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Significance: Oral cancer surgery requires accurate margin delineation to balance complete resection with post-operative functionality. Current in vivo fluorescence imaging systems provide two-dimensional margin assessment yet fail to quantify tumor depth prior to resection. Harnessing structured light in combination with deep learning (DL) may provide near real-time three-dimensional margin detection. Aim: A DL-enabled fluorescence spatial frequency domain imaging (SFDI) system trained with in silico tumor models was developed to quantify the depth of oral tumors. Approach: A convolutional neural network was designed to produce tumor depth and concentration maps from SFDI images. Three in silico representations of oral cancer lesions were developed to train the DL architecture: cylinders, spherical harmonics, and composite spherical harmonics (CSHs). Each model was validated with in silico SFDI images of patient-derived tongue tumors, and the CSH model was further validated with optical phantoms. Results: The performance of the CSH model was superior when presented with patient-derived tumors ( P -value < 0.05 ). The CSH model could predict depth and concentration within 0.4 mm and 0.4 µ g / mL , respectively, for in silico tumors with depths less than 10 mm. Conclusions: A DL-enabled SFDI system trained with in silico CSH demonstrates promise in defining the deep margins of oral tumors.

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Significance: Near-infrared optical imaging methods have shown promise for monitoring response to neoadjuvant chemotherapy (NAC) for breast cancer, with endogenous contrast coming from oxy- and deoxyhemoglobin. Spatial frequency domain imaging (SFDI) could be used to detect this contrast in a low-cost and portable format, but it has limited imaging depth. It is possible that local tissue compression could be used to reduce the effective tumor depth. Aim: To evaluate the potential of SFDI for therapy response prediction, we aim to predict how changes to tumor size, stiffness, and hemoglobin concentration would be reflected in contrast measured by SFDI under tissue compression. Approach: Finite element analysis of compression on an inclusion-containing soft material is combined with Monte Carlo simulation to predict the measured optical contrast. Results: When the effect of compression on blood volume is not considered, contrast gain from compression increases with the size and stiffness of the inclusion and decreases with the inclusion depth. With a model of reduction of blood volume from compression, compression reduces imaging contrast, an effect that is greater for larger inclusions and stiffer inclusions at shallower depths. Conclusions: This computational modeling study represents a first step toward tracking tumor changes induced by NAC using SFDI and local compression.

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OBJECTIVE: This study aimed to examine the spatiotemporal coherence of capillary lumen fluctuations in relation to spatial variations in the pericyte lining in the cortex of anesthetized mice. METHODS: Two-photon microscopic angiography data (previously published) were reanalyzed, and spatial variations in capillary diameter fluctuations at rest and in capillary lining with vascular mural cells were measured along capillary centerlines. RESULTS: Relatively large diameters of the capillaries (5.5 µm) coincided with a dense pericyte lining, while small capillaries (4.3 µm) had a sparse pericyte lining. Temporal variations had a frequency of about 0.1 Hz with an amplitude of 0.5 µm, which were negatively correlated with pericyte lining density. Spatial frequency analysis further revealed a common pattern of spatial variations in capillary diameter and pericyte lining, but temporal variations differed. The temporal variations in capillary lumens were locally distinct from those in neighboring locations, suggesting intrinsic fluctuations independent of the pericyte lining. CONCLUSIONS: Capillary lumens in the brain exhibit slow microfluctuations that are independent of pericyte lining. These microfluctuations could affect the distribution of flowing blood cells and may be important for homogenizing their distribution in capillary networks.

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The benefits of femtosecond laser-assisted in situ keratomileusis (FS-LASIK) for correcting vision, particularly in terms of spherical equivalent (SE) and visual acuity (VA), have gained broad recognition. Nevertheless, it has remained uncertain whether FS-LASIK has a positive impact on contrast sensitivity (CS). In this study, we measured CS on seven participants by the quick contrast sensitivity function (qCSF) and compared CS before and after the surgery at two time points (1 day and 7 days after) by the repeated measures analysis of variance (ANOVA). Then, we clarified the underlying mechanisms using the perceptual template model (PTM). Furthermore, we investigated the relationship among SE, VA, and CS employing the Pearson correlation test. We found that (1) CS exhibited significant improvements on postoperative day 1, with further enhancements observed up to postoperative day 7, (2) CS improvements were dependent on spatial frequency (SF) and external noise, (3) CS improvements were attributed to the reduction of internal noise and the enhancement of the perceptual template, (4) VA and SE demonstrated significant improvement post-surgery, and (5) no significant correlations were observed among SE, VA, and CS, possibly due to limitations in sample size and lighting conditions. These findings contribute to our comprehension of FS-LASIK and provide a great indicator for assessing the outcomes of visual surgery.

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Significance: Spatial frequency domain imaging (SFDI) applies patterned near-infrared illumination to quantify the optical properties of subsurface tissue. The periocular region is unique due to its complex ocular adnexal anatomy. Although SFDI has been successfully applied to relatively flat in vivo tissues, regions that have significant height variations and curvature may result in optical property inaccuracies. Aim: We characterize the geometric impact of the periocular region on SFDI imaging reliability. Approach: SFDI was employed to measure the reduced scattering coefficient ( µ s ' ) and absorption coefficient ( µ a ) of the periocular region in a cast facial tissue-simulating phantom by capturing images along regions of interest (ROIs): inferior temporal quadrant (ITQ), inferior nasal quadrant (INQ), superior temporal quadrant (STQ), central eyelid margin (CEM), rostral lateral nasal bridge (RLNB), and forehead (FH). The phantom was placed on a chin rest and imaged nine times from an "en face" or "side profile" position, and the flat back of the phantom was measured 15 times. Results: The measured µ a and µ s ' of a cast facial phantom are accurate when comparing the ITQ, INQ, STQ, and FH to its flat posterior surface. Paired t tests of ITQ, INQ, STQ, and FH µ a and µ s ' concluded that there is not enough evidence to suggest that imaging orientation impacted the measurement accuracy. Regions of extreme topographical variation, i.e., CEM and RLNB, did exhibit differences in measured optical properties. Conclusions: We are the first to evaluate the geometric implications of wide-field imaging along the periocular region using a solid tissue-simulating facial phantom. Results suggest that the ITQ, INQ, STQ, and FH of a generalized face have minimal impact on the SFDI measurement accuracy. Areas with heightened topographic variation exhibit measurement variability. Device and facial positioning do not appear to bias measurements. These findings confirm the need to carefully select ROIs when measuring optical properties along the periocular region.

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To address the problem of the high hardware requirements and insufficient data storage capacity in current ultrasonic imaging testing, a novel approach is developed using a programmable device, which combines spatial-frequency parallel subsampling with the distributed compressive sensing simultaneous orthogonal matching pursuit (DCS-SOMP) algorithm to achieve fast and high-quality ultrasonic imaging inspection with a small amount of subsampled data. The spatial sparse measurement method was employed to achieve spatial subsampling and minimize the count of signals. Additionally, frequency subsampling was utilized to significantly reduce the data volume of time-domain signals while ensuring signal quality by truncating the primary testing frequency components. The subsampled data was then reconstructed using distributed compressive sensing (DCS) for multi-channel data reconstruction. The experiment of ultrasonic scanning imaging was conducted on a carbon steel specimen containing six transverse through-holes with a diameter of Ф1.5 mm at different depths. The ultrasonic signals were acquired using the spatial-frequency parallel subsampling method, and subsequently reconstructed using the DCS-SOMP algorithm. The results show that the proposed method achieves comparable image quality to that obtained with complete data, using only 1/8 of the complete data, while accurately locating and quantifying defects.

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Soybean seeds are susceptible to damage from the Riptortus pedestris, which is a significant factor affecting the quality of soybean seeds. Currently, manual screening methods for soybean seeds are limited to visual inspection, making it difficult to identify seeds that are phenotypically defect-free but have been punctured by stink bugs on the sub-surface. To facilitate the convenient and efficient identification of healthy soybean seeds, this paper proposes a soybean seed pest detection method based on spatial frequency domain imaging combined with RL-SVM. Firstly, soybean optical data is obtained using single integration sphere technique, and the vigor index of soybean seeds is obtained through germination experiments. Then, based on the above two data items using feature extraction algorithms (the successive projections algorithm and the competitive adaptive reweighted sampling algorithm), the characteristic wavelengths of soybeans are identified. Subsequently, the spatial frequency domain imaging technique is used to obtain the sub-surface images of soybean seeds in a forward manner, and the optical coefficients such as the reduced scattering coefficient µ ' s and absorption coefficient µ a of soybean seeds are inverted. Finally, RL-MLR, RL-GRNN, and RL-SVM prediction models are established based on the ratio of the area of insect-damaged sub-surface to the entire seed, soybean varieties, and µ a at three wavelengths (502 nm, 813 nm, and 712 nm) for predicting and identifying soybean the stinging and sucking pest damage levels of soybean seeds. The experimental results show that the spatial frequency domain imaging technique yields small errors in the optical coefficients of soybean seeds, with errors of less than 15% for µ a and less than 10% for µ ' s . After parameter adjustment through reinforcement learning, the Macro-Recall metrics of each model have improved by 10%-15%, and the RL-SVM model achieves a high Macro-Recall value of 0.9635 for classifying the pest damage levels of soybean seeds.

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Endoscopes are crucial for assisting in surgery and disease diagnosis, including the early detection of cancer. The effective use of endoscopes relies on their optical performance, which can be characterized with a series of metrics such as resolution, vital for revealing anatomical details. The modulation transfer function (MTF) is a key metric for evaluating endoscope resolution. However, the 2020 version of the ISO 8600-5 standard, while introducing an endoscope MTF measurement method, lacks empirical validation and excludes opto-electronic video endoscopes, the largest family of endoscopes. Measuring the MTF of video endoscopes requires tailored standards that address their unique characteristics. This paper aims to expand the scope of ISO 8600-5:2020 to include video endoscopes, by optimizing the MTF test method and addressing parameters affecting measurement accuracy. We studied the effects of intensity and uniformity of image luminance, chart modulation compensation, linearity of image digital values, auto gain control, image enhancement, image compression and the region of interest dimensions on images of slanted-edge test charts, and thus the MTF based on these images. By analyzing these effects, we provided recommendations for setting and controlling these factors to obtain accurate MTF curves. Our goal is to enhance the standard's relevance and effectiveness for measuring the MTF of a broader range of endoscopic devices, with potential applications in the MTF measurement of other digital imaging devices.

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Visual processing differs between the foveal and peripheral visual field. These differences can lead to different appearances of objects in the periphery and the fovea, posing a challenge to perception across saccades. Differences in the appearance of visual features between the peripheral and foveal visual field may bias change discrimination across saccades. Previously it has been reported that spatial frequency (SF) appears higher in the periphery compared to the fovea (Davis et al., 1987). In this study, we investigated the visual appearance of SF before and after a saccade and the discrimination of SF changes during saccades. In addition, we tested the contributions of pre- and postsaccadic information to change discrimination performance. In the first experiment, we found no differences in the appearance of SF before and after a saccade. However, participants showed a clear bias to report SF increases. Interestingly, a 200-ms postsaccadic blank improved the precision of the responses but did not affect the bias. In the second experiment, participants showed lower thresholds for SF increases than for decreases, suggesting that the bias in the first experiment was not just a response bias. Finally, we asked participants to discriminate the SF of stimuli presented before a saccade. Thresholds in the presaccadic discrimination task were lower than in the change discrimination task, suggesting that transsaccadic change discrimination is not merely limited by presaccadic discrimination in the periphery. The change direction bias might stem from more effective masking or overwriting of the presaccadic stimulus by the postsaccadic low SF stimulus.

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Despite their young age, zebrafish larvae have a well-developed visual system and can distinguish between different visual stimuli. First, we investigated if the first visual surroundings the larvae experience during the first days after hatching shape their habitat preference. Indeed, these animals seem to "imprint" on the first surroundings they see and select visual stimuli accordingly at 7 days post fertilization (dpf). In particular, if zebrafish larvae experience a bar background just after hatching, they later on prefer bars over white stimuli, and vice versa. We then used this acquired preference for bars to investigate innate numerical abilities. We wanted to specifically test if the zebrafish larvae show real numerical abilities or if they rely on a lower-level mechanism-i.e. spatial frequency-to discriminate between two different numerosities. When we matched the spatial frequency in stimuli with different numbers of bars, the larvae reliably selected the higher numerosity. A previous study has ruled out that 7 dpf zebrafish larvae use convex hull, cumulative surface area and density to choose between two numerosities. Therefore, our results indicate that zebrafish larvae rely on real numerical abilities rather than other cues, including spatial frequency, when spontaneously comparing two sets with different numbers of bars.

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Patients with post-traumatic stress disorder (PTSD) exhibit sex differences in symptomology, with women more likely to report higher rates of intrusive and avoidance symptoms than men, underscoring the need for sex-informed approaches to research and treatment. Our study delved into the sex-specific aspects of stress-induced visual impairments using the single prolonged stress (SPS) model, a partially validated rodent model for PTSD. Male SPS mice exhibit heightened optimal spatial frequency (SF) of primary visual cortex (V1) neurons, while female counterparts exhibit decreased optimal temporal frequency (TF) of V1 neurons. This phenomenon persisted until the 29th day after SPS modeling, and it may be the physiological basis for the observed increase in visual acuity in male SPS mice in visual water task. Furthermore, our study found that corticotropin-releasing factor receptor 1 regulated optimal TF and optimal SF of V1 in mice, but did not exhibit sex differences. These findings indicated that severe stress induces sex-specific effects on visual function.

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Low-light conditions pose significant challenges to vision tasks, such as salient object detection (SOD), due to insufficient photons. Light-insensitive RGB-T SOD models mitigate the above problems to some extent, but they are limited in performance as they only focus on spatial feature fusion while ignoring the frequency discrepancy. To this end, we propose an RGB-T SOD model by mining spatial-frequency cues, called SFMNet, for low-light scenes. Our SFMNet consists of spatial-frequency feature exploration (SFFE) modules and spatial-frequency feature interaction (SFFI) modules. To be specific, the SFFE module aims to separate spatial-frequency features and adaptively extract high and low-frequency features. Moreover, the SFFI module integrates cross-modality and cross-domain information to capture effective feature representations. By deploying both modules in a top-down pathway, our method generates high-quality saliency predictions. Furthermore, we construct the first low-light RGB-T SOD dataset as a benchmark for evaluating performance. Extensive experiments demonstrate that our SFMNet can achieve higher accuracy than the existing models for low-light scenes.

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BACKGROUND: Schizophrenia (SZ), a psychiatric disorder for which there is no precise diagnosis, has had a serious impact on the quality of human life and social activities for many years. Therefore, an advanced approach for accurate treatment is required. NEW METHOD: In this study, we provide a classification approach for SZ patients based on a spatial-temporal residual graph convolutional neural network (STRGCN). The model primarily collects spatial frequency features and temporal frequency features by spatial graph convolution and single-channel temporal convolution, respectively, and blends them both for the classification learning, in contrast to traditional approaches that only evaluate temporal frequency information in EEG and disregard spatial frequency features across brain regions. RESULTS: We conducted extensive experiments on the publicly available dataset Zenodo and our own collected dataset. The classification accuracy of the two datasets on our proposed method reached 96.32% and 85.44%, respectively. In the experiment, the dataset using delta has the best classification performance in the sub-bands. COMPARISON WITH EXISTING METHODS: Other methods mainly rely on deep learning models dominated by convolutional neural networks and long and short time memory networks, lacking exploration of the functional connections between channels. In contrast, the present method can treat the EEG signal as a graph and integrate and analyze the temporal frequency and spatial frequency features in the EEG signal. CONCLUSION: We provide an approach to not only performs better than other classic machine learning and deep learning algorithms on the dataset we used in diagnosing schizophrenia, but also understand the effects of schizophrenia on brain network features.

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Human and artificial features that coexist in certain types of human-like robots create a discrepancy in perceived humanness and evoke uncanny feelings in human observers. However, whether this perceptual mismatch in humanness occurs for all faces, and whether it is related to the uncanny feelings toward them, is unknown. We investigated this by examining perceived humanness for a variety of natural images of robot and human faces with different spatial frequency (SF) information: that is, faces with only low SF, middle SF, and high SF information, and intact (spatially unfiltered) faces. Uncanny feelings elicited by these faces were also measured. The results showed perceptual mismatches that LSF, MSF, and HSF faces were perceived as more human than intact faces. This was particularly true for intact robot faces that looked slightly human, which tended to evoke strong uncanny feelings. Importantly, the mismatch in perceived humanness between the intact and spatially filtered faces was positively correlated with uncanny feelings toward intact faces. Given that the human visual system performs SF analysis when processing faces, the perceptual mismatches observed in this study likely occur in real life for all faces, and as such might be a ubiquitous source of uncanny feelings in real-life situations.

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Elastic spherical polishing tools effectively conform to the polishing surface and exhibit high efficiency in the removal of materials, so they are extensively used in the sub-aperture polishing stages of optical components. However, their processing is often accompanied by significant mid-spatial frequency (MSF) errors, which critically degrade the performance of optical systems. To suppress the MSF errors generated during polishing with spherical tools, this study investigates the influence factor of MSF errors during the polishing process through an analysis of the convolution effect in material removal. A material removal profile model is established, and a uniform removal simulation is conducted to assess the influence of different shape material removal profiles on MSF errors. Simulation and experimental results show that a Gaussian-like shape material removal profile is more effective in suppressing the MSF errors during polishing compared to the "W" and trapezoidal shape material removal profiles. In addition, based on the characteristics of the RMS decreasing in a serrated trend with the decrease in path spacing, a path spacing optimization method considering the polishing efficiency is proposed to improve the polishing efficiency while controlling the MSF errors, and the effectiveness of the path spacing optimization method is verified by comparing the MSF error at the maximum theoretical path spacing and the path spacing that is less than this. Finally, the path spacing optimization method is used to polish single-crystal silicon to further illustrate its practicality.

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Background: This study compares the reproducibility of freehand (FH) vs. foam cast (FC) scans and investigates the intrarater reliability of the ultrasound FC muscle architecture and tissue organization measurements of the gastrocnemius medialis (GM) and vastus lateralis (VL) muscles with fixed and repositioning FC scans. Methods: Thirteen young adults (22 ± 3 years) underwent repeated sagittal B-mode ultrasound measurements of GM and VL. FH, FC, and repositioned FC scans were conducted. Muscle architecture measurements included muscle thickness (MT), pennation angle (PA), and fascicle length (FL). Spatial frequency analysis assessed muscle tissue organization. Results: MT decreased from 2.1 to 1.8 cm in GM and from 2.4 to 2.2 cm in VL with the FC compared with the FH. Reproducibility between the FH and the FC showed poor to good intraclass correlation coefficients (ICCs) for MT (0.46-0.77) and PA (0.09-0.86) as well as poor to moderate ICCs for FL (0.41), with very low to moderate test-retest variability (TRV) (4%-18%). Tissue organization indicated low to good ICCs (0.21-0.80) with low to moderate TRV (4%-19.5%). The re-scanning results of fixed FC indicated excellent ICCs for MT (0.95-0.996), good for PA (0.77-0.90), and moderate for FL (0.73-0.76), with low TRV (5%-10%) for both muscles. Tissue organization displayed moderate to good ICCs (0.61-0.87) with very low to low TRV (4%-9%). For repositioned FC scans in GM and VL, MT showed good to excellent ICCs (0.86-0.98) with very low to low TRV (2%-8%). PA and FL demonstrated moderate to good ICCs (0.57-0.75), with very low to moderate TRV (2%-13%). Tissue organization revealed ICCs ranging from poor to good (0.13-0.87) for both muscles, with low to moderate TRV (5%-18%). Conclusion: The FC systematically reduced MT by 2-3 mm. Furthermore, reproducibility revealed low ICCs and high data variability for several muscle architecture and tissue organization parameters. Thus, switching methods within a single study is not recommended. Nevertheless, FC ultrasound scans demonstrated excellent intrarater reliability for assessing MT. In the case of fixed FC scans particularly, moderate to excellent ICCs were observed for all muscle architecture and tissue organization parameters, accompanied by very low to low variability. Therefore, FC scans are recommended for investigating acute effects on muscle architecture and tissue organization when the FC remains on the leg throughout the period of measurements.

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Primate visual cortex exhibits key organizational principles: cortical magnification, eccentricity-dependent receptive field size and spatial frequency tuning as well as radial bias. We provide compelling evidence that these principles arise from the interplay of the non-uniform distribution of retinal ganglion cells, and a quasi-uniform convergence rate from the retina to the cortex. We show that convolutional neural networks outfitted with a retinal sampling layer, which resamples images according to retinal ganglion cell density, develop these organizational principles. Surprisingly, our results indicate that radial bias is spatial-frequency dependent and only manifests for high spatial frequencies. For low spatial frequencies, the bias shifts towards orthogonal orientations. These findings introduce a novel hypothesis about the origin of radial bias. Quasi-uniform convergence limits the range of spatial frequencies (in retinal space) that can be resolved, while retinal sampling determines the spatial frequency content throughout the retina.

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Accurately identifying the Kirsten rat sarcoma virus (KRAS) gene mutation status in colorectal cancer (CRC) patients can assist doctors in deciding whether to use specific targeted drugs for treatment. Although deep learning methods are popular, they are often affected by redundant features from non-lesion areas. Moreover, existing methods commonly extract spatial features from imaging data, which neglect important frequency domain features and may degrade the performance of KRAS gene mutation status identification. To address this deficiency, we propose a segmentation-guided Transformer U-Net (SG-Transunet) model for KRAS gene mutation status identification in CRC. Integrating the strength of convolutional neural networks (CNNs) and Transformers, SG-Transunet offers a unique approach for both lesion segmentation and KRAS mutation status identification. Specifically, for precise lesion localization, we employ an encoder-decoder to obtain segmentation results and guide the KRAS gene mutation status identification task. Subsequently, a frequency domain supplement block is designed to capture frequency domain features, integrating it with high-level spatial features extracted in the encoding path to derive advanced spatial-frequency domain features. Furthermore, we introduce a pre-trained Xception block to mitigate the risk of overfitting associated with small-scale datasets. Following this, an aggregate attention module is devised to consolidate spatial-frequency domain features with global information extracted by the Transformer at shallow and deep levels, thereby enhancing feature discriminability. Finally, we propose a mutual-constrained loss function that simultaneously constrains the segmentation mask acquisition and gene status identification process. Experimental results demonstrate the superior performance of SG-Transunet over state-of-the-art methods in discriminating KRAS gene mutation status.

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Significance: Current methods for wound healing assessment rely on visual inspection, which gives qualitative information. Optical methods allow for quantitative non-invasive measurements of optical properties relevant to wound healing. Aim: Spatial frequency domain imaging (SFDI) measures the absorption and reduced scattering coefficients of tissue. Typically, SFDI assumes homogeneous tissue; however, layered structures are present in skin. We evaluate a multi-frequency approach to process SFDI data that estimates depth-specific scattering over differing penetration depths. Approach: Multi-layer phantoms were manufactured to mimic wound healing scattering contrast in depth. An SFDI device imaged these phantoms and data were processed according to our multi-frequency approach. The depth sensitive data were then compared with a two-layer scattering model based on light fluence. Results: The measured scattering from the phantoms changed with spatial frequency as our two-layer model predicted. The performance of two δ-P1 models solutions for SFDI was consistently better than the standard diffusion approximation. Conclusions: We presented an approach to process SFDI data that returns depth-resolved scattering contrast. This method allows for the implementation of layered optical models that more accurately represent physiologic parameters in thin tissue structures as in wound healing.

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Significance: The conventional optical properties (OPs) reconstruction in spatial frequency domain (SFD) imaging, like the lookup table (LUT) method, causes OPs aliasing and yields only average OPs without depth resolution. Integrating SFD imaging with time-resolved (TR) measurements enhances space-TR information, enabling improved reconstruction of absorption (µa) and reduced scattering (µs') coefficients at various depths. Aim: To achieve the stratified reconstruction of OPs and the separation between µa and µs', using deep learning workflow based on the temporal and spatial information provided by time-domain SFD imaging technique, while enhancing the reconstruction accuracy. Approach: Two data processing methods are employed for the OPs reconstruction with TR-SFD imaging, one is full TR data, and the other is the featured data extracted from the full TR data (E, continuous-wave component, ⟨t⟩, mean time of flight). We compared their performance using a series of simulation and phantom validations. Results: Compared to the LUT approach, utilizing full TR, E and ⟨t⟩ datasets yield high-resolution OPs reconstruction results. Among the three datasets employed, full TR demonstrates the optimal accuracy. Conclusions: Utilizing the data obtained from SFD and TR measurement techniques allows for achieving high-resolution separation reconstruction of µa and µs' at different depths within 5 mm.

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