolution (SISR) of the thick pinhole imaging system. The point spread function (PSF) of the imaging system is obtained by analytical calculation and Monte Carlo simulation methods, and the mathematical model of the imaging system is established using a linear equation. To solve the ill-posed inverse problem, we adopt randomly initialized deep convolutional neural networks (DCNNs) as an image prior without pre-training, which is named deep image prior (DIP). The results demonstrate that, by utilizing the SISR technique to increase the number of pixels in reconstructed images, the proposed DIP algorithm can mitigate the spatial resolution degradation caused by an insufficient spatial sampling frequency of the camera. Compared with various classical algorithms, the proposed DIP algorithm exhibits superior capabilities in recovering high-frequency signals and suppressing ringing artifacts. Furthermore, the convergence and robustness of the proposed DIP algorithm under different random seeds and SNR conditions are also verified.

An Algorithm for Ship Detection in Complex Observation Scenarios Based on Mooring Buoys

Marine anchor buoys, as fixed-point profile observation platforms, are highly susceptible to the threat of ship collisions. Installing cameras on buoys can effectively monitor and collect evidence from ships. However, when using a camera to capture images, it is often affected by the continuous shaking of buoys and rainy and foggy weather, resulting in problems such as blurred images and rain and fog occlusion. To address these problems, this paper proposes an improved YOLOv8 algorithm. Firstly, the polarized self-attention (PSA) mechanism is introduced to preserve the high-resolution features of the original deep convolutional neural network and solve the problem of image spatial resolution degradation caused by shaking. Secondly, by introducing the multi-head self-attention (MHSA) mechanism in the neck network, the interference of rain and fog background is weakened, and the feature fusion ability of the network is improved. Finally, in the head network, this model combines additional small object detection heads to improve the accuracy of small object detection. Additionally, to enhance the algorithm’s adaptability to camera detection scenarios, this paper simulates scenarios, including shaking blur, rain, and foggy conditions. In the end, numerous comparative experiments on a self-made dataset show that the algorithm proposed in this study achieved 94.2% mAP50 and 73.2% mAP50:95 in various complex environments, which is superior to other advanced object detection algorithms.

Design and performance of a low-emittance electron gun for electron beam probe

An Electron Beam Probe (EBP) is preferable to provide a non-interceptive measurement of the transverse distribution of a dense bunched ion beam. During EBP operation, it was observed that the electron beam spot subjects to expansion when scanned across the ion beam due to the strong space charge field of the ion bunch, leading to significant degradation of spatial resolution. To mitigate this effect, a small electron beam spot is beneficial as demonstrated in previous research. This paper presents design details of a low-emittance, small-spot, and long-work-distance electron gun. Geometry optimization of the gun and focus lens is conducted using a multiobjective genetic algorithm (MOGA) interfaced with space charge simulation codes to maintain normalized thermal emittance within an acceptable increase and achieve a small spot at a long work distance. The emittance is measured using a slit-screen technique, resulting in a norm. rms emittance of 0.046(+-0.005) um*rad under low beam current conditions (50 nA). In high-resolution mode, a beam spot size of 0.6 mm (rms) is achieved at a work distance of 1.2 m with the combination of Einzel lens and solenoid. The gun has been successfully operated as part of EBP experiments for accurate and high-resolution measurement of ion beam width, demonstrating excellent agreement with wire scanner measurements with relative deviation not exceeding 1%.

Brillouin-based fiber loss measurement for PON using ERA-BOTDA with differential width pairs of frequency-swept pump pulses

We demonstrate Brillouin-based loss distribution measurement for passive optical networks with differential width pairs of frequency-swept pump pulses that can suppress the spatial resolution degradation, especially those created by large loss events typical of an 8-branch splitter. Based on the acoustic wave simulation of a frequency-swept pump pulse, we investigate the arrangement of the modulation frequency within the optical pulse. A preliminary experiment is conducted on a 5-dB loss event near an 8-branch splitter. The results show that the proposed technique can accurately measure loss events with a spatial resolution of better than 3 m.

SPFS: SNR peak-based frequency selection method to alleviate resolution degradation in MPI real-time imaging

Objective. The primary objective of this study is to address the reconstruction time challenge in magnetic particle imaging (MPI) by introducing a novel approach named SNR-peak-based frequency selection (SPFS). The focus is on improving spatial resolution without compromising reconstruction speed, thereby enhancing the clinical potential of MPI for real-time imaging. Approach. To overcome the trade-off between reconstruction time and spatial resolution in MPI, the researchers propose SPFS as an innovative frequency selection method. Unlike conventional SNR-based selection, SPFS prioritizes frequencies with signal-to-noise ratio (SNR) peaks that capture crucial system matrix information. This adaptability to varying quantities of selected frequencies enhances versatility in the reconstruction process. The study compares the spatial resolution of MPI reconstruction using both SNR-based and SPFS frequency selection methods, utilizing simulated and real device data. Main results. The research findings demonstrate that the SPFS approach substantially improves image resolution in MPI, especially when dealing with a limited number of frequency components. By focusing on SNR peaks associated with critical system matrix information, SPFS mitigates the spatial resolution degradation observed in conventional SNR-based selection methods. The study validates the effectiveness of SPFS through the assessment of MPI reconstruction spatial resolution using both simulated and real device data, highlighting its potential to address a critical limitation in the field. Significance. The introduction of SPFS represents a significant breakthrough in MPI technology. The method not only accelerates reconstruction time but also enhances spatial resolution, thus expanding the clinical potential of MPI for various applications. The improved real-time imaging capabilities of MPI, facilitated by SPFS, hold promise for advancements in drug delivery, plaque assessment, tumor treatment, cerebral perfusion evaluation, immunotherapy guidance, and in vivo cell tracking.

Depth-encoding using optical photon TOF in a prism-PET detector with tapered crystals.

High-resolution brain positron emission tomography (PET) scanner is emerging as a significant and transformative non-invasive neuroimaging tool to advance neuroscience research as well as improve diagnosis and treatment in neurology and psychiatry. Time-of-flight (TOF) and depth-of-interaction (DOI) information provide markedly higher PET imaging performance by increasing image signal-to-noise ratio and mitigating spatial resolution degradation due to parallax error, respectively. PET detector modules that utilize light sharing can inherently carry DOI information from the multiple timestamps that are generated per gamma event. The difference between two timestamps that are triggered by scintillation photons traveling in opposite directions signifies the event's depth-dependent optical photon TOF (oTOF). However, light leak at the crystal-readout interface substantially degrades the resolution of this oTOF-based depthencoding. We demonstrate the feasibility of oTOF-based depth encoding by mitigating light leak in single-ended-readout Prism-PET detector modules using tapered crystals. Minimizing light leak also improved both energy-based DOI and coincidence timingresolutions. The tapered Prism-PET module consists of a 16 16 array of 1.5 1.5 20 lutetium yttrium oxyorthosillicate (LYSO) crystals, which are tapered down to 1.2 1.2 at the crystal-readout interface. The LYSO array couples 4-to-1 to an 8 8 array of 3 3 silicon photomultiplier (SiPM) pixels on the tapered end and to a segmented prismatoid light guide array on the opposite end. Performance of tapered and non-tapered Prism-PET detectors was experimentally characterized and evaluated by measuring flood histogram, energy resolution, energy-, and oTOF-based DOI resolutions, and coincidence timing resolution. Sensitivities of scanners using different Prism-PET detector designs were simulated using Geant4 application for tomographic emission (GATE). For the tapered (non-tapered) Prism-PET module, the measured full width at half maximum (FWHM) energy, timing, energy-based DOI, and oTOF-based DOI resolutions were 8.88 (11.18)%, 243 (286)ps, 2.35 (3.18)mm, and 5.42 (13.87)mm, respectively. The scanner sensitivities using non-tapered and tapered crystals, and 10 rings of detector modules, were simulated to be 30.9 and 29.5kcps/MBq,respectively. The tapered Prism-PET module with minimized light leak enabled the first experimental report of oTOF-based depth encoding at the detector module level. It also enabled the utilization of thinner (i.e., 0.1mm) inter-crystal spacing with barium sulfate as the reflector while also improving energy-based DOI and timingresolutions.

Image quality evaluation in deep-learning-based CT noise reduction using virtual imaging trial methods: Contrast-dependent spatial resolution.

Deep-learning-based image reconstruction and noise reduction methods (DLIR) have been increasingly deployed in clinical CT. Accurate image quality assessment of these methods is challenging as the performance measured using physical phantoms may not represent the true performance of DLIR in patients since DLIR is trained mostly on patient images. In this work, we aim to develop a patient-data-based virtual imaging trial framework and, as a first application, use it to measure the spatial resolution properties of a DLIR method. The patient-data-based virtual imaging trial framework consists of five steps: (1) insertion of lesions into projection domain data using the acquisition geometry of the patient exam to simulate different lesion characteristics; (2) insertion of noise into projection domain data using a realistic photon statistical model of the CT system to simulate different dose levels; (3) creation of DLIR-processed images from projection or image data; (4) creation of ensembles of DLIR-processed patient images from a large number of noise and lesion realizations; and (5) evaluation of image quality using ensemble DLIR images. This framework was applied to measure the spatial resolution of a ResNet based deep convolutional neural network (DCNN) trained on patient images. Lesions in a cylindrical shape and different contrast levels (-500, -100, -50, -20, -10 HU) were inserted to the lower right lobe of the liver in a patient case. Multiple dose levels were simulated (50%, 25%, 12.5%). Each lesion and dose condition had 600 noise realizations. Multiple reconstruction and denoising methods were used on all the noise realizations, including the original filtered-backprojection (FBP), iterative reconstruction (IR), and the DCNN method with three different strength setting (DCNN-weak, DCNN-medium, and DCNN-strong). Mean lesion signal was calculated by performing ensemble averaging of all the noise realizations for each lesion and dose condition and then subtracting the lesion-present images from the lesion absent images. Modulation transfer functions (MTFs) both in-plane and along the z-axis were calculated based on the mean lesion signals. The standard deviations of MTFs at each condition were estimated with bootstrapping: randomly sampling (with replacement) all the DLIR/FBP/IR images from the ensemble data (600 samples) at each condition. The impact of varying lesion contrast, dose levels, and denoising strengths were evaluated. Statistical analysis with paired t-test was used to compare the z-axis and in-plane spatial resolution of five algorithms for five different contrasts and three dose levels. The in-plane and z-axis spatial resolution degradation of DCNN becomes more severe as the contrast or radiation dose decreased, or DCNN denoising strength increased. In comparison with FBP, a 59.5% and 4.1% reduction of in-plane and z-axis MTF (in terms of spatial frequencies at 50% MTF), respectively, was observed at low contrast (-10 HU) for DCNN with the highest denoising strength at 25% routine dose level. When the dose level reduces from 50% to 12.5% of routine dose, the in-plane and z-axis MTFs reduces from 92.1% to 76.3%, and from 98.9% to 95.5%, respectively, at contrast of -100 HU, using FBP as the reference. For most conditions of contrasts and dose levels, significant differences were found among the five algorithms, with the following relationship in both in-plane and cross-plane spatial resolution: FBP>DCNN-Weak>IR>DCNN-Medium>DCNN-Strong. The spatial resolution difference among algorithms decreases at higher contrast or dose levels. A patient-data-based virtual imaging trial framework was developed and applied to measuring the spatial resolution properties of a DCNN noise reduction method at different contrast and dose levels using real patient data. As with other non-linear image reconstruction and post-processing techniques, the evaluated DCNN method degraded the in-plane and z-axis spatial resolution at lower contrast levels, lower radiation dose, and higher denoising strength.

Effect of depth of interaction resolution on the spatial resolution of SIAT aPET

Objective. Spatial resolution is a crucial parameter for a positron emission tomography (PET) scanner. The spatial resolution of a high-resolution small animal PET scanner is significantly influenced by the effect of depth of interaction (DOI) uncertainty. The aim of this work is to investigate the impact of DOI resolution on the spatial resolution of a small animal PET scanner called SIAT aPET and determine the required DOI resolution to achieve nearly uniform spatial resolution within the field of view (FOV). Approach. The SIAT aPET detectors utilize 1.0 × 1.0 × 20 mm3 crystals, with an average DOI resolution of ∼2 mm. A default number of 16 DOI bins are used during data acquisition. First, a Na-22 point source was scanned in the center of the axial FOV with different radial offsets. Then, a Derenzo phantom was scanned at radial offsets of 0 and 15 mm in the center axial FOV. The measured DOI information was rebinned to 1, 2, 4 and 8 DOI bins to mimic different DOI resolutions of the detectors during image reconstruction. Main results. Significant artifacts were observed in images obtained from both the point source and Derenzo phantom when using only one DOI bin. When accurate measurement of DOI is not achieved, degradation in spatial resolution is more pronounced in the radial direction compared to tangential and axial directions for large radial offsets. The radial spatial resolutions at a 30 mm radial offset are 5.05, 2.62, 1.24, 0.86 and 0.78 mm when using 1, 2, 4, 8, or 16 DOI bins, respectively. The axial spatial resolution improved from ∼1.3 to 0.7 mm as the number of DOI bins increased from 1 to 16 at radial offsets from 0 to 25 mm. Two DOI bins are required to obtain images without significant artifacts. The required DOI resolution is about three times the crystal width of SIAT aPET to achieve a uniform submillimeter spatial resolution within the central 60 mm FOV and resolve the 1 mm rods of the Derenzo phantom at both positions.

Illumination-Based Color Reconstruction for the Dynamic Vision Sensor.

This work demonstrates a novel, state-of-the-art method to reconstruct colored images via the dynamic vision sensor (DVS). The DVS is an image sensor that indicates only a binary change in brightness, with no information about the captured wavelength (color) or intensity level. However, the reconstruction of the scene's color could be essential for many tasks in computer vision and DVS. We present a novel method for reconstructing a full spatial resolution, colored image utilizing the DVS and an active colored light source. We analyze the DVS response and present two reconstruction algorithms: linear-based and convolutional-neural-network-based. Our two presented methods reconstruct the colored image with high quality, and they do not suffer from any spatial resolution degradation as other methods. In addition, we demonstrate the robustness of our algorithm to changes in environmental conditions, such as illumination and distance. Finally, compared with previous works, we show how we reach the state-of-the-art results. We share our code on GitHub.

Impact of the maximum ring difference on image quality and noise characteristics of a total-body PET/CT scanner

The sensitivity of a PET system highly depends on the axial acceptance angle or maximum ring difference (MRD), which can be particularly high for total-body scanners due to their larger axial field of views (aFOVs). This study aims to evaluate the impact on image quality (IQ) and noise performance when MRD85 (18°), the current standard for clinical use, is increased to MRD322 (52°) for the Biograph Vision Quadra (Siemens Healthineers). MethodsStudies with a cylindrical phantom covering the 106 cm aFOV and an IEC phantom filled with 18F, 68Ga and 89Zr were performed for acquisition times from 60 to 1800 s and activity concentrations from 0.4 to 3 kBq/ml to assess uniformity, contrast recovery coefficients (CRCs) and to characterize noise by coefficient of variation (CV). Spatial resolution was compared for both MRDs by sampling a quadrant of the FOV with a point source. Further IQ, CV, liver SUVmean and SUVmax were compared for a cohort of 5 patients scanned with [18F]FDG (3 MBq/kg, 1 h p.i.) from 30 to 300 s. ResultsCV was improved by a factor of up to 1.49 and is highest for short acquisition times, peaks at the center field of view and mitigates parabolic in axial direction with no difference to MRD85 beyond the central 80 cm. No substantial differences between the two evaluated MRDs in regards to uniformity, SUVmean or CRC for the different isotopes were observed. A degradation of the average spatial resolution of 0.9 ± 0.2 mm in the central 40 cm FOV was determined with MRD322. Depending on the acquisition time MRD322 resulted in a decrease of SUVmax between 23.8% (30 s) and 9.0% (300 s). ConclusionPatient and phantom studies revealed that scan time could be lowered by approximately a factor of two with MRD322 while maintaining similar noise performance. The moderate degradation in spatial resolution for MRD322 is worth to exploit the full potential of the Quadra by either shorten scan times or leverage noise performance in particular for low count scenarios such as ultra-late imaging or dynamic studies with high temporal resolution.

Ultra-high-resolution spectral silicon-based photon-counting detector CT for coronary CT angiography: Initial results in a dynamic phantom

BackgroundRecent improvements in CT detector technology have led to smaller detector pixels resolving frequencies beyond 20 lp/cm and enabled ultra-high-resolution CT. Silicon-based photon-counting detector (PCD) CT is one such technology that promises improved spatial and spectral resolution. However, when the detector pixel sizes are reduced, the impact of cardiac motion on CT images becomes more pronounced. Here, we investigated the effects cardiac motion on the image quality of a clinical prototype Si-PCD scanner in a dynamic heart phantom. MethodsA series of 3D-printed vessels were created to simulate coronary arteries with diameter in the 1–3.5 ​mm range. Four coronary stents were set inside the d ​= ​3.5 ​mm vessels and all vessels were filled with contrast agents and were placed inside a dynamic cardiac phantom. The phantom was scanned in motion (60 bpm) and at rest on a prototype clinical Si-PCD CT scanner in 8-bin spectral UHR mode. Virtual monoenergetic images (VMI) were generated at 70 ​keV and CT number accuracy and effective spatial resolution (blooming) of rest and motion VMIs were compared. ResultsLinear regression analysis of CT numbers showed excellent agreement (r ​> ​0.99) between rest and motion. We did not observe a significant difference (p ​> ​0.48) in estimating free lumen diameters. Differences in in-stent lumen diameter and stent strut thickness were non-significant with maximum mean difference of approximately 70 ​μm. ConclusionWe found no significant degradation in CT number accuracy or spatial resolution due to cardiac motion. The results demonstrate the potential of spectral UHR coronary CT angiography enabled by Si-PCD.

An Evaluation Method of Ship-Tracking Algorithms for High-Frequency Surface Wave Radar considering High Maneuvers Generated by the MMG Model

Thelong-distance coverage of high-frequency surface wave radar (HFSWR) has promoted it as an enormous means for ship monitoring on the country’s maritime territory. Since it is a primaryradar, noncooperative targets can also be detected. However, this radar also has a shortcoming of low spatial and temporal resolutions due to the narrow available bandwidth in the HF band. This limitation can reduce the performance of ship detection and tracking, especially for highly maneuvering ships. This paper proposes a new method to assess the tracking algorithm for a high-maneuvering ship. The absence of a high-maneuvering plot in the AIS data and existing analytical models are replaced by the MMG model run on MANSIM software. The linear, turning, and zigzag motions are generated and used to evaluate the tracking algorithms. The Monte Carlo simulation was conducted regarding the degradation of spatial resolution in the higher radial range. The tracking performance was analyzed by calculating the RMSE of four parameters, i.e., absolute position, radial range, bearing angle, and speed. For a trial case, four tracking algorithms were evaluated, i.e., Kalman filter (KF), extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF). The evaluation results showed that the EKF tracker had a minor error for the linear track with RMSE of absolute position, radial range, bearing angle, and speed being 1.368 km, 0.526 km, 1.550°, and 0.005 m/s, respectively. Otherwise, the UKF performed slightly better than EKF for the high maneuver targets. The RMSE of absolute position, radial range, bearing angle, and speed were 1.649 km, 0.639 km, 1.919°, and 0.165 m/s, respectively. The results also ensure the applicability of the MMG model to evaluate the tracking algorithm’s performance in HFSWR.

Joint estimation of interaction position and energy deposition in semiconductor SPECT imaging sensors using fully connected neural network

Objective. Pixelated semiconductor detectors such as CdTe and CZT sensors suffer spatial resolution and spectral performance degradation induced by charge-sharing effects. It is critical to enhance the detector property through recovering the energy-deposition and position estimation. Approach. In this work, we proposed a fully-connected-neural-network-based charge-sharing reconstruction algorithm to correct the charge-loss and estimate the sub-pixel position for every multi-pixel charge-sharing event. Main results. Evident energy resolution improvement can be observed by comparing the spectrum produced by a simple charge-sharing addition method and the proposed energy correction methods. We also demonstrate that sub-pixel resolution can be achieved in projections obtained with a small pinhole collimator and an innovative micro-ring collimator. Significance. These achievements are crucial for multiple-tracer SPECT imaging applications, and for other semiconductor detector-based imaging modalities.

Contrast- and noise-dependent spatial resolution measurement for deep convolutional neural network-based noise reduction in CT using patient data.

Deep convolutional neural network (DCNN)-based noise reduction methods have been increasingly deployed in clinical CT. Accurate assessment of their spatial resolution properties is required. Spatial resolution is typically measured on physical phantoms, which may not represent the true performance of DCNN in patients as it is typically trained and tested with patient images and the generalizability of DNN to physical phantoms is questionable. In this work, we proposed a patient-data-based framework to measure the spatial resolution of DCNN methods, which involves lesion- and noise-insertion in projection domain, lesion ensemble averaging, and modulation transfer function measurement using an oversampled edge spread function from the cylindrical lesion signal. The impact of varying lesion contrast, dose levels, and CNN denoising strengths were investigated for a ResNet-based DCNN model trained using patient images. The spatial resolution degradation of DCNN reconstructions becomes more severe as the contrast or radiation dose decreased, or DCNN denoising strength increased. The measured 50%/10% MTF spatial frequencies of DCNN with highest denoising strength were (-500 HU:0.36/0.72 mm-1; -100 HU:0.32/0.65 mm-1; -50 HU:0.27/0.53 mm-1; -20 HU:0.18/0.36 mm-1; -10 HU:0.15/0.30 mm-1), while the 50%/10% MTF values of FBP were almost kept constant of 0.38/0.76 mm-1.

Experimental Assessment of PCA and DT Classification for Streamlined Position Reconstruction in Anger Cameras

In this article, we present a study of machine learning (ML) algorithms to simplify the computation of the planar scintillation coordinates in Anger Cameras for emission tomography applications. Two ML-based techniques for data inference and one technique to speed-up the training procedure are explored within the framework of a multimodal SPECT scanner. First, the use of principal component analysis (PCA), a dimensionality reduction algorithm, is explored to reduce the computational complexity of the maximum-likelihood statistical estimation method. The analysis indicates a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 3$ </tex-math></inline-formula> -fold reduction of computational complexity for typical Anger Camera architectures (with 72 channels). Second, the estimation of the scintillation coordinates is formulated as a classification problem, addressed by means of a decision tree (DT) classifier. No degradation of the achievable intrinsic spatial resolution <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(\sim 1.2$ </tex-math></inline-formula> -mm FWHM) of the detection module was observed when applying PCA (reducing from 72 to 25 components). The DT classifier was trained on experimental data obtained using a parallel-hole collimator: again no degradation of spatial resolution is observed and the computation cost is reduced by more than two orders of magnitude. Finally, in order to overcome the limits of a cumbersome training procedure involving the translation of the collimator, data augmentation was successfully leveraged for the generation of artificial data.

Stopping-power ratio estimation for proton radiotherapy using dual-energy computed tomography and prior-image constrained denoising.

Dual-energy computed tomography (DECT) is a promising technique for estimating stopping-power ratio (SPR) for proton therapy planning. It is known, however, that deriving electron density (ED) and effective atomic number (EAN) from DECT data can cause noise amplification in the resulting SPR images. This can negate the benefits of DECT. This work introduces a new algorithm for estimating SPR from DECT with noise suppression, using a pair of CT scans with spectral separation. The method is demonstrated using phantom measurements. An iterative algorithm is presented, reconstructing ED and EAN with noise suppression, based on Prior Image Constrained Denoising (PIC-D). The algorithm is tested using a Siemens Definition AS+ CT scanner (Siemens Healthcare, Forchheim, Germany). Three phantoms are investigated: a calibration phantom (CIRS 062M), a QA phantom (CATPHAN 700), and an anthropomorphic head phantom (CIRS 731-HN). A task-transfer function (TTF) and the noise power spectrum are derived from SPR images of the QA phantom for the evaluation of image quality. Comparisons of accuracy and noise for ED, EAN, and SPR are made for various versions of the algorithm in comparison to a solution based on Siemens syngo.via Rho/Z software and the current clinical standard of a single-energy CT stoichiometric calibration. A gamma analysis is also applied to the SPR images of the head phantom and water-equivalent distance (WED) is evaluated in a treatment planning system for a proton treatment field. The algorithm is effective at suppressing noise in both ED and EAN and hence also SPR. The noise is tunable to a level equivalent to or lower than that of the syngo.via Rho/Z software. The spatial resolution (10% and 50% frequencies in the TTF) does not degrade even for the highest noise suppression investigated, although the average spatial frequency of noise does decrease. The PIC-D algorithm showed better accuracy than syngo.via Rho/Z for low density materials. In the calibration phantom, it was superior even when excluding lung substitutes, with root-mean-square deviations for ED and EAN less than 0.3% and 2%, respectively, compared to 0.5% and 3%. In the head phantom, however, the SPR accuracy of the PIC-D algorithm was comparable (excluding sinus tissue) to that derived from syngo.via Rho/Z: less than 1% error for soft tissue, brain, and trabecular bone substitutes and 5-7% for cortical bone, with the larger error for the latter likely related to the phantom geometry. Gamma evaluation showed that PIC-D can suppress noise in a patient-like geometry without introducing substantial errors in SPR. The absolute pass rates were almost identical for PIC-D and syngo.via Rho/Z. In the WED evaluations no general differences were shown. The PIC-D DECT algorithm provides scanner-specific calibration and tunable noise suppression. It is vendor agnostic and applicable to any pair of CT scans with spectral separation. Improved accuracy to current methods was not clearly demonstrated for the complex geometry of a head phantom, but the suppression of noise without spatial resolution degradation and the possibility of incorporating constraints on image properties, suggests the usefulness of the approach.

High-fidelity denoising for differential pulse-width pair brillouin optical time domain analyzer based on block-matching and 3D filtering

In the field of structural deformation and strain measurement for civil structural health monitoring, Brillouin optical time-domain analyzer (BOTDA) system with sub-meter high spatial resolution is required. Differential pulse-width pair Brillouin optical time-domain analyzer (DPP-BOTDA) is an excellent method to achieve the goal without the need to modify the system structure. But the signal-to-noise ratio (SNR) of DPP-BOTDA is poor due to the differential operation, which limits its practical applications. Here, we propose to use Block-Matching and 3D filtering (BM3D) algorithm to improve the SNR of DPP-BOTDA with less degradation of measurement accuracy and spatial resolution. The influence of each BM3D parameter on the denoising performance is fully analyzed in detail for the first time which helps the optimization of the algorithm. Three pulse pairs of 50/47 ns, 50/45 ns and 50/43 ns are used for the demonstration with a sensing range of 9.9 km. With the optimized BM3D, the experimental spatial resolution only degrades by 0.08 m, 0.05 m and 0.04 m after denoising when the SNR improvement is 9 dB, respectively. And the temperature uncertainty for the three cases is improved to be 0.79 °C, 0.36 °C and 0.24 °C, respectively. We believe that this research will improve the efficiency of the parameter optimization when using the BM3D method for noise reduction and contribute to the practical application of the DPP-BOTDA system.

Axial scalable add-on PET/MRI prototype based on four-layer DOI detectors integrated with a RF coil

We developed an add-on PET which was a PET insert based on 4-layered depth-of-interaction (DOI) detectors integrated with a birdcage RF-coil. The PET detectors were placed close to subjects to achieve high sensitivity. The DOI capability suppress degradation of spatial resolution at the edge of the field of view (FOV). Previously, we developed an first prototype which had a short axial FOV. In order to extend the axial FOV, we developed new scalable add-on PET prototype. The constructed RF-coil of the new prototype was combined with 24 DOI-PET detector units Each detector unit with two PET detectors was shielded with a carbon fiber shielding box. Each PET detector consisted of a 14 × 14 × 4-layer array of lutetium fine silicate crystals (1.9 mm × 1.9 mm × 4.0 mm), an 8 × 8 multi-pixel photon counter (MPPC, S12641PA-050) array and a readout circuit board with an ASIC. The RF coil was tuned for a 3T clinical MRI (MAGNETOM Verio, Siemens). We evaluated performance the scalable add-on PET prototype in simultaneous PET/MRI measurements. We obtained spatial resolutions of 2.0 mm at the center of the FOV and better than 3.3 mm in the whole FOV. The energy resolution of 12.4 % was obtained at 511 keV. In addition, we observed no degradation of PET performance caused by the MRI measurement. The signal-to-noise ratio (SNR) of the MRI image was 186.2 in simultaneous measurements with the PET. The maximum ΔB0 and maximum difference of the secondary magnetic field due to the eddy current effect were smaller than 0.2 ppm and ±1.0μT, respectively. It meant that the influence of the PET detectors on the static magnetic field and secondary magnetic field did not cause problems.

Quantifying spatial resolution in a fast neutron radiography system

Neutron imaging is a powerful nondestructive examination modality that has been employed in various applications. Fast neutrons provide advantages over lower energy neutrons, such as examining thicker samples and inducing negligible activation and transmutations. However, fast neutrons interact mostly via elastic scattering with both the neutron detector and the object, causing degradation in spatial resolution. This study explores the quantification of spatial resolution caused by the testing target itself and suggests proper candidate materials for characterizing spatial resolution in terms of modulation transfer function. Knife-edge radiographs of 3 mm, 6 mm, and 5 cm thick Tantalum (Ta) foils, and a 2.54 cm Tungsten (W) cube were acquired using a CCD-based imaging system and a Polyvinyl Toluene (PVT) scintillator at the Ohio State Research Reactor (OSURR)’s fast neutron beam facility. The spatial resolutions calculated were 195 ± 20μm, 224 ± 22μm, 248 ± 25μm, and 435 ± 44μm for 3 mm, 6 mm, and 5 cm Ta foils, and 2.54 cm W cube, respectively. The results showed a worsening spatial resolution with increasing target thickness. Simulations and calculations estimate that elastic scattering kinematics between neutrons and protons in the PVT medium also limits spatial resolution, and it sets a lower limit of ∼44μm on the spatial resolution for 2 MeV neutrons.

Polarization Image Demosaicking via Nonlocal Sparse Tensor Factorization

Division-of-focal-plane (DoFP) polarimeter provides a way for snapshot acquisition, making it available to simultaneously record polarization measurements at different orientations. This polarization imaging system has gained more attention in the last few years and is promising to be used in the fields of computer vision and remote sensing. However, this system suffers from the degradation of spatial resolution. To reconstruct polarization information at full resolution, polarization image demosaicking is indispensable. To address polarization image demosaicking issue while preserving the essential structure of polarization data, a sparse tensor factorization-based model is proposed. For a target cube, its similar cubes are first grouped together as a tensor. Then, its compact dictionary and sparse core tensor are learned by factorizing the tensor using sparse coding. Moreover, the correlation among different polarization orientations and the nonlocal self-similarity are adopted to boost the performance. Experimental results on synthetic and real-world data demonstrate that our proposed model outperforms several state-of-the-art methods in terms of both quantitative measurements and visual quality.