Unified Noise-aware Network for Low-count PET Denoising with Varying Count Levels.

As PET imaging is accompanied by substantial radiation exposure and cancer risk, reducing radiation dose in PET scans is an important topic. However, low-count PET scans often suffer from high image noise, which can negatively impact image quality and diagnostic performance. Recent advances in deep learning have shown great potential for recovering underlying signal from noisy counterparts. However, neural networks trained on a specific noise level cannot be easily generalized to other noise levels due to different noise amplitude and variances. To obtain optimal denoised results, we may need to train multiple networks using data with different noise levels. But this approach may be infeasible in reality due to limited data availability. Denoising dynamic PET images presents additional challenge due to tracer decay and continuously changing noise levels across dynamic frames. To address these issues, we propose a Unified Noise-aware Network (UNN) that combines multiple sub-networks with varying denoising power to generate optimal denoised results regardless of the input noise levels. Evaluated using large-scale data from two medical centers with different vendors, presented results showed that the UNN can consistently produce promising denoised results regardless of input noise levels, and demonstrate superior performance over networks trained on single noise level data, especially for extremely low-count data.

Tether-entangled conjugated helices.

A new design concept, tether-entangled conjugated helices (TECHs), is introduced for helical polyaromatic molecules. TECHs consist of a linear polyaromatic ladder backbone and periodically entangling tethers with the same planar chirality. By limiting the length of tether, all tethers synchronously bend and twist the backbone with the same manner, and change it into a helical ribbon with a determinate helical chirality. The 3D helical features are customizable via modular synthesis by using two types of synthons, the planar chiral tethering unit (C 2 symmetry) and the docking unit (C 2h symmetry), and no post chiral resolution is needed. Moreover, TECHs possess persistent chiral properties due to the covalent locking of helical configuration by tethers. Concave-type and convex-type oligomeric TECHs are prepared as a proof-of-concept. Unconventional double-helix π-dimers are observed in the single crystals of concave-type TECHs. Theoretical studies indicate the smaller binding energies in double-helix π-dimers than conventional planar π-dimers. A concentration-depend emission is found for concave-type TECHs, probably due to the formation of double-helix π-dimers in the excited state. All TECHs show strong circularly polarized luminescence (CPL) with dissymmetric factors (|g lum|) generally over 10-3. Among them, the (P)-T4-tBu shows the highest |g lum| of 1.0 × 10-2 and a high CPL brightness of 316 M-1 cm-1.

Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation.

Image-to-image translation is a vital component in medical imaging processing, with many uses in a wide range of imaging modalities and clinical scenarios. Previous methods include Generative Adversarial Networks (GANs) and Diffusion Models (DMs), which offer realism but suffer from instability and lack uncertainty estimation. Even though both GAN and DM methods have individually exhibited their capability in medical image translation tasks, the potential of combining a GAN and DM to further improve translation performance and to enable uncertainty estimation remains largely unexplored. In this work, we address these challenges by proposing a Cascade Multi-path Shortcut Diffusion Model (CMDM) for high-quality medical image translation and uncertainty estimation. To reduce the required number of iterations and ensure robust performance, our method first obtains a conditional GAN-generated prior image that will be used for the efficient reverse translation with a DM in the subsequent step. Additionally, a multi-path shortcut diffusion strategy is employed to refine translation results and estimate uncertainty. A cascaded pipeline further enhances translation quality, incorporating residual averaging between cascades. We collected three different medical image datasets with two sub-tasks for each dataset to test the generalizability of our approach. Our experimental results found that CMDM can produce high-quality translations comparable to state-of-the-art methods while providing reasonable uncertainty estimations that correlate well with the translation error.

TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction.

Inter-frame motion in dynamic cardiac positron emission tomography (PET) using rubidium-82 (82Rb) myocardial perfusion imaging impacts myocardial blood flow (MBF) quantification and the diagnosis accuracy of coronary artery diseases. However, the high cross-frame distribution variation due to rapid tracer kinetics poses a considerable challenge for inter-frame motion correction, especially for early frames where intensity-based image registration techniques often fail. To address this issue, we propose a novel method called Temporally and Anatomically Informed Generative Adversarial Network (TAI-GAN) that utilizes an all-to-one mapping to convert early frames into those with tracer distribution similar to the last reference frame. The TAI-GAN consists of a feature-wise linear modulation layer that encodes channel-wise parameters generated from temporal information and rough cardiac segmentation masks with local shifts that serve as anatomical information. Our proposed method was evaluated on a clinical 82Rb PET dataset, and the results show that our TAI-GAN can produce converted early frames with high image quality, comparable to the real reference frames. After TAI-GAN conversion, the motion estimation accuracy and subsequent myocardial blood flow (MBF) quantification with both conventional and deep learning-based motion correction methods were improved compared to using the original frames. The code is available at https://github.com/gxq1998/TAI-GAN.

DuDoCFNet: Dual-Domain Coarse-to-Fine Progressive Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECT.

Single-Photon Emission Computed Tomography (SPECT) is widely applied for the diagnosis of coronary artery diseases. Low-dose (LD) SPECT aims to minimize radiation exposure but leads to increased image noise. Limited-view (LV) SPECT, such as the latest GE MyoSPECT ES system, enables accelerated scanning and reduces hardware expenses but degrades reconstruction accuracy. Additionally, Computed Tomography (CT) is commonly used to derive attenuation maps ( µ -maps) for attenuation correction (AC) of cardiac SPECT, but it will introduce additional radiation exposure and SPECT-CT misalignments. Although various methods have been developed to solely focus on LD denoising, LV reconstruction, or CT-free AC in SPECT, the solution for simultaneously addressing these tasks remains challenging and under-explored. Furthermore, it is essential to explore the potential of fusing cross-domain and cross-modality information across these interrelated tasks to further enhance the accuracy of each task. Thus, we propose a Dual-Domain Coarse-to-Fine Progressive Network (DuDoCFNet), a multi-task learning method for simultaneous LD denoising, LV reconstruction, and CT-free µ -map generation of cardiac SPECT. Paired dual-domain networks in DuDoCFNet are cascaded using a multi-layer fusion mechanism for cross-domain and cross-modality feature fusion. Two-stage progressive learning strategies are applied in both projection and image domains to achieve coarse-to-fine estimations of SPECT projections and CT-derived µ -maps. Our experiments demonstrate DuDoCFNet's superior accuracy in estimating projections, generating µ -maps, and AC reconstructions compared to existing single- or multi-task learning methods, under various iterations and LD levels. The source code of this work is available at https://github.com/XiongchaoChen/DuDoCFNet-MultiTask.

Tethered Helical Ladder-Type Aromatic Lactams.

Tethered nonplanar aromatics (TNAs) make up an important class of nonplanar aromatic compounds showing unique features. However, the knowledge on the synthesis, structures, and properties of TNAs remains insufficient. In this work, a new type of TNAs, the tethered aromatic lactams, is synthesized via Pd-catalyzed consecutive intramolecular direct arylations. These molecules possess a helical ladder-type conjugated system of up to 13 fused rings. The overall yields ranged from 3.4 to 4.3%. The largest of the tethered aromatic lactams, 6L-Bu-C14, demonstrates a guest-adaptive hosting capability of TNAs for the first time. When binding fullerene guests, the cavity of 6L-Bu-C14 became more circular to better accommodate spherical fullerene molecules. The host-guest interaction is thoroughly studied by X-ray crystallography, theoretical calculations, fluorescence titration, and nuclear magnetic resonance (NMR) titration experiments. 6L-Bu-C14 shows stronger binding with C70 than with C60 due to the better convex-concave π-π interaction. P and M enantiomers of all tethered aromatic lactams show distinct and persistent chiroptical properties and demonstrate the potential of chiral TNAs as circularly polarized luminescence (CPL) emitters.

Population-based deep image prior for dynamic PET denoising: A data-driven approach to improve parametric quantification.

The high noise level of dynamic Positron Emission Tomography (PET) images degrades the quality of parametric images. In this study, we aim to improve the quality and quantitative accuracy of Ki images by utilizing deep learning techniques to reduce the noise in dynamic PET images. We propose a novel denoising technique, Population-based Deep Image Prior (PDIP), which integrates population-based prior information into the optimization process of Deep Image Prior (DIP). Specifically, the population-based prior image is generated from a supervised denoising model that is trained on a prompts-matched static PET dataset comprising 100 clinical studies. The 3D U-Net architecture is employed for both the supervised model and the following DIP optimization process. We evaluated the efficacy of PDIP for noise reduction in 25%-count and 100%-count dynamic PET images from 23 patients by comparing with two other baseline techniques: the Prompts-matched Supervised model (PS) and a conditional DIP (CDIP) model that employs the mean static PET image as the prior. Both the PS and CDIP models show effective noise reduction but result in smoothing and removal of small lesions. In addition, the utilization of a single static image as the prior in the CDIP model also introduces a similar tracer distribution to the denoised dynamic frames, leading to lower Ki in general as well as incorrect Ki in the descending aorta. By contrast, as the proposed PDIP model utilizes intrinsic image features from the dynamic dataset and a large clinical static dataset, it not only achieves comparable noise reduction as the supervised and CDIP models but also improves lesion Ki predictions.

Changing Cinnamaldehyde Skeleton Achieves Antibacterial Nanoswitch.

Changeable substituent groups of organic molecules can provide an opportunity to clarify the antibacterial mechanism of organic molecules by tuning the electron cloud density of their skeleton. However, understanding the antibacterial mechanism of organic molecules is challenging. Herein, we reported a molecular view strategy for clarifying the antibacterial switch mechanism by tuning electron cloud density of cinnamaldehyde molecule skeleton. The cinnamaldehyde and its derivatives were self-assembled into nanosheets with excellent water solubility, respectively. The experimental results show that α-bromocinnamaldehyde (BCA) nanosheets exhibits unprecedented antibacterial activity, but there is no antibacterial activity for α-methylcinnamaldehyde nanosheets. Therefore, the BCA nanosheets and α-methylcinnamaldehyde nanosheets achieve an antibacterial switch. Theoretical calculations further confirmed that the electron-withdrawing substituent of the bromine atom leads to a lower electron cloud density of the aldehyde group than that of the electron-donor substituent of the methyl group at the α-position of the cinnamaldehyde skeleton, which is a key point in elucidating the antimicrobial switch mechanism. The excellent biocompatibility of BCA nanosheets was confirmed by CCK-8. The mouse wound infection model, H&E staining, and the crawling ability of drosophila larvae show that as-prepared BCA nanosheets are safe and promising for wound healing. This study provides a new strategy for the synthesis of low-cost organic nanomaterials with good biocompatibility. It is expected to expand the application of natural organic small molecule materials in antimicrobial agents.

MOF-derived NiAl2O4/NiCo2O4 porous materials as supercapacitors with high electrochemical performance.

Metal-organic framework compounds are extensively utilized in various fields, such as electrode materials, owing to their distinctive porous structure and significant specific surface area. In this study, NiCoAl-MOF metal-organic framework precursors were synthesized by a solvothermal method, and NiAl2O4/NiCo2O4 electrode materials were prepared by the subsequent calcination of the precursor. These materials were characterized by XRD, XPS, BET tests, and SEM, and the electrochemical properties of the electrode materials were tested by CV and GCD methods. BET tests showed that NiAl2O4/NiCo2O4 has an abundant porous structure and a large specific surface area of up to 105 m2 g-1. The specific capacitance of NiAl2O4/NiCo2O4 measured by the GCD method reaches up to 2870.83 F g-1 at a current density of 1 A g-1. The asymmetric supercapacitor NiAl2O4/NiCo2O4//AC assembled with activated carbon electrodes has a maximum energy density of 166.98 W h kg-1 and a power density of 750.00 W kg-1 within a voltage window of 1.5 V. In addition, NiAl2O4/NiCo2O4 materials have good cycling stability. These advantages make it a good candidate for the application of high-performance supercapacitors.

Promotion of SO2 resistance of Ce-La/TiO2 denitrification catalysts by V doping.

Conventional cerium-based denitrification catalysts show good catalytic activity at moderate and high temperatures, but their denitrification performance may be decreased due to poisoning by SO2 in the flue gas. In this paper, V was introduced into Ce-La/TiO2 catalysts by a ball-milling method, and the effects of the V content on catalyst denitrification performance and SO2 resistance were investigated. Fourier-transform diffuse reflectance in situ infrared spectroscopy was used to examine the denitrification mechanism and evaluate the catalysts for surface acidity, redox characteristics, and SO2 adsorption. After introducing V, Brønsted acids played the dominant role in the catalytic reaction by increasing the number of acidic sites on the catalyst surface, adsorbing NH3 to participate in the reaction, and improving the sulfur resistance by inhibiting SO2 poisoning. The Ce3+ and O ratio on the catalyst surface were also enhanced by V doping, which reduced interactions between SO2 and the primary metal oxide active ingredients. The modified catalyst inhibited the formation of sulfate species on the catalyst surface and prevented the generation of additional nitrate species on the surface, which protected the main active sites. After V doping, the NH3-SCR reaction on the catalyst surface followed the Langmuir-Hinshelwood mechanism.

Carbon quantum dots modified and Y3+ doped Ni3(NO3)2(OH)4 nanospheres with excellent battery-like supercapacitor performance.

Supercapacitor is an important energy storage device widely used in the automobile industry, military production, and communication equipment because of its fast charge-discharge rate, and high power density. Herein, carbon quantum dots modified and Y3+ doped Ni3(NO3)2(OH)4 (NiY@CQDs) nanospheres are prepared by a solvothermal method and used as an electrode material. The electrochemical properties of NiY@CQDs were measured in a three-electrode system. An asymmetric supercapacitor (ASC) cell was assembled with activated carbon (AC) as the anode and NiY@CQDs as the cathode. The electrochemical properties of the ASC device were measured in a two-electrode system. Experimental results show the shape of NiY@CQDs is petal-shaped and the introducing carbon quantum dots and doping Y3+ significantly increases the specific surface area, conductivity, and specific capacitance of Ni3(NO3)2(OH)4. The mass-specific capacitance of NiY@CQDs reaches up to 2944 F g-1 at a current density of 1 A g-1. The asymmetric supercapacitor of NiY@CQDs//AC has a high energy density of 138.65 Wh kg-1 at a power density of 1500 W kg-1, displaying a wide range of application prospects in the energy storage area.

Zero-Dimensional Hybrid Antimony Chloride with Near-Unity Broad-Band Orange-Red Emission toward Solid-State Lighting.

Zero-dimensional (0D) hybrid metal halides are attractive owing to their distinctive structure as well as photoluminescence (PL) characteristics. To discover 0D hybrid metal halides with high photoluminescence quantum yield and good stability is of great significance for white light-emitting diodes (LEDs). Herein, a novel hybrid antimony chloride (CTP)2SbCl5 is synthesized, which shows a bright broad-band orange-red emission peaking at 620 nm under the low energy excitation (365 nm), achieving an excellent photoluminescence quantum yield of 96.8%. In addition, (CTP)2SbCl5 shows an additional emission peaking at 470 nm when excited at high energy (323 nm). PL spectra and density functional theory results demonstrate that the observed dual-band emission originates from the singlet and triplet self-trapped excitons confined in isolated [SbCl5]2- square pyramids. Moreover, (CTP)2SbCl5 presents relatively superior air stability, and the PL intensity still maintains 78% of the initial PL intensity when exposed to the air for above 2 weeks. Benefiting from high-efficiency PL emission and good stability of (CTP)2SbCl5, a stable warm white LED device with a 92.3% color rendering index was prepared by coating blue phosphor BaMgAl10O17:Eu2+, green (Sr,Ba)2SiO4:Eu2+, and orange-red (CTP)2SbCl5 on a 365 nm LED chip. This work provides an efficient luminescent material and also demonstrates the potential application of 0D hybrid antimony chloride in solid-state lighting.

Generation of Whole-Body FDG Parametric Ki Images from Static PET Images Using Deep Learning.

FDG parametric Ki images show great advantage over static SUV images, due to the higher contrast and better accuracy in tracer uptake rate estimation. In this study, we explored the feasibility of generating synthetic Ki images from static SUV ratio (SUVR) images using three configurations of U-Nets with different sets of input and output image patches, which were the U-Nets with single input and single output (SISO), multiple inputs and single output (MISO), and single input and multiple outputs (SIMO). SUVR images were generated by averaging three 5-min dynamic SUV frames starting at 60 minutes post-injection, and then normalized by the mean SUV values in the blood pool. The corresponding ground truth Ki images were derived using Patlak graphical analysis with input functions from measurement of arterial blood samples. Even though the synthetic Ki values were not quantitatively accurate compared with ground truth, the linear regression analysis of joint histograms in the voxels of body regions showed that the mean R2 values were higher between U-Net prediction and ground truth (0.596, 0.580, 0.576 in SISO, MISO and SIMO), than that between SUVR and ground truth Ki (0.571). In terms of similarity metrics, the synthetic Ki images were closer to the ground truth Ki images (mean SSIM = 0.729, 0.704, 0.704 in SISO, MISO and MISO) than the input SUVR images (mean SSIM = 0.691). Therefore, it is feasible to use deep learning networks to estimate surrogate map of parametric Ki images from static SUVR images.

[Degradation of Ciprofloxacin by CuNiFe LDHs/BiO2-x Heterojunction-activated Peroxymonosulfate Under Visible Light Irradiation].

Herein, a CuNiFe LDHs/BiO2-x composite photocatalyst was successfully synthesized using a hydrothermal method and applied to activate peroxymonosulfate to degrade ciprofloxacin under visible light irradiation. Owing to the synergistic effect of photocatalysis and PMS activation, a high removal efficiency of CIP up to 88.3% was achieved. The prepared photocatalysts were characterized using XRD, FT-IR, SEM, XPS, UV-Vis DRS, and other methods. The optimal loading amount of CuNiFe LDHs was determined, and the effects of PMS dosage, initial pH value, and inorganic anions (Cl-, CO32-, and NO3-) on the degradation were investigated. Electron paramagnetic resonance and free radical trapping experiments demonstrated that·OH and h+ were the main active species for degrading CIP, and the possible degradation mechanism of the system was proposed.

FedFTN: Personalized federated learning with deep feature transformation network for multi-institutional low-count PET denoising.

Low-count PET is an efficient way to reduce radiation exposure and acquisition time, but the reconstructed images often suffer from low signal-to-noise ratio (SNR), thus affecting diagnosis and other downstream tasks. Recent advances in deep learning have shown great potential in improving low-count PET image quality, but acquiring a large, centralized, and diverse dataset from multiple institutions for training a robust model is difficult due to privacy and security concerns of patient data. Moreover, low-count PET data at different institutions may have different data distribution, thus requiring personalized models. While previous federated learning (FL) algorithms enable multi-institution collaborative training without the need of aggregating local data, addressing the large domain shift in the application of multi-institutional low-count PET denoising remains a challenge and is still highly under-explored. In this work, we propose FedFTN, a personalized federated learning strategy that addresses these challenges. FedFTN uses a local deep feature transformation network (FTN) to modulate the feature outputs of a globally shared denoising network, enabling personalized low-count PET denoising for each institution. During the federated learning process, only the denoising network's weights are communicated and aggregated, while the FTN remains at the local institutions for feature transformation. We evaluated our method using a large-scale dataset of multi-institutional low-count PET imaging data from three medical centers located across three continents, and showed that FedFTN provides high-quality low-count PET images, outperforming previous baseline FL reconstruction methods across all low-count levels at all three institutions.

Federated Transfer Learning for Low-dose PET Denoising: A Pilot Study with Simulated Heterogeneous Data.

Positron emission tomography (PET) with a reduced injection dose, i.e., low-dose PET, is an efficient way to reduce radiation dose. However, low-dose PET reconstruction suffers from a low signal-to-noise ratio (SNR), affecting diagnosis and other PET-related applications. Recently, deep learning-based PET denoising methods have demonstrated superior performance in generating high-quality reconstruction. However, these methods require a large amount of representative data for training, which can be difficult to collect and share due to medical data privacy regulations. Moreover, low-dose PET data at different institutions may use different low-dose protocols, leading to non-identical data distribution. While previous federated learning (FL) algorithms enable multi-institution collaborative training without the need of aggregating local data, it is challenging for previous methods to address the large domain shift caused by different low-dose PET settings, and the application of FL to PET is still under-explored. In this work, we propose a federated transfer learning (FTL) framework for low-dose PET denoising using heterogeneous low-dose data. Our experimental results on simulated multi-institutional data demonstrate that our method can efficiently utilize heterogeneous low-dose data without compromising data privacy for achieving superior low-dose PET denoising performance for different institutions with different low-dose settings, as compared to previous FL methods.

High-Efficiency Narrow-Band Green-Emitting Manganese(II) Halide for Multifunctional Applications.

Zero-dimensional (0D) Mn2+-based metal halides used as luminescent materials and scintillators have become a research hotspot in the field of photoelectric materials and devices due to their unique composition, structure, and fluorescence properties. It is of great value to explore new Mn2+-based metal halides to achieve multifunctional applications. Herein, the novel 0D Mn2+-based metal halide single crystal (BPTP)2MnBr4 is synthesized by a simple solvent-antisolvent recrystallization method. Under excitation at 468 nm, the (BPTP)2MnBr4 single crystal shows a pronounced narrow-band green luminescence centered at 515 nm derived from the d-d transition of the Mn2+ ion. This emission has a relatively narrow full width at half maximum of 43 nm and a high photoluminescence quantum yield (PLQY) of 82%. In addition, (BPTP)2MnBr4 exhibits good thermal stability at 393 K with a retention of 79% of the initial photoluminescence intensity at 298 K. Benefiting from its strong blue light excitation, high PLQY, and good thermal stability, we manufacture an ideal white light-emitting diode (LED) device using a 460 nm blue LED chip, green-emitting (BPTP)2MnBr4, and commercial K2SiF6:Mn4+ red phosphor. Under 20 mA drive current, the LED shows a high luminous efficiency of 112 lm/W and a wide color gamut of 110.8%, according to the National Television System Committee standard. In addition, (BPTP)2MnBr4 crystals show a strong X-ray absorption. Based on the commercial Lu3Al5O12:Ce3+ scintillator, the calculated light yield of (BPTP)2MnBr4 reaches up to about 136,000 photons/MeV and the detection limit reaches 0.282 µGyair s-1. Additionally, a melt quenching approach is used to construct a (BPTP)2MnBr4 clear glass scintillation screen, realizing a spatial resolution of 10.1 lp/mm. The proper performances of (BPTP)2MnBr4 as phosphor-converted LED materials and the X-ray scintillator with the addition of eco-friendly, low-cost solution processability make 0D Mn2+-based metal halides potential luminescent materials for multifunctional applications.

Deep-Learning-Based Few-Angle Cardiac SPECT Reconstruction Using Transformer.

Convolutional neural networks (CNNs) have been extremely successful in various medical imaging tasks. However, because the size of the convolutional kernel used in a CNN is much smaller than the image size, CNN has a strong spatial inductive bias and lacks a global understanding of the input images. Vision Transformer, a recently emerged network structure in computer vision, can potentially overcome the limitations of CNNs for image-reconstruction tasks. In this work, we proposed a slice-by-slice Transformer network (SSTrans-3D) to reconstruct cardiac SPECT images from 3D few-angle data. To be specific, the network reconstructs the whole 3D volume using a slice-by-slice scheme. By doing so, SSTrans-3D alleviates the memory burden required by 3D reconstructions using Transformer. The network can still obtain a global understanding of the image volume with the Transformer attention blocks. Lastly, already reconstructed slices are used as the input to the network so that SSTrans-3D can potentially obtain more informative features from these slices. Validated on porcine, phantom, and human studies acquired using a GE dedicated cardiac SPECT scanner, the proposed method produced images with clearer heart cavity, higher cardiac defect contrast, and more accurate quantitative measurements on the testing data as compared with a deep U-net.

DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT.

Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is widely applied for the diagnosis of cardiovascular diseases. Attenuation maps (µ-maps) derived from computed tomography (CT) are utilized for attenuation correction (AC) to improve the diagnostic accuracy of cardiac SPECT. However, in clinical practice, SPECT and CT scans are acquired sequentially, potentially inducing misregistration between the two images and further producing AC artifacts. Conventional intensity-based registration methods show poor performance in the cross-modality registration of SPECT and CT-derived µ-maps since the two imaging modalities might present totally different intensity patterns. Deep learning has shown great potential in medical imaging registration. However, existing deep learning strategies for medical image registration encoded the input images by simply concatenating the feature maps of different convolutional layers, which might not fully extract or fuse the input information. In addition, deep-learning-based cross-modality registration of cardiac SPECT and CT-derived µ-maps has not been investigated before. In this paper, we propose a novel Dual-Channel Squeeze-Fusion-Excitation (DuSFE) co-attention module for the cross-modality rigid registration of cardiac SPECT and CT-derived µ-maps. DuSFE is designed based on the co-attention mechanism of two cross-connected input data streams. The channel-wise or spatial features of SPECT and µ-maps are jointly encoded, fused, and recalibrated in the DuSFE module. DuSFE can be flexibly embedded at multiple convolutional layers to enable gradual feature fusion in different spatial dimensions. Our studies using clinical patient MPI studies demonstrated that the DuSFE-embedded neural network generated significantly lower registration errors and more accurate AC SPECT images than existing methods. We also showed that the DuSFE-embedded network did not over-correct or degrade the registration performance of motion-free cases. The source code of this work is available at https://github.com/XiongchaoChen/DuSFE_CrossRegistration.

Managing Multiple Halide-Related Defects for Efficient and Stable Inorganic Perovskite Solar Cells.

Halide-related surface defects on inorganic halide perovskite not only induce charge recombination but also severely limit the long-term stability of perovskite solar cells. Herein, adopting density functional theory calculation, we verify that iodine interstitials (Ii ) has a low formation energy similar to that of the iodine vacancy (VI ) and is also readily formed on the surface of all-inorganic perovskite, and it is regarded to function as an electron trap. We screen a specific 2,6-diaminopyridine (2,6-DAPy) passivator, which, with the aid of the combined effects from halogen-Npyridine and coordination bonds, not only successfully eliminates the Ii and dissociative I2 but also passivates the abundant VI . Furthermore, the two symmetric neighboring -NH2 groups interact with adjacent halides of the octahedral cluster by forming hydrogen bonds, which further promotes the adsorption of 2,6-DAPy molecules onto the perovskite surface. Such synergetic effects can significantly passivate harmful iodine-related defects and undercoordinated Pb2+ , prolong carrier lifetimes and facilitate the interfacial hole transfer. Consequently, these merits enhance the power-conversion efficiency (PCE) from 19.6 % to 21.8 %, the highest value for this type of solar cells, just as importantly, the 2,6-DAPy-treated CsPbI3-x Brx films show better environmental stability.