Spectral Reconstruction Method Research Articles

Opto-intelligence spectrometer using diffractive neural networks

Abstract Spectral reconstruction, critical for understanding sample composition, is extensively applied in fields like remote sensing, geology, and medical imaging. However, existing spectral reconstruction methods require bulky equipment or complex electronic reconstruction algorithms, which limit the system’s performance and applications. This paper presents a novel flexible all-optical opto-intelligence spectrometer, termed OIS, using a diffractive neural network for high-precision spectral reconstruction, featuring low energy consumption and light-speed processing. Simulation experiments indicate that the OIS is able to achieve high-precision spectral reconstruction under spatially coherent and incoherent light sources without relying on any complex electronic algorithms, and integration with a simplified electrical calibration module can further improve the performance of OIS. To demonstrate the robustness of OIS, spectral reconstruction was also successfully conducted on real-world datasets. Our work provides a valuable reference for using diffractive neural networks in spectral interaction and perception, contributing to ongoing developments in photonic computing and machine learning.

Spectral reconstruction of fundus images using retinex-based semantic spectral separation transformer, applied for retinal oximetry

Multispectral imaging, a non-invasive technique to measure retinal oxygen saturation levels, is limited due to its time-consuming and expensive nature. In this paper, we present R3ST (Retinex-based Semantic Spectral Separation Transformer), an innovative end-to-end method for RGB fundus image spectral reconstruction. Our proposed model leverages RGB images to reconstruct multispectral images, making retinal oximetry more accessible for diagnostics.Existing spectral reconstruction methods face challenges in achieving high accuracy and generalizing across camera sources, affecting diagnostic results. R3ST addresses these issues through an unsupervised semantic spectral separation module that strives to encourage the separate reconstruction of pixels with different semantics as much as possible. Additionally, we introduce an oxygen saturation loss to improve reconstruction accuracy within the relevant regions. To overcome the limitations of generalization properties, our model incorporates a self-supervised Retinex reflectance extraction module. This simulates various imaging systems and extracts reflectance information from fundus images for reconstruction.Extensive experiments demonstrate R3ST’s superior performance in spectral reconstruction accuracy, making retinal oximetry a more viable, cost-effective diagnostic option that provides essential eye health and systemic health information. The code of R3ST is available at https://github.com/telesc0pe/R3ST.

Unsupervised spectral reconstruction from RGB images under two lighting conditions.

Unsupervised spectral reconstruction (SR) aims to recover the hyperspectral image (HSI) from corresponding RGB images without annotations. Existing SR methods achieve it from a single RGB image, hindered by the significant spectral distortion. Although several deep learning-based methods increase the SR accuracy by adding RGB images, their networks are always designed for other image recovery tasks, leaving huge room for improvement. To overcome this problem, we propose a novel, to our knowledge, approach that reconstructs the HSI from a pair of RGB images captured under two illuminations, significantly improving reconstruction accuracy. Specifically, an SR iterative model based on two illuminations is constructed at first. By unfolding the proximal gradient algorithm solving this SR model, an interpretable unsupervised deep network is proposed. All the modules in the proposed network have precise physical meanings, which enable our network to have superior performance and good generalization capability. Experimental results on two public datasets and our real-world images show the proposed method significantly improves both visually and quantitatively as compared with state-of-the-art methods.

PPG heart rate extraction algorithm based on the motion artifact intensity Classification and removal framework

When heart rate is extracted by wearable PPG, motion artifact noise causes serious interference. Since the accuracy of PPG heart rate extraction tends to be positively correlated with the complexity of heart rate extraction algorithm, it is challenging to extract heart rate via wearable PPG throughout the day by taking into account both algorithmic complexity and the accuracy of heart rate extraction. In this paper, a PPG heart rate extraction algorithm is proposed under the motion artifact intensity classification and removal (MAICR) framework. Firstly, it evaluates the motion artifact intensity using acceleration time-domain information and PPG frequency-domain information. Then, different heart rate estimation methods are selected according to motion artifact intensity, in ascending order. This corresponds to the use of the frequency-domain direct extraction method, the MNLMS adaptive filtering method, and the joint sparse spectral reconstruction method. Additionally, the overall framework of the algorithm (MAICR) is developed by considering the performance of various methods. Finally, experiments are conducted on the public dataset to demonstrate that the proposed algorithm is applicable to reduce the algorithmic complexity effectively while maintaining the accuracy in heart rate extraction at a certain degree.

SSTU: Swin-Spectral Transformer U-Net for hyperspectral whole slide image reconstruction

Whole Slide Imaging and Hyperspectral Microscopic Imaging provide great quality data with high spatial and spectral resolution for histopathology. Existing Hyperspectral Whole Slide Imaging systems combine the advantages of the techniques above, thus providing rich information for pathological diagnosis. However, it cannot avoid the problems of slow acquisition speed and mass data storage demand. Inspired by the spectral reconstruction task in computer vision and remote sensing, the Swin-Spectral Transformer U-Net (SSTU) has been developed to reconstruct Hyperspectral Whole Slide images (HWSis) from multiple Hyperspectral Microscopic images (HMis) of small Field of View and Whole Slide images (WSis). The Swin-Spectral Transformer (SST) module in SSTU takes full advantage of Transformer in extracting global attention. Firstly, Swin Transformer is exploited in space domain, which overcomes the high computation cost in Vision Transformer structures, while it maintains the spatial features extracted from WSis. Furthermore, Spectral Transformer is exploited to collect the long-range spectral features in HMis. Combined with the multi-scale encoder-bottleneck-decoder structure of U-Net, SSTU network is formed by sequential and symmetric residual connections of SSTs, which reconstructs a selected area of HWSi from coarse to fine. Qualitative and quantitative experiments prove the performance of SSTU in HWSi reconstruction task superior to other state-of-the-art spectral reconstruction methods.

Bi-directional hyperspectral reconstruction of cherry tomato: diagnosis of internal tissues maturation stage and composition.

Precision monitoring maturity in climacteric fruits like tomato is crucial for minimising losses within the food supply chain and enhancing pre- and post-harvest production and utilisation. This paper introduces an approach to analyse the precision maturation of tomato using hyperspectral tomography-like. A novel bi-directional spectral reconstruction method is presented, leveraging visible to near-infrared (Vis-NIR) information gathered from tomato spectra and their internal tissues (skin, pulp, and seeds). The study, encompassing 118 tomatoes at various maturation stages, employs a multi-block hierarchical principal component analysis combined with partial least squares for bi-directional reconstruction. The approach involves predicting internal tissue spectra by decomposing the overall tomato spectral information, creating a superset with eight latent variables for each tissue. The reverse process also utilises eight latent variables for reconstructing skin, pulp, and seed spectral data. The reconstruction of the tomato spectra presents a mean absolute percentage error of 30.44 % and 5.37 %, 5.25 % and 6.42 % and Pearson's correlation coefficient of 0.85, 0.98, 0.99 and 0.99 for the skin, pulp and seed, respectively. Quality parameters, including soluble solid content (%), chlorophyll (a.u.), lycopene (a.u.), and puncture force (N), were assessed and modelled with PLS with the original and reconstructed datasets, presenting a range of R2 higher than 0.84 in the reconstructed dataset. An empirical demonstration of the tomato maturation in the internal tissues revealed the dynamic of the chlorophyll and lycopene in the different tissues during the maturation process. The proposed approach for inner tomato tissue spectral inference is highly reliable, provides early indications and is easy to operate. This study highlights the potential of Vis-NIR devices in precision fruit maturation assessment, surpassing conventional labour-intensive techniques in cost-effectiveness and efficiency. The implications of this advancement extend to various agronomic and food chain applications, promising substantial improvements in monitoring and enhancing fruit quality.

Dual energy CT reconstruction using the constrained one step spectral image reconstruction algorithm.

The constrained one-step spectral CT Image Reconstruction method (cOSSCIR) has been developed to estimate basis material maps directly from spectral CT data using a model of the polyenergetic x-ray transmissions and incorporating convex constraints into the inversion problem. This 'one-step' approach has been shown to stabilize the inversion in the case of photon-counting CT, and may provide similar benefits to dual-kV systems that utilize integrating detectors. Since the approach does not require the same rays be acquired for every spectral measurement, cOSSCIR can apply to dual energy protocols and systems used clinically, such as fast and slow kV switching systems and dual sourcescanning. The purpose of this study is to investigate the use of cOSSCIR applied to dual-kV data, using both registered and unregistered spectral acquisitions, specifically slow and fast kV switching imaging protocols. For this application, cOSSCIR is investigated using inverse crime simulations and dual-kV experiments. This study is the first demonstration of cOSSCIR on the dual-kV reconstructionproblem. An integrating detector model was developed for the purpose of reconstructing dual-kV data, and an inverse crime study was used to validate the detector model within the cOSSCIR framework using a simulated pelvic phantom. Experiments were also used to evaluate cOSSCIR on the dual energy problem. Dual-kV data was obtained from a physical phantom containing analogs of adipose, bone, and liver tissues, with the aim of recovering the material coefficients in the bone and adipose basis material maps. cOSSCIR was applied to acquisitions where all rays performed both spectral measurements (registered) and fast and slow kV switching acquisitions (unregistered). cOSSCIR was also compared to two image-domain decomposition approaches, where image-domain methods are the conventional approach for decomposing unregistered spectraldata. Simulations demonstrate the application of cOSSCIR to the dual-kV inversion problem by successfully recovering the material basis maps on ideal data, while further showing that unregistered data presents a more challenging inversion problem. In our experimental reconstructions, the recovered basis material coefficient errors were found to be less than 6.5% in the bone, adipose, and liver regions for both registered and unregistered protocols. Similarly, the errors were less than 4% in the 50 keV virtual mono-energetic images, and the recovered material decomposition vectors nearly overlap their corresponding ground-truth vectors. Additionally, a preliminary two material decomposition study of iodine quantification recovered an average concentration of 9.2 mg/mL from a 10 mg/mL experimental iodineanalog. Using our integrating detector and spectral models, cOSCCIR is capable of accurately recovering material basis maps from dual-kV data for both registered and unregistered data. The material decomposition quantification compare favorably to the image domain approaches, and our results were not affected by the imaging protocol. Our results also suggest the extension of cOSSCIR to iodine quantification using two materialdecomposition.

Photon-counting spectral CT reconstruction with sparse and double low-rank components fusion

ObjectivePhoton-counting CT (computed tomography) has aroused more attention. The relatively high dose of X-ray increases concerns about radiation, dose reduction can mitigate radiation risk, however, projection data within energy channel suffers from strong noise, leading to degraded results. Besides, there is high correlation among channels since the multi-channel data is captured from the same object, which should be utilized to improve reconstruction quality. To suppress noise while preserving details in reconstruction and improve material decomposition accuracy, we proposed a novel optimization-based spectral CT reconstruction method with a fusion framework. MethodsTwo results were obtained by implementing two iterative reconstructions with different constraints and can be treated as complementary components. The first constraint is sparsity, the second constraint is double low-rank, moreover, a heuristic strategy is designed to solve double low-rank constrained optimization. We developed a guided filter based fusion framework to fuse sparse component and double low-rank component. ResultsExperimental results showed that our method achieved satisfying visualization, higher values of PSNR (peak signal-to-noise ratio) and SSIM (structure similarity), moreover, lower material decomposition error in this work.Conclusion.Our method can well perform spectral CT reconstruction under low-signal-to-ratio situation and it has decent performance in noise suppression, edge preserving and material discrimination.

A generalized land surface reflectance reconstruction method for aerosol retrieval: Application to the Particulate Observing Scanning Polarimeter (POSP) onboard GaoFen-5 (02) satellite

Newly launched sensors often face challenges in obtaining sufficient observations in a short time to support the development of land surface reflectance (LSR) constraint for high-precision aerosol retrieval. To address this problem, we proposed a generalized spectral LSR reconstruction method. First, short-term overlapping observation of the target sensor and a reference sensor currently in operation is utilized to generate a representative spectral LSR comparison dataset. Then, the differences in spectral LSR caused by spectral response functions (SRF) are explored to construct a spectral LSR reconstruction model, which can convert the reference sensor's spectral LSR to match the spectral LSR of the target sensor. Finally, long-term spectral LSR data of the reference sensor can be reconstructed for the target sensor to support the surface constraint for aerosol retrieval. We applied this method to the newly launched Particulate Observing Scanning Polarimeter (POSP) instrument onboard GaoFen-5 (02) satellite, using MODIS as the reference sensor. We used one-month concurrently overlapping observation of POSP and MODIS to quantify the differences in spectral LSR and built the spectral LSR reconstruction model. Four years of MODIS LSR data were then reconstructed to develop the multispectral surface constraint scheme (B2RE) for POSP, which enables POSP data for high-precision AOD retrieval. We retrieved the global AOD at 550 nm over land for December 2021 to February 2022. Validation results of POSP AOD against AErosol RObotic NETwork (AERONET) AOD show that the POSP AOD derived from 490 nm observation is the most accurate with the correlation coefficient (R) of 0.914, root mean square error (RMSE) of 0.080, mean relative error (MRE) of 1.60% and 82.5% of the values falling into the expected error (EE) envelope of ± (0.05 + 0.15AODAERONET). The spatial distribution of monthly POSP AOD also has good agreement with MODIS Deep Blue AOD product.

Using a Vegetation Index as a Proxy for Reliability in Surface Reflectance Time Series Reconstruction (RTSR)

Time series of optical remote sensing data are instrumental for monitoring vegetation dynamics, but are hampered by missing or noisy observations due to varying atmospheric conditions. Reconstruction methods have been proposed, most of which focus on time series of a single vegetation index. Under the assumption that relatively high vegetation index values can be considered as trustworthy, a successful approach is to adjust the smoothed value to the upper envelope of the time series. However, this assumption does not hold for surface reflectance in general. Clouds and cloud shadows result in, respectively, high and low values in the visible and near infrared part of the electromagnetic spectrum. A novel spectral Reflectance Time Series Reconstruction (RTSR) method is proposed. Smoothed values of surface reflectance values are adjusted to approach the trustworthy observations, using a vegetation index as a proxy for reliability. The Savitzky–Golay filter was used as the smoothing algorithm here, but different filters can be used as well. The RTSR was evaluated on 100 sites in Europe, with a focus on agriculture fields. Its potential was shown using different criteria, including smoothness and the ability to retain trustworthy observations in the original time series with RMSE values in the order of 0.01 to 0.03 in terms of surface reflectance.

A Rehabilitation of Pixel-Based Spectral Reconstruction from RGB Images.

Recently, many deep neural networks (DNN) have been proposed to solve the spectral reconstruction (SR) problem: recovering spectra from RGB measurements. Most DNNs seek to learn the relationship between an RGB viewed in a given spatial context and its corresponding spectra. Significantly, it is argued that the same RGB can map to different spectra depending on the context with respect to which it is seen and, more generally, that accounting for spatial context leads to improved SR. However, as it stands, DNN performance is only slightly better than the much simpler pixel-based methods where spatial context is not used. In this paper, we present a new pixel-based algorithm called A++ (an extension of the A+ sparse coding algorithm). In A+, RGBs are clustered, and within each cluster, a designated linear SR map is trained to recover spectra. In A++, we cluster the spectra instead in an attempt to ensure neighboring spectra (i.e., spectra in the same cluster) are recovered by the same SR map. A polynomial regression framework is developed to estimate the spectral neighborhoods given only the RGB values in testing, which in turn determines which mapping should be used to map each testing RGB to its reconstructed spectrum. Compared to the leading DNNs, not only does A++ deliver the best results, it is parameterized by orders of magnitude fewer parameters and has a significantly faster implementation. Moreover, in contradistinction to some DNN methods, A++ uses pixel-based processing, which is robust to image manipulations that alter the spatial context (e.g., blurring and rotations). Our demonstration on the scene relighting application also shows that, while SR methods, in general, provide more accurate relighting results compared to the traditional diagonal matrix correction, A++ provides superior color accuracy and robustness compared to the top DNN methods.

Computational spectrometer based on local feature-weighted spectral reconstruction.

The computational spectrometer enables the reconstruction of spectra from precalibrated information encoded. In the last decade, it has emerged as an integrated and low-cost paradigm with vast potential for applications, especially in portable or handheld spectral analysis devices. The conventional methods utilize a local-weighted strategy in feature spaces. These methods overlook the fact that the coefficients of important features could be too large to reflect differences in more detailed feature spaces during calculations. In this work, we report a local feature-weighted spectral reconstruction (LFWSR) method, and construct a high-accuracy computational spectrometer. Different from existing methods, the reported method learns a spectral dictionary via L 4-norm maximization for representing spectral curve features, and considers the statistical ranking of features. According to the ranking, weight features and update coefficients then calculate the similarity. What's more, the inverse distance weighted is utilized to pick samples and weight a local training set. Finally, the final spectrum is reconstructed utilizing the local training set and measurements. Experiments indicate that the reported method's two weighting processes produce state-of-the-art high accuracy.

SpectralMAE: Spectral Masked Autoencoder for Hyperspectral Remote Sensing Image Reconstruction

Accurate hyperspectral remote sensing information is essential for feature identification and detection. Nevertheless, the hyperspectral imaging mechanism poses challenges in balancing the trade-off between spatial and spectral resolution. Hardware improvements are cost-intensive and depend on strict environmental conditions and extra equipment. Recent spectral imaging methods have attempted to directly reconstruct hyperspectral information from widely available multispectral images. However, fixed mapping approaches used in previous spectral reconstruction models limit their reconstruction quality and generalizability, especially dealing with missing or contaminated bands. Moreover, data-hungry issues plague increasingly complex data-driven spectral reconstruction methods. This paper proposes SpectralMAE, a novel spectral reconstruction model that can take arbitrary combinations of bands as input and improve the utilization of data sources. In contrast to previous spectral reconstruction techniques, SpectralMAE explores the application of a self-supervised learning paradigm and proposes a masked autoencoder architecture for spectral dimensions. To further enhance the performance for specific sensor inputs, we propose a training strategy by combining random masking pre-training and fixed masking fine-tuning. Empirical evaluations on five remote sensing datasets demonstrate that SpectralMAE outperforms state-of-the-art methods in both qualitative and quantitative metrics.

Performance-Enhanced Static Modulated Fourier Transform Spectrometer with a Spectral Reconstruction

A static modulated Fourier transform spectrometer has been noted to be a compact and fast evaluation tool for spectroscopic inspection, and many novel structures have been reported to support its performance. However, it still suffers from poor spectral resolution due to the limited sampling data points, which marks its intrinsic drawback. In this paper, we outline the enhanced performance of a static modulated Fourier transform spectrometer with a spectral reconstruction method that can compensate for the insufficient data points. An enhanced spectrum can be reconstructed by applying a linear regression method to a measured interferogram. We obtain the transfer function of a spectrometer by analyzing what interferogram can be detected with different values of parameters such as focal length of the Fourier lens, mirror displacement, and wavenumber range, instead of direct measurement of the transfer function. Additionally, the optimal experimental conditions for the narrowest spectral width are investigated. Application of the spectral reconstruction method achieves an improved spectral resolution from 74 cm-1 when spectral reconstruction is not applied to 8.9 cm-1, and a narrowed spectral width from 414 cm-1 to 371 cm-1, which are close to the values of the spectral reference. In conclusion, the spectral reconstruction method in a compact static modulated Fourier transform spectrometer effectively enhances its performance without any additional optic in the structure.

Two-dimensional effects on electrostatic instabilities in Hall thrusters. II. Comparison of particle-in-cell simulation results with linear theory dispersion relations

In Paper I, we successfully used an external circuit to significantly damp the Breathing Mode (BM) oscillations in 2D particle-in-cell self-consistent simulations of the axial–azimuthal plane of a Hall thruster. We also introduced the two-point power spectral density reconstruction method (PSD2P) used to analyze electrostatic instabilities and generate dispersion diagrams in azimuthal and axial directions, at various times during the BM period. Here, a 3D Dispersion Relation (DR) for electrostatic modes is calculated by linearizing the continuity/momentum fluid equations for electrons and ions. We show that by taking the appropriate limits, this relation can be simplified to derive the DRs of some well-known E×B instabilities, such as the electron cyclotron drift instability and its evolution to the Ion Acoustic Wave (IAW), and the Ion Transit-Time Instability (ITTI). The PSD2P diagrams demonstrate the importance of considering the 2D nature of the IAW and ITTI, which have been previously considered to be mono-dimensional (azimuthal and axial, respectively). In particular, we show that the IAW grows near the maximum of the magnetic field and due to its axial components propagates toward both the anode and the cathode (in addition to the well-known azimuthal propagation). The resulting wavefront is, therefore, bent. By analogy to the propagation of acoustic waves in gases, it is proposed that the cause of the IAW wavefront bending is the strong electron temperature gradients in the axial direction. We also show that the ITTI has a strong positive growth rate when a small azimuthal component is present. Finally, we observe that the ITTI significantly affects the discharge current.

Two-dimensional effects on electrostatic instabilities in Hall thrusters. I. Insights from particle-in-cell simulations and two-point power spectral density reconstruction techniques

Using 2D particle-in-cell (PIC) simulations coupled to a fluid description of the gas dynamics, we study the electrostatic instabilities developing in the axial–azimuthal plane of a Hall thruster, during several periods of a low-frequency oscillation (the so-called breathing mode at 10 kHz). As done in experiments, the 2D PIC-MCC (Monte Carlo collision) code is coupled to an electrical circuit in order to partially damp the (otherwise large) discharge current fluctuations at the breathing mode frequency. The different electrostatic higher frequency modes that develop in the plasma are analyzed using a two-point power spectral density reconstruction method, which allows us to generate the dispersion diagrams (in the frequency-wavenumber space) along the axial and azimuthal directions and at different times during the low-frequency breathing mode oscillations. This technique allows us to distinguish between different well-identified instabilities: the electron cyclotron drift instability and its evolution toward an ion acoustic wave and the ion transit time instability. These instabilities are usually considered unidirectional (either axial or azimuthal); however, it is shown here that they exist in both directions. This two-dimensional character is instrumental in understanding where these instabilities grow and how they propagate in the thruster channel and plume. A theoretical discussion of this aspect is proposed in Paper II. The effects of (i) the azimuthal length of the simulation box and (ii) the electron temperature injection at the cathode are also discussed.

Evaluating the Performance of Different Cameras for Spectral Reconstruction

Spectral Reconstruction (SR) algorithms seek to map RGB images to their hyperspectral image counterparts. Statistical methods such as regression, sparse coding, and deep neural networks are used to determine the SR mapping. All these algorithms are optimized ‘blindly’ and the provenance of the RGBs is not considered. In this paper, we benchmark the performance of SR methods—in order of increasing complexity: regression, sparse coding, and deep neural network—when different RGB camera spectral sensitivity functions are used. In effect, we ask: “Are some cameras better able to recover spectra from RGBs than others?”. In our experiments, RGB images are generated by numerical integration for a fixed set of hyperspectral images using 9 different camera response functions (each from a different camera manufacturer) plus the CIE 1964 color matching functions. Then, we train SR methods on the respective RGB image sets. Our experiments show three important results. First, different cameras <strong>do</strong> support slightly better or worse spectral reconstruction but, secondly, that changing the spectral sensitivities alone does not change the ranking of different algorithms. Finally, we show that sometimes switching the used camera for SR can give a greater performance boost than switching to use a more complex SR method.

Research on the deep learning-based exposure invariant spectral reconstruction method.

The surface spectral reflectance of an object is the key factor for high-fidelity color reproduction and material analysis, and spectral acquisition is the basis of its applications. Based on the theoretical imaging model of a digital camera, the spectral reflectance of any pixels in the image can be obtained through spectral reconstruction technology. This technology can avoid the application limitations of spectral cameras in open scenarios and obtain high spatial resolution multispectral images. However, the current spectral reconstruction algorithms are sensitive to the exposure variant of the test images. That is, when the exposure of the test image is different from that of the training image, the reconstructed spectral curve of the test object will deviate from the real spectral to varying degrees, which will lead to the spectral data of the target object being accurately reconstructed. This article proposes an optimized method for spectral reconstruction based on data augmentation and attention mechanisms using the current deep learning-based spectral reconstruction framework. The proposed method is exposure invariant and will adapt to the open environment in which the light is easily changed and the illumination is non-uniform. Thus, the robustness and reconstruction accuracy of the spectral reconstruction model in practical applications are improved. The experiments show that the proposed method can accurately reconstruct the shape of the spectral reflectance curve of the test object under different test exposure levels. And the spectral reconstruction error of our method at different exposure levels is significantly lower than that of the existing methods, which verifies the proposed method's effectiveness and superiority.

Imaging of polychromatic sources through Compton spectral reconstruction

Objective. Study the performance of a spectral reconstruction method for Compton imaging of polychromatic sources and compare it to standard Compton reconstruction based on the selection of photopeak events. Approach. The proposed spectral and the standard photopeak reconstruction methods are used to reconstruct images from simulated sources emitting simultaneously photons of 140, 245, 364 and 511 keV. Data are simulated with perfect and realistic energy resolutions and including Doppler broadening. We compare photopeak and spectral reconstructed images both qualitatively and quantitatively by means of activity recovery coefficient and spatial resolution. Main results. The presented method allows improving the images of polychromatic sources with respect to standard reconstruction methods. The main reasons for this improvement are the increase of available statistics and the reduction of contamination from higher initial photon energies. The reconstructed images present lower noise, higher activity recovery coefficient and better spatial resolution. The improvements become more sensible as the energy resolution of the detectors decreases. Significance. Compton cameras have been studied for their capability of imaging polychromatic sources, thus allowing simultaneous imaging of multiple radiotracers. In such scenarios, Compton images are conventionally reconstructed for each emission energy independently, selecting only those measured events depositing a total energy within a fixed window around the known emission lines. We propose to employ a spectral image reconstruction method for polychromatic sources, which allows increasing the available statistics by using the information from events with partial energy deposition. The detector energy resolution influences the energy window used to select photopeak events and therefore the level of contamination by higher energies. The spectral method is expected to have a more important impact as the detector resolution worsens. In this paper we focus on energy ranges from nuclear medical imaging and we consider realistic energy resolutions.

Deep Learning-Based Spectrum Reconstruction Method for Raman Spectroscopy

Raman spectroscopy, measured by a Raman spectrometer, is usually disturbed by the instrument response function and noise, which leads to certain measurement error and further affects the accuracy of substance identification. In this paper, we propose a spectral reconstruction method which combines the existing maximum a posteriori (MAP) method and deep learning (DL) to recover the degraded Raman spectrum. The proposed method first employs the MAP method to reconstruct the measured Raman spectra, so as to obtain preliminary estimated Raman spectra. Then, a convolutional neural network (CNN) is trained by using the preliminary estimated Raman spectra and the real Raman spectra to learn the mapping from the preliminary estimated Raman spectra to the real Raman spectra, so as to achieve a better spectral reconstruction effect than merely using the MAP method or a CNN. To prove the effectiveness of the proposed spectral reconstruction method, we employed the proposed method and some traditional spectral reconstruction methods to reconstruct the simulated and measured Raman spectra, respectively. The experimental results show that compared with traditional methods, the estimated Raman spectra reconstructed by the proposed method are closer to the real Raman spectra.