Non-contrast CT (NCCT) is the first-line imaging modality for evaluating acute ischemic stroke (AIS). Diffusion-weighted MRI (DWI) can reveal early ischemic changes that often remain subtle on NCCT within the treatment window. However, DWI suffers from longer scanning times, metal contraindications, and restricted availability, all of which may delay treatment. To bridge this gap, we propose an explicit transformation generative adversar ial network (ETGAN) that synthesizes valuable DWI from NCCT scans. Our core innovation is an explicit semantic feature transformation that injects learned ischemic priors directly into the synthesis pipeline. Specifically, we in troduce a Feature Transformation Module (FTM), pre-trained via cross-modal contrastive learning, to map NCCT features into an ischemia-aware pseudo-DWI feature space. By incorporating the pre-trained FTM, the genera tor explicitly transforms hierarchical NCCT features into pseudo-DWI features, which are aligned with real DWI through a dedicated feature loss. These prior-informed representations enable ETGAN to more accurately recon struct early ischemic lesions in synthesized DWI. Additionally, an attention-enhanced discriminator focuses on ischemic regions and guides the generator to synthesize finer lesion details. Our proposed method was evalu ated on public and in-house datasets, achieving high fidelity with SSIM scores of 0.946 and 0.905, respectively. Extensive experiments showed that ETGAN outperformed existing methods in both visual quality and quanti tative metrics. Crucially, the synthesized DWI significantly improved neuroradiologists' diagnostic accuracy in identifying AIS from NCCT alone (p < 0.001). Our model offers a promising solution for rapid and accurate AIS assessment when MRI is inaccessible or delayed.

Laser speckle contrast imaging (LSCI) provides label-free, wide-field blood-flow imaging with high spatiotemporal resolution, but its quantitative interpretation remains challenging because the measured speckle contrast is affected by both tissue optical properties and flow dynamics. Here, we present Flow dynamic and Optical property Coupled Laser Speckle Imaging (FOCUS), which integrates LSCI with spatial frequency domain imaging (SFDI) for quantitative blood-flow mapping. FOCUS employs a single laser source, a laser speckle reducer, and a digital micromirror device. This configuration enables switching between high-coherence planar illumination for LSCI and low-coherence structured illumination for SFDI, allowing pixel-wise co-registered measurements at the same wavelength. SFDI is used to recover the absorption coefficient μa and the reduced scattering coefficient μs′, whereas LSCI provides the speckle contrast K. These measurements are incorporated into a correlation–diffusion-based inversion framework to estimate quantitative blood-flow parameters on a pixel-wise basis. To account for different flow regimes, FOCUS considers both Brownian and directed-flow motion models and constructs corresponding dual-dynamics lookup tables. Phantom experiments demonstrate improved repeatability under varying optical properties and flow speeds. In vivo, FOCUS maintains stable perfusion measurements during indocyanine green injection in large vessels. It also sensitively detects flow reduction in the ischemic core of a photothrombotic stroke model. These results demonstrate the feasibility of FOCUS for quantitative laser speckle perfusion imaging with optical-property correction.

Endoscopic laser speckle contrast imaging (eLSCI) has the potential to reduce the risk of anastomotic leakage in minimally invasive colorectal cancer resection surgeries. However, its clinical utility is limited by quantization distortion, low SNR, low sensitivity, and flow imaging bias-all mainly due to low light intensity, detector noise, and uneven laser illumination. While analog gain amplification can enhance quantization accuracy and speckle dynamic range, the inherent trade-off between signal enhancement and noise amplification renders analog gain less favorable for LSCI. Moreover, the imaging community lacks a consensus on the noise model under analog gain. To address these challenges, we established a noise-correction framework under analog gain based on synthesis noise and maximum likelihood estimation (AGNc-SNMLE). AGNc-SNMLE balances the enhancement of signal intensity with the correction of amplified noise introduced by analog gain. Simulation and experiments showed that AGNc-SNMLE significantly improved SNR and sensitivity, optimized the imaging signal-to-background ratio, expanded flow linearity, and reduced flow imaging bias of eLSCI under low-light intensity. AGNc-SNMLE requires no hardware modifications or additional computational overhead, and is fully compatible with low-cost laser and camera, making it a powerful and cost-effective solution for low-light intensity eLSCI.

Laser Speckle Contrast Imaging (LSCI) is a non-invasive, label-free technique that provides high-resolution 2D imaging of blood flow perfusion. In practical applications, however, target tissues typically exhibit 3D morphological surfaces. When LSCI is applied to such tissues, 2D blood perfusion images fail to accurately reflect the 3D spatial distribution of blood flow. Moreover, as tissue curvature increases, spatial compression in the image plane leads to significant overestimation of blood flow speed, which would mislead clinical diagnoses and research. To resolve these issues, in this paper, we proposed a new technology, namely 3D-sLSCI, with aim to correct the influence of tissue curvature on LSCI. Specifically, we developed a 3D-sLSCI measurement system through integrating LSCI with Phase-shifting profilometry (PSP) technology, permitting the simultaneous acquisition of blood flow and surface morphology information. Additionally, we established a theoretical model and correction algorithm to improve blood flow estimation within the curved tissues. The proposed correction algorithm is then incorporated into the 3D-sLSCI system to compensate the measurement errors induced by curvature changes. Experimental results from phantom and in vivo showed that 3D-sLSCI enhances measurement accuracy within the curved tissues and provides reliable 3D surface-rendered blood perfusion information. This work contributes to the improvements of LSCI precision in geometrically complicated, variable environments, expanding its potential applications in biomedical imaging.

Blood flow is essential for maintaining normal physiological functions of the human body. Endoscopic laser speckle contrast imaging (LSCI) can achieve rapid, high-resolution, label-free, and long-term blood flow perfusion velocity monitoring in minimally invasive surgery. However, conventional endoscopic LSCI uses a low-coherence laser illumination scheme, leading to restricted angles of illumination, compromised laser coherence, uneven laser illumination distribution, and low coupling efficiency, all of which degrade the quality of LSCI in the endoscope. In this paper, we propose that conical fiber (CF)-coupled high-coherence laser can be used to achieve large-angle, high-coherence, high-uniformity, and high coupling efficiency laser illumination in the endoscope. Additionally, we establish an effective model for calculating the divergence angle of CFs. Through phantom and animal experiments, we reveal that laser illumination based on CF markedly enhances endoscopic LSCI performance. This technology broadens the imaging field of view, enhances the signal-to-noise ratio, enables more sensitive detection of minute blood flow changes, expands the detectable flow range, and improves signal-to-background ratio of endoscopic LSCI. Our findings suggest that CF-based laser illumination stands as a highly promising advancement in endoscopic LSCI.

Elasticity is a fundamental property of materials, and recent advancements in wave-based elastography have revealed significant potential for various biomedical and engineering applications, including biomedical imaging, nondestructive evaluation, and structural health monitoring. However, the implementation of elastography requires high-precision imaging systems, which limits its broader applicability. The laser profilometer, a conventional and cost-effective device that operates based on laser triangulation measurement, has been widely utilized in industrial applications for assessing surface profiles. However, its application in elastography has not been previously explored. This study represents, to the best of our knowledge, the first attempt to adapt a laser profilometer for measuring the elasticity of soft materials. A simple and noncontact method for measuring elasticity has been established utilizing the laser profilometer to track the propagation of surface waves on soft materials when excited by an airpuff. The results demonstrate that laser profilometer elastography can track the propagation of surface waves with a broad spectrum following a single airpuff excitation. The temporal separation of wave propagation from the reflected waves enables precise calculation of the propagation velocity of surface waves. The surface wave velocities measured by laser profilometer elastography and laser speckle elastography show strong agreement with a correlation coefficient of 0.997. Additionally, the shear elastic modulus of agarose phantoms has been validated by comparing the results obtained from a rotary rheometer. This approach improves the noncontact elastic measurement capabilities of traditional laser profilometers by only utilizing an airpuff system. Therefore, it has the potential to expand a new application of laser profilometers and be widely utilized for elasticity measurement in both biomedical and industrial applications.

BACKGROUND:The optimal management of the inferior mesenteric artery (IMA) in rectal cancer surgery remains controversial owing to its unclear impact on bowel perfusion. This study aimed to objectively evaluate perfusion differences between high tie (HT) and low tie (LT) using laser speckle contrast imaging (LSCI) and determine its value in guiding surgical decisions. METHODS:Patients who underwent laparoscopic anterior rectal resection for rectal or rectosigmoid cancer were prospectively enrolled for either HT or LT. The primary outcome was the maximum perfusion distance (MPD). The secondary outcomes included the Speckle Flow Index (SFI) at the transection site and the frequency of LSCI-guided surgical revisions. RESULTS:After propensity score matching ( n = 30/group), no significant overall difference was found in the median MPD ( P = 0.12) or mean SFI ( P = 0.20) between the HT and LT groups. However, a key finding was the identification of a high-risk HT patient subgroup with critically short MPD, a phenotype that was absent in the LT cohort. Consequently, LSCI guidance prompted surgical revision in 16.7% of the HT patients (vs. 0% in the LT group). Ultimately, this individualized approach resulted in an equally low anastomotic leakage rate (3.3%) in both the cohorts. CONCLUSION:Our results from this pilot study are hypothesis-generating. While the average perfusion did not differ significantly between IMA management techniques, HT posed a unique risk by creating a patient subset with critically compromised perfusion. Real-time LSCI assessment proved effective in identifying these high-risk individuals intraoperatively, prompting timely surgical revisions and thereby reducing the incidence of anastomotic leakage.

AIM:Anastomotic leakage (AL) is a devastating complication following anterior rectal resection. This study aimed to evaluate whether a strategy utilizing real-time Laser Speckle Contrast Imaging (LSCI) for intraoperative perfusion assessment could reduce the incidence of AL. METHODS:We conducted a single-centre study comparing a prospective cohort undergoing LSCI-guided surgery (LSCI group) with a historical cohort (retrospective group). Propensity score matching (PSM) was used to balance baseline characteristics. The LSCI system was used to objectively assess bowel perfusion and guide the selection of the proximal transection site. The primary outcome was the rate of anastomotic leakage. RESULTS:After PSM, 105 patients in the LSCI group were matched with 176 patients in the control group, with all baseline characteristics being comparable. The incidence of AL was significantly lower in the LSCI group compared to the control group (1.9% vs. 8.5%, p < 0.05). Furthermore, the rate of severe postoperative infective complications requiring major intervention (percutaneous drainage or reoperation) was also significantly reduced in the LSCI group (1.9% vs. 9.6%; p = 0.02). In the LSCI group, real-time perfusion assessment led to an intraoperative change of the planned anastomotic site in 6 patients (5.7%), predominantly in low-to-middle rectal cancer cases. CONCLUSION:The use of an LSCI-guided strategy for real-time intraoperative perfusion assessment may reduce the rates of both AL and severe postoperative infective complications undergoing ARR surgery. This objective, quantitative technology is a valuable tool for optimizing surgical decision-making and improving patient outcomes.

Background The relationship between chronic kidney disease (CKD) and cerebral small vessel disease has been inconsistently reported. In particular, there is a lack of research focusing on patients with acute ischemic stroke, a key area that could provide important insights into the brain–kidney connection. Methods AND RESULTS We established a large‐sample size, multicenter prospective cohort study (SMART [Cerebral Small Vascular Disease Registry Multicenter Clinical Trial]) across 13 subcenters in central China. All participants underwent long‐term, continuous renal function monitoring. CKD was assessed using the Kidney Disease Improving Global Outcomes criteria, defined as abnormal kidney function lasting for at least 3 consecutive months. Magnetic resonance imaging, including T2‐weighted and susceptibility‐weighted imaging, was used to detect markers of cerebral small vessel disease such as white matter hyperintensities, cerebral microbleeds, lacunar infarctions, and enlarged perivascular spaces. Multinomial, binomial, and ordinal logistic regression models were employed, adjusting for demographic, vascular, and stroke‐related factors. Among the 3909 patients with acute ischemic stroke (mean age 62 years, 35.3% female), 307 (7.9%) were diagnosed with CKD. Higher CKD risk grades were correlated with an increased burden of cerebral small vessel disease. After adjusting confounding factors, white matter hyperintensities (odds ratio [OR], 1.841 [95% CI, 1.413–2.400], P<0.001), lacunar infarctions (OR, 3.455 [95% CI, 2.314–5.158], P<0.001), and cerebral microbleeds (OR, 2.514 [95% CI, 1.976–3.199], P=0.005) were significantly more frequent in patients with CKD. Additionally, patients with CKD exhibited higher rates of cardiac embolism (OR, 1.405 [95% CI, 1.067–1.851], P=0.016) compared with other stroke causes. Conclusions Stroke clinicians should recognize CKD as a potentially independent and modifiable risk factor for cerebral small vessel disease.

Diffuse speckle contrast analysis (DSCA) is a valuable technique for monitoring blood flow speed, but its accuracy and sensitivity are often compromised by system noise and non-ergodicity, particularly in deep tissue measurements. Noise and static scattering tissues lead to overestimated speckle contrast and consequent underestimation of blood flow speed, especially in regions of rapid flow. To address these challenges, we developed a noise correction method integrated with a non-ergodicity calibrated model to enhance the sensitivity and accuracy of DSCA. A guide for selecting an appropriate core diameter and numerical aperture was provided for multi-mode fiber DSCA. Validation through phantom and in vivo experiments demonstrated that the non-ergodicity and noise-corrected DSCA improved the sensitivity of blood flow speed measurements in deep tissues.

The role of microglia in blood-brain barrier (BBB) leakage and neovascularization after ischemic stroke remains unclear. Here, a post-stroke perivascular niche of microglia characterized by low expression of M2 markers and elevated glycolysis, oxidative phosphorylation (OXPHOS), and phagocytic activity is identified, which is termed stroke-activated vascular-associated microglia (stroke-VAM). It is found that Fkbp5 acts as a central regulator driving BBB disruption and impaired neovascularization through stroke-VAM. Single-nucleus RNA sequencing (snRNA-seq) analysis of Cx3cr1Cre Fkbp5flox/flox (Fkbp5 cKO) mice in the ipsilateral hemisphere reveals enhanced interactions between stroke-VAM and endothelial cells, influencing signaling pathways that maintain BBB integrity and promote neovascularization. After ischemic injury, microglia in Fkbp5 cKO mice exhibits higher M2 marker expression and reduces glycolysis, OXPHOS, and phagocytosis, resulting in decreased BBB leakage and enhanced angiogenesis. Mechanistically, unbiased snRNA-seq analysis shows that the Hippo signaling pathway is altered in Fkbp5 cKO stroke-VAM. Fkbp5 inhibits Yap1 phosphorylation, facilitating its nuclear translocation. These findings provide new insights into how the perivascular microglial niche contributes to both the degradation and regeneration of cerebral vasculature, offering potential therapeutic avenues for acute ischemic stroke.

Accurate quantification of blood perfusion is critical for understanding vascular pathophysiology. Although optical coherence tomography (OCT) can offer an insight into the blood perfusion by measuring three-dimensional blood flow velocity, existing OCT velocimetry techniques based on dynamic light scattering face inaccurate measurements of blood perfusion due to limited measurable dynamic range with a finite sampling frequency dependent on a specific swept-source laser or spectrometer. High-speed saturation or low-speed overestimation makes it challenging to cover the entire parabolic velocity distribution like Poiseuille flow in a blood vessel, in which the non-Newtonian shear-thinning behavior of blood fundamentally governs velocity gradients. Here, we proposed hybrid decorrelation (HD) OCT to enable blood perfusion quantification. HD-OCT interpreted the blood flow velocity at slow regimes based on the temporal intensity correlation function, while with its temporal integration in fast-flow regions. It achieved a measurement of blood flow velocity from 0.5 to 400 mm/s. HD-OCT improved the accuracy of blood perfusion measurement based on a precise cross-sectional velocity distribution.

In clinical research, high-resolution (HR) MRI images can help reduce misdiagnosis rates among physicians. However, prolonged acquisition times may negatively impact patient comfort. Typically, clinicians compromise between image quality and acquisition time by acquiring thick-slice images to shorten scanning duration, supplemented by other faster sequences. To ensure diagnostic accuracy, researchers employ super- resolution (SR) techniques to restore image resolution. Although numerous advanced SR models have been proposed, most struggle with generalization bottlenecks at high up scaling factors, leading to artificial structures in reconstructed images. To address this issue, we propose Brain-CDM, a specialized cascaded diffusion model designed for high-ratio inter-slice super-resolution of thick-slice MRI. The core components of our model are diffusion models, renowned for their superior ability to model complex distributions. The architecture consists of two diffusion models connected in a cascaded manner, employing a multi-stage progressive optimization strategy to decompose the high-ratio SR task into multiple low-ratio subtasks, thereby overcoming the information bottleneck of single-stage models. Additionally, we leverage simultaneously acquired multi-contrast sequences to provide complementary information for the model. Extensive evaluations across multiple datasets demonstrate the superior super- resolution reconstruction performance of our method over existing techniques.

With the rapid development of various whole-brain ex vivo imaging technologies, there is a growing need to develop cross-modal 3D image registration methods to integrate multimodal imaging datasets. While comprehensive cross-modality registration tools such as D-LMBmap and mBrainAligner have been successfully implemented for mouse brains, macaque whole-brain registration presents unique challenges. These include more pronounced non-uniform deformations in larger ex vivo specimens, greater modality-specific contrast differences relative to standard space, and increased inter-individual variability. To address these challenges, we developed Macaca-Star, which incorporates deep learning models and self-individual MRI to tackle cross-modal and ex vivo sample deformation challenges in macaque whole-brain registration. Macaca-Star provides fully automated alignment of fMOST and 2D fluorescent slice images to the NMT MRI standard space, allowing for comprehensive integration of anterograde axonal projections and retrograde-traced neuronal soma profiles. ### Competing Interest Statement The authors have declared no competing interest. the National Key R&D Program of China, 2022YEF0203200 and 2022YFA1603604 the STI2030-Major Projects, 2021ZD0200104 the National Natural Science Foundation of China, 82260227, 61890950, and 62401185 Hainan University Research Start-up Fund, KYQD(ZR)20072 and KYQD(ZR)22074 the PhD Scientific Research and Innovation Foundation of The Education Department of Hainan Province Joint Project of Sanya Yazhou Bay Science and Technology City, HSPHDSRF-2024-08-010

The hippocampus is a critical brain region. Transcriptome data provides valuable insights into the structure and function of the hippocampus at the gene level. However, transcriptome data is often incomplete. To address this issue, we use the convolutional neural network model to repair the missing voxels in the hippocampus region, based on Allen institute coronal slices in situ hybridization (ISH) dataset. Moreover, we analyze the gene expression correlation between coronal and sagittal dataset in the hippocampus region. The results demonstrated that the trend of gene expression correlation between the coronal and sagittal datasets remained consistent following the repair of missing data in the coronal ISH dataset. In the last, we use repaired ISH dataset to identify novel genes specific to hippocampal subregions. Our findings demonstrate the accuracy and effectiveness of using deep learning method to repair ISH missing data. After being repaired, ISH has the potential to improve our comprehension of the hippocampus's structure and function.

Neurovascular coupling (NVC) is crucial for maintaining brain function and holds significant implications for diagnosing neurological disorders. However, the neuron type and spatial specificity in NVC remain poorly understood. In this study, we investigated the spatiotemporal characteristics of local cerebral blood flow (CBF) driven by excitatory (VGLUT2) and inhibitory (VGAT) neurons in the mouse sensorimotor cortex. By integrating optogenetics, wavefront modulation technology, and laser speckle contrast imaging (LSCI), we achieved precise, spatially targeted photoactivation of type-specific neurons and real-time CBF monitoring. We observed three distinct CBF response patterns across different locations: unimodal, bimodal, and biphasic. While unimodal and bimodal patterns were observed in different locations for both neuron types, the biphasic pattern was exclusive to inhibitory neurons. Our results reveal the spatiotemporal complexity of NVC across different neuron types and demonstrate our method's ability to analyze this complexity in detail.

Increasing evidence has revealed the large-scale nonstationary synchronizations as traveling waves in spontaneous neural activity. However, the interplay of various cell types in fine-tuning these spatiotemporal patters remains unclear. Here, we performed comprehensive exploration of spatiotemporal synchronizing structures across different cell types, states (awake, anesthesia, motion) and developmental axis in male mice. We found traveling waves in glutamatergic neurons exhibited greater variety than those in GABAergic neurons. Moreover, the synchronizing structures of GABAergic neurons converged toward those of glutamatergic neurons during development, but the evolution of waves exhibited varying timelines for different sub-type interneurons. Functional connectivity arises from both standing and traveling waves, and negative connections can be elucidated by the spatial propagation of waves. In addition, some traveling waves were correlated with the spatial distribution of gene expression. Our findings offer further insights into the neural underpinnings of traveling waves, functional connectivity, and resting-state networks, with cell-type specificity and developmental perspectives. Neural mechanisms underlying brain-wide synchronization are not fully understood. Here authors show that traveling waves are prevalent in both excitatory and inhibitory neural populations, more pronounced in glutamatergic neurons, vary across developmental stages, and are associated with functional connections and gene expression.

Laser speckle contrast imaging (LSCI) has gained significant attention in the biomedical field for its ability to map the spatio-temporal dynamics of blood perfusion in vivo. However, LSCI faces difficulties in accurately resolving blood perfusion in microvessels. Although the transmissive detecting geometry can improve the spatial resolution of tissue imaging, ballistic photons directly transmitting forward through tissue without scattering will cause misestimating in the flow speed by LSCI because of the lack of a quantitative theoretical model of transmissvie LSCI. Here, we develop a model of temporal LSCI which accounts for the effect of nonscattered light on estimating decorrelation time. Based on this model, we further propose a dual-exposure temporal laser speckle imaging method (dEtLSCI) to correct the overestimation of background speed when performing traditional transmissive LSCI, and reconstruct microvascular angiography using the scattered component extracted from total transmitted light. Experimental results demonstrated that our new method opens an opportunity for LSCI to simultaneously resolve the blood vessels morphology and blood flow speed at microvascular level in various contexts, ranging from the drug-induced vascular response to angiogenesis and the blood perfusion monitoring during tumor growth.