Dual modal compressive photocurrent and optical imaging through a multimode fibre

Dual modal compressive photocurrent and optical imaging through a multimode fibre | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Dual modal compressive photocurrent and optical imaging through a multimode fibre Lei Su, Yufei Wang, Hangfeng Zhang, Sanjukta Sarkar, Wen Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5819817/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Multimode fibres (MMFs) have gained attention for their potential in high-resolution, minimally invasive imaging applications due to their small diameter and high-density signal transmission. However, challenges such as mode interference, environmental sensitivity, and the need for frequent recalibration have limited their practical imaging applications. Optical imaging using MMFs usually relies on wavefront shaping and raster-scanning techniques, which require extended calibration and imaging time along with high computational resources. Here we present a dual-modal MMF imaging system based on compressive sensing, enabling both optical imaging and photocurrent mapping. By projecting random speckle patterns onto photovoltaic surfaces, photocurrent mapping is achieved without the need for raster scanning. Experimental results demonstrate the system’s capability to achieve micron-level spatial characterization, with spatial resolution determined by the fibre’s numerical aperture and operating wavelength. Calibration and imaging processes are completed within 2 seconds, with a compression ratio 28 times below the Nyquist limit. This dual-modal imaging approach paves the way for fibre-based endoscopes capable of simultaneous optical and electrical characterizations, offering new opportunities in biomedical imaging and material science. Physical sciences/Optics and photonics/Optical techniques/Imaging and sensing Physical sciences/Engineering/Biomedical engineering Physical sciences/Optics and photonics/Applied optics/Fibre optics and optical communications Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction M ultimode fibres (MMF), owing to their distinct physical structure, including a larger fibre core and multiple fibre modes, have emerged as promising tools in endoscopic technology 1,2 . Their capacity for high-density image transmission enables minimally invasive optical access deep within tissues 2–10 . Compared to traditional imaging endoscopes, MMFs have a smaller diameter, which helps minimize tissue damage during in-vivo applications 5,6 . At the distal end of an MMF, coherent light typically emerges as a random speckle pattern due to mode interference and dispersion. To map the input-output relation of signals through MMFs, different calibration methods, such as transmission matrices (TMs) 11–15 , and deep neural networks (DNNs) 16–19 , are commonly employed. For imaging, an array of diffraction-limited focal points is often generated at the fibre’s distal end to scan the imaging object. Such fibre outputs require modulations on the fibre input through wavefront shaping techniques 20,21 . To reconstruct an image with n pixels, an equivalent number of wavefront projections is required to generate the focal points. The imaging speed of the MMF therefore depends on both the number of wavefront frames and their sampling rate. Moreover, MMFs are sensitive to factors like environmental stabilities, fibre bending and deformation. Frequent recalculation on the fibre input-output relation is needed when the fibre state is changed during the imaging process. To address this issue, either a database of TMs or DNNs covering all potential fibre states must be established 4,22–24 , or real-time updating of the TM and DNN must be implemented 7,25 . Recalibrating the MMF and updating wavefront projections remains experimentally intricate and computationally insufficient. Compressive sensing (CS) provides an alternative that allows signals to be sampled at rates significantly below the Nyquist frequency, provided the signal is sparse on some basis 26,27 . It has been demonstrated that compressive imaging through an MMF enables super-resolution in spatial dimensions and improved temporal speed of data acquisition, allowing for the study of sub-diffraction-limited structures and fast processes 28–32 . During the calibration stage, it is only necessary to measure the intensity of a limited number of speckle patterns, which will then serve as the sampling illumination sequence in the imaging stage. This approach bypasses the need for wavefront shaping and raster scanning by compressing the total number of illumination frames required for imaging the target. Most reported MMF imaging techniques are based on fluorescent imaging for optically visualizing beads and biological specimens 21 . Implementation of compressive imaging through a MMF has been reported in applications such as photoacoustic imaging of blood cells 33 , and epi-fluorescence imaging of Alzheimer’s disease (AD) human brain tissue on the slide 34 . It remains of great interest to explore the multifunctionality and modalities of compressive imaging using a single MMF. In this paper, we introduce a dual-modal MMF imaging system based on compressive sensing, capable of both optical imaging and spatial mapping of light-induced photocurrent. Photocurrent mapping through a MMF is achieved by projecting random speckle patterns on the surface of solar cells. Laser beam-induced current mapping is a common method for monitoring the performance of the photovoltaic device by examining defects or degradation in the active layer through its photo response 35 . Unlike traditional scanning mechanisms, which require precise free-space laser alignment and are limited in scanning speed as they direct the beam across the device surface, compressive sensing employs orthogonal patterns to achieve single-pixel mapping with increased speed and resolution 36,37 . To date, compressive current mapping using compressive sensing has relied on projection patterns generated by spatial-light-modulated free-space laser beams 36,38,39 . The spatial resolution is therefore limited to the minimum pixel size (tens of micron) on the spatial light modulator. To the best of our knowledge, compressive photocurrent mapping using a single MMF as a single-pixel light source has not yet been reported. In this work, we show that both optical imaging and photocurrent mapping at the distal end of a thin MMF can be effectively reconstructed using the compressive sensing algorithm. Principle In this section, the principle of using the principle of compressive sensing using an MMF will be described. The scheme of the method is shown in Fig. 1 (a). The process of using an MMF to conduct optical imaging and current mapping can be translated into a linear problem: $${\text{M}}={\text{S}} \times {\text{O}}$$ 1 where \({\text{S}} \in {{\mathbb{R}}^{{\text{i}} \times {\text{j}}}}\) is the measurement matrix, \({\text{O}} \in {{\mathbb{R}}^{\text{j}}}\) is the vector representing the object to be sampled, and \({\text{M}} \in {{\mathbb{R}}^{\text{i}}}\) is the collected data from measurements. The measurement matrix is derived by scanning i beam spots at varied positions on the fibre proximal end, resulting in i distinct speckle patterns at its distal end. Due to variations in excited modes and their random interference when light passes through the fiber, speckle patterns can be uncorrelated to each other 40 . This makes them an ideal basis for achieving compressive measurements 29 . During pre-calibration, a camera captures these speckle patterns, reflecting the total pixel count of each speckle pattern as j . The measurement matrix S ( i × j ) is subsequently constructed by flattening and stacking the vector from each speckle pattern. When the sequence of speckle patterns is projected onto the object, the object’s signal response to each speckle forms the i ×1 measurement vector M . In compressive sensing, the object O is acquired using a number of measurements significantly lower than traditionally required by the Nyquist-Shannon sampling theorem, i.e. i < < j , which lead the linear problem into an ill-posed problem. The compression ratio can be calculated as j / i . To recover the spatial information of the object from fewer measurements, TV minimization by augmented Lagrangian and alternating direction algorithm (TVAL3) are used in our work to reconstruct the object image by solving the following optimization problem 41 : $$\mathop {\arg \hbox{min} }\limits_{{\text{O}}} {\left\| {\text{O}} \right\|_{{\text{TV}}}}{\text{ s}}{\text{.t}}{\text{. SO}}={\text{M, O}} \geqslant 0.$$ 2 In our experiments, we use two types of samples to validate the system’s optical imaging and electrical sampling capabilities, as shown in Fig. 1(b). For fluorescent imaging, we sonicate 20 mg of 10 µm Nile red fluorescent particles in deionized water (SPHERO, FH-1005202, 1.0% w/v concentration) and mix them with 500 mg of Polydimethylsiloxane (PDMS). A droplet of the blend is then deposited onto a 130 µm-thick glass substrate and spin-coated at 6,000 rpm for 60 seconds to ensure an even distribution of the beads. The peak of the emission light from the beads is around 560 nm. For the photovoltaic device spatial characterisation, a thin polymer photomask featuring a negative pattern is applied atop an amorphous solar cell (SANYO, AM-8701) to offer a clearer representation for current mapping, as only the areas exposed to light can produce the light-induced current. The pattern of the mask is designed to be a 3-line pair, akin to a resolution target. The white sections on the mask allow light transmission while the black sections block the light. The width of each line is equal to the distance separating two adjacent lines. Additionally, the length of each line is set to be five times its width, meaning if we define the linewidth as ‘a’, the 3-line pair would take up an area of 5a × 5a. The mapping ability of the system can then be determined from the minimum distinguishable spacing between two lines. Both samples are mounted on a 3-axis translation stage. The area of interest will be carefully aligned to the illuminated area within the speckle patterns. The reconstructed spatial features will correspond to the speckle pixels, which defines the FOV. In this work, we perform the proof-of-concept experiments for dual modalities on two types of the MMF: For photocurrent imaging, we perform current mapping of a solar cell with the features of a few hundreds of microns using an MMF with a diameter of 105 and 200 µm respectively. For optical imaging, we conduct fluorescence imaging on fluorescent beads with a diameter of 10 µm at the tips of both fibre types. Results Photocurrent Mapping The current response from the solar cell is firstly analysed by illuminating the speckle sequence on the far-field end of the MMF with a core diameter of 105 μm. The current response is measured for each MMF speckle pattern. In Fig.2(a), we demonstrate the current reading versus the MMF speckle index, ranging from 0 to 324. The 350 μm linewidth sample displays a higher average current value compared to the 190 μm linewidth sample, owing to higher light transmission through the line-pair photomask. The optical intensity of each speckle is also measured accordingly using an optical power meter (PM100D, Thorlabs) placed on the camera plane. To further verify the efficient response from the solar cell, we narrow the speckle index’s window range of interest and normalise both the current reading and the speckle intensity to a range of 0 to 1, as depicted in Fig.2(b). The current response from both samples matches well with the variance of the speckle intensity. The cross-correlation of the speckle patterns acquired at the fibre distal end is calculated using Pearsson correlation coefficient 32 . The results are depicted in Fig.2(c). The cross-correlation between two distinct speckle patterns is close to zero, which indicates that the speckle patterns are uncorrelated and orthogonal to each other, making them an ideal basis for compressive sensing 30,33 . The calibration time will take no longer than 2 seconds as only 324 frames of speckle patterns will be sequentially played with a speed of 200 fps. Figure.3 (a) and (c) shows the bright-field images of line pairs on the photomask, featuring linewidths of 350 and 190 μm. A white LED is placed in front of the MMF, serving as an incoherent light source that is coupled into the proximal end of the fibre. The reference image is then captured by a camera focused on sample plane 1 (see Methods). We use these images as the ground truth of the excited area of the solar cell. The calibration of the fibre is performed under the same field of view of the camera to capture the image of the speckle pattern at the fibre distal tip which contains 600 × 600 pixels. To save the computation resource, we downsize the speckle pattern image to 96 × 96 pixels. Thus, the actual compression ratio is around 28 (96 × 96/324). The area outside the fibre core is padded with zeros to mitigate the background noise. The boundary of the fibre core is marked in white circle in Fig.3. The current readings from the solar cell and sensing matrix generated from the speckle patterns is then fed into the TVAL3 algorithm. The reconstructed results are shown in Fig.3(b) and (d). The white colour represents the current intensity of the illuminated area of the solar cell, with the peak value normalized to 1. Both images show the current mapping from the solar cell at the same position as the ground truth bright-field image. The line pair are distinguishable to each other, which demonstrate the mapping ability of the fibre. It is worth to note that the reconstructed line pairs are coarse at their edges mainly because of the down sampling of speckle patterns. In the second set of the experiment, finer line-pair features on the photomask with linewidths of 10 and 6 μm are selected for photocurrent mapping on a MMF with a core diameter of 200 μm. However, the negative nature of the photomask and the fine feature sizes within tens of micrometres make it difficult to align the fibre tip to target the transmissive part if the solar cell is attached. To overcome this issue, the photomask is aligned without the solar cell, using a manual translation stage to match the line-pair to the central part of the fibre core. The focused line-pair pattern is magnified by the objective lens and tube lens, and then projected to the camera plane that is conjugate to the solar cell surface (Sample plane 1, see methods). Calibration is performed afterwards by removing the photomask and capturing the speckle patterns at a plane approximately 50 μm away from the MMF distal tip due to the thickness of the photomask. Ideally, the solar cell should be positioned at sample plane 2, as close to the fibre tip as possible without making contact. The feature mask can be coated on top of the active layer of the solar cell in the future for better indication of actual fine features mapping in the near-field sample plane. The coordinate of the feature mask can be programmed with a motorized translation stage for easier targeting. Here, the photocurrent map of the solar cell is still induced in the far-field with a linewidth of a few hundreds of micrometres according to the magnification factor of the system. The solar cell is acting as a bucket detector in this case 42,43 . The value of the linewidth in the following reconstructions are normalised according to the ground truth. In Fig.4(b) and (d), we demonstrate the reconstruction results under the full FOV of the fibre core respectively. Yellow dashed lines are plotted across the horizontal axis of the reconstructed line pairs in order to further study the resolution and contrast of the reconstruction results. As shown in Fig. 4(e), the yellow line represents the signal-to-noise ratio on the reconstructed photocurrent map. The normalised width of each line correlates well with the gap between two lines, as the 6 μm 3-line pair occupies a total of 30 μm while the 10 μm pair spans around 50 μm. Fluorescence Imaging The optical imaging ability the system is finally demonstrated by performing the CS-based fluorescence imaging at the distal tips of both types of the fibre. The beads slide is inserted to the tips of the fibre (Sample plane 2) with minimal spacing. Figure 5(a) and (c) shows the bright field image of two sets of 10 μm fluorescent beads under the FOV of 105 and 200 μm fibre core respectively. The scale bar in Fig.5 is set to 25 μm to show the difference between two fibre cores. Same sets of the speckle patterns from previous calibrations are then projected to the beads. PMT readings with higher signal-to-noise ratio can be obtained as the fluorescent beads require lower laser energy to be excited in contrast to the laser-induced current on the solar cell. The reconstructed 96 × 96 pixels images are shown in Fig.5(b) and (d). The distribution of the beads can be well recovered while a clean and dark background close to zero can be achieved for both fibres. Discussion We have experimentally demonstrated a dual modal MMF-based imaging system that is capable of both photocurrent and optical imaging using compressive sensing. In terms of the spatial characterisation, the imaging FOV is dependent on the core size of fibre, while the spatial resolution is determined by the NA and operating wavelength, calculated as λ/2NA. Theoretically, the fibre used in our work can achieve a spatial resolution at the diffraction limit of 2.66 µm for the 105 µm fibre and 1.21 µm for 200 µm fibre. Our current limitations stem from the size of the photomask (6 µm) placed on top of the solar cell, which is used to mimic the electrical mapping of defects in a solar panel. It is worth to note that spatial characterization beyond the diffraction limit is feasible using the CS-based reconstruction protocol 30 , 31 , 44 . To image at the bare fibre tips, though pre-calibration can be done in seconds, the sampling speed is still limited by the detection module of the system to get the averaged current reading. Real time calibration can be achieved in the case of far-field photocurrent mapping which eliminates the issue of temporal stability of the fibre. The proposed system enables the MMF to extend its application beyond optical microscopy, also allowing for detection and spatial characterisation on photocurrent. This advancement could pave the way for the development of a thin fibre endoscope, which is capable of performing optical and photocurrent imaging at the same time. Two potential applications can be further developed based on the proposed system:1) During the fabrication process of photovoltaic devices 45 , integrating the MMF with a high-power fibre laser at its output end allows the MMF to be used for locating defects or traps at the interface, monitoring device degradation, and performing laser patterning and ablation with precise targeting. 2) To study the electrical activity of cells, particularly neurons, calcium imaging, measures changes in calcium ion concentration, which are an indirect indicator of electrical activity. With the development of genetically encoded voltage indicators, voltage imaging can provide a more accurate and immediate representation of neuronal activity by directly measuring changes in membrane potential 46 , 47 . The conformational changes in the voltage-sensitive dye are translated into changes in fluorescence occurring at milliseconds level. Therefore, high sampling rate of the imaging probe is necessary to capture the rapid changes in fluorescence 48 , 49 . Compressive sensing-based fibre probe will naturally outperform the raster-scanning one in this application for much smaller number of measurements. To conclude, by employing thin MMFs as the imaging probe combined with compressive sensing methods, we achieved micron-level spatial characterization in both photocurrent and optical imaging modalities. The calibration and imaging process can be completed within 2 seconds, with a compression ratio 28 times below the Nyquist-Shannon limit. The proposed dual-modal compressive MMF imaging system offers new possibilities for multi-modal imaging through combined photocurrent and optical signal analysis, as well as for achieving enhanced spatial resolution and high imaging speed in the spatial characterization. Methods The experimental setup is depicted in Fig.6, consisting of four primary modules: Imaging Probe: For this experiment, we select step-index MMFs with core diameters of 105 μm (Thorlabs, FG105LVA, 0.1 NA) and 200 μm (Thorlabs, FG200UEA, 0.22 NA). Both fibres had their acrylate coatings removed and were cleaved at both ends. Each fibre measures 9 cm in length. Calibration Module: A 20 × objective lens (OL2, Olympus 20× Plan N, 0.4 NA) and a tube lens are placed at the MMF's distal end. This setup magnifies the output speckle patterns from the MMF to the imaging plane of a CMOS camera (Qimaing, Optimos). The magnification factor of the speckle size on the camera imaging plane is ~22. A beam splitter (BS, Thorlabs, CCM1-BS013) is positioned 10 cm in front of the camera to direct the speckle pattern to the sample plane. Real-time calibration is achievable by maintaining the same optical path length from the BS to both the imaging and sampling planes (marked as sample plane 1). The whole calibration arm will be removed to make space for inserting the sample at the distal end of fibre. Detection Module: For fluorescence imaging, emission light from fluorescent beads is travelled back through the same fibre and then reflected by a dichroic mirror (DM, Thorlabs, DMSP550R) set at a 45° angle. This DM acts as a short pass filter with a cutoff wavelength of 533nm. An additional bandpass filter (F1, Edmund Optics, 592nm with 43nm bandwidth) further blocks residual 532 nm excitation light before the beam enters the photomultiplier tube (PMT, Hamamatsu H10722-20-01). A shutter shields the PMT from light exposure during idle times. The voltage reading from the PMT is then send to a USB oscilloscope (Picoscope 2206B) for data logging. The signal is captured at the sampling rate of 200 kS/s and we calculate the averaged value within each illumination period by thresholding at the rising edge of each trigger pulse. The background voltage of the system is also recorded without any sample at the fibre distal in order to increase the signal-to-noise ratio. The measurement vector is then generated by subtracting the background noise. For photovoltaic device mapping, the light-induced short-circuit current is recorded by a digital multimeter (Keithley 2450) connected to the solar cell's electrodes. The sampling rate is limited to 10 S/s to achieve a high-resolution of current reading. Declarations Acknowledgements L.S. acknowledges financial support from Engineering and Physical Sciences Research Council (grant number EP/L022559/1, EP/L022559/2, EP/V050311/1, EP/W004399/1 and EP/Y008405/1), Royal Society (grant number RG130230, IE161214) and H2020 Marie Skłodowska-Curie Actions (grant number 790666). Author contributions L.S. and Y.W. conceived the idea. Y.W. performed the experiments and processed the experimental data. H.Z. prepared the imaging sample. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5819817","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":431685070,"identity":"c24b43f0-6469-4e29-9df4-2736f01d1945","order_by":0,"name":"Lei Su","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqElEQVRIiWNgGAWjYBACPhDxAcZgYEggrIUNiBlnQBnEa2HmIVEL+zNpm7JtiW0MzA8/MLalEaOFx0w659xtoBY2YwnGthyitLBJ57aBtDCYMTC2VRDpMEuwFvZvxGphMJNmBGvhAdlCjMOYeYwte87dNm5j5imWSDhHhPf52dsf3vhRdlu2n71944cPZcmEtTAwQ50HZiQQoQHmPOKVjoJRMApGwQgEACuNKn2tUGk1AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-7894-7881","institution":"Queen Mary University of London","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Su","suffix":""},{"id":431685071,"identity":"2fa66394-3ed8-432a-8bb9-10a330ce144f","order_by":1,"name":"Yufei Wang","email":"","orcid":"https://orcid.org/0000-0001-7357-0168","institution":"Queen Mary University of London","correspondingAuthor":false,"prefix":"","firstName":"Yufei","middleName":"","lastName":"Wang","suffix":""},{"id":431685072,"identity":"20823c61-d15a-4148-9ce5-5132c85e9fdb","order_by":2,"name":"Hangfeng Zhang","email":"","orcid":"https://orcid.org/0000-0002-3928-8772","institution":"School of Biological and Chemical Sciences, Queen Mary University of London","correspondingAuthor":false,"prefix":"","firstName":"Hangfeng","middleName":"","lastName":"Zhang","suffix":""},{"id":431685073,"identity":"9f8b3b48-b231-4c33-b4b7-c0b2f8eafca4","order_by":3,"name":"Sanjukta Sarkar","email":"","orcid":"","institution":"University of Cambridge","correspondingAuthor":false,"prefix":"","firstName":"Sanjukta","middleName":"","lastName":"Sarkar","suffix":""},{"id":431685074,"identity":"99a19d95-7df9-4788-b14f-0dc4858801fd","order_by":4,"name":"Wen Wang","email":"","orcid":"https://orcid.org/0000-0002-6913-4731","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-01-13 12:10:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5819817/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5819817/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79115199,"identity":"adb748d3-cd06-4d9d-bce3-d38e36b58cde","added_by":"auto","created_at":"2025-03-24 14:56:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":521829,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of dual modal spatial characterisation using an MMF. (a) Principle of compressive imaging using speckle patterns. (b) Speckle projections on the sample. Left: Imaging on fluorescent beads. Right: Current mapping on a solar cell with a mask.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5819817/v1/3040fe479627b0ebb1ab5e43.png"},{"id":79115202,"identity":"4c8a756e-0039-4837-ad5f-bb81b5922c21","added_by":"auto","created_at":"2025-03-24 14:56:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":579076,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Current reading of the solar cell. (b) Normalised speckle intensity versus normalised current variance. Blue curve:350 μm features. Red curve 190 μm features (c) Cross-correlation map of 324 speckle patterns.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5819817/v1/dea0eda4b1d97173621b80cf.png"},{"id":79115520,"identity":"6707d8a8-2e99-40c2-b1c8-1b5b16287390","added_by":"auto","created_at":"2025-03-24 15:04:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":99214,"visible":true,"origin":"","legend":"\u003cp\u003eCurrent mapping of the features on the solar cell using a MMF with a diameter of 105 μm. (a) Bright field image of the negative photomask line pairs with a linewidth of 350 μm and (c) 190 μm features. (b) 96 × 96 reconstructed current mapping of the solar cell with a linewidth of 350 μm and (d) 190 μm features. Scale bar is 1000 μm.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5819817/v1/0123d15f9f802ff7e845ed5c.png"},{"id":79115221,"identity":"a92c29d6-7624-48f3-ac10-dd66ba571f95","added_by":"auto","created_at":"2025-03-24 14:56:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":153792,"visible":true,"origin":"","legend":"\u003cp\u003eCurrent mapping of the features on the solar cell using a MMF with a diameter of 200 μm. (a) Bright field image of the negative photomask line pairs with a linewidth of 10 μm and (c) 6 μm features. Scale bar is 25 μm. (b) 96 × 96 reconstructed current mapping of the solar cell with a normalised linewidth of 10 μm and (d) 6 μm features. In practice, Scale bar is 550 μm. (e) Cross-sectional pixel intensity scan.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5819817/v1/6a3330f99ae9d123437b61dc.png"},{"id":79115204,"identity":"1f8cbd8d-834e-4e39-a842-4f8fe4bbaf21","added_by":"auto","created_at":"2025-03-24 14:56:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":142518,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental results for fluorescence imaging at the distal end of the MMF. (a) Bright-field image of 10 μm fluorescent beads under the FOV of 105 μm and (d) 200 μm fibre core respectively. (b) Reconstructed image using the fibre probe. The scale bars are 25 μm.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5819817/v1/9bb0cea6b29edd70f0866509.png"},{"id":79115220,"identity":"505531f3-f347-4c75-8d65-5816dec99037","added_by":"auto","created_at":"2025-03-24 14:56:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":339445,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of experimental setup for fluorescent imaging and current mapping. DMD: Digital Micro Mirror; L1: Achromatic Doublet, f = 200 mm; L2, L4: Achromatic Doublet, f = 50 mm; L3: Achromatic Doublet, f = 200 mm; DM: Dichroic Mirror; M1: Silver mirror; F1: Bandpass Filter; S: Shutter; PMT: Photomultiplier tube; OBJ1, OBJ2: Objective lenses; BS: Beam Splitter; CAM: CCD camera; SC: Solar Cell; DMM: Digital Multi-meter.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5819817/v1/8e71a7f1dae52877c478148a.png"},{"id":79117160,"identity":"985d64e0-b6f3-48fd-945b-ceb130d776f6","added_by":"auto","created_at":"2025-03-24 15:20:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2230156,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5819817/v1/70b7a14d-16c1-49e6-8ea2-5077c75a8772.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Dual modal compressive photocurrent and optical imaging through a multimode fibre","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003eultimode fibres (MMF), owing to their distinct physical structure, including a larger fibre core and multiple fibre modes, have emerged as promising tools in endoscopic technology\u003csup\u003e1,2\u003c/sup\u003e. Their capacity for high-density image transmission enables minimally invasive optical access deep within tissues\u003csup\u003e2\u0026ndash;10\u003c/sup\u003e. Compared to traditional imaging endoscopes, MMFs have a smaller diameter, which helps minimize tissue damage during in-vivo applications\u003csup\u003e5,6\u003c/sup\u003e. At the distal end of an MMF, coherent light typically emerges as a random speckle pattern due to mode interference and dispersion. To map the input-output relation of signals through MMFs, different calibration methods, such as transmission matrices (TMs)\u003csup\u003e11\u0026ndash;15\u003c/sup\u003e, and deep neural networks (DNNs)\u003csup\u003e16\u0026ndash;19\u003c/sup\u003e, are commonly employed. For imaging, an array of diffraction-limited focal points is often generated at the fibre\u0026rsquo;s distal end to scan the imaging object. Such fibre outputs require modulations on the fibre input through wavefront shaping techniques\u003csup\u003e20,21\u003c/sup\u003e. To reconstruct an image with \u003cem\u003en\u003c/em\u003e pixels, an equivalent number of wavefront projections is required to generate the focal points. The imaging speed of the MMF therefore depends on both the number of wavefront frames and their sampling rate. Moreover, MMFs are sensitive to factors like environmental stabilities, fibre bending and deformation. Frequent recalculation on the fibre input-output relation is needed when the fibre state is changed during the imaging process. To address this issue, either a database of TMs or DNNs covering all potential fibre states must be established\u003csup\u003e4,22\u0026ndash;24\u003c/sup\u003e, or real-time updating of the TM and DNN must be implemented\u003csup\u003e7,25\u003c/sup\u003e. Recalibrating the MMF and updating wavefront projections remains experimentally intricate and computationally insufficient.\u003c/p\u003e\n\u003cp\u003eCompressive sensing (CS) provides an alternative that allows signals to be sampled at rates significantly below the Nyquist frequency, provided the signal is sparse on some basis\u003csup\u003e26,27\u003c/sup\u003e. It has been demonstrated that compressive imaging through an MMF enables super-resolution in spatial dimensions and improved temporal speed of data acquisition, allowing for the study of sub-diffraction-limited structures and fast processes\u003csup\u003e28\u0026ndash;32\u003c/sup\u003e. During the calibration stage, it is only necessary to measure the intensity of a limited number of speckle patterns, which will then serve as the sampling illumination sequence in the imaging stage. This approach bypasses the need for wavefront shaping and raster scanning by compressing the total number of illumination frames required for imaging the target. Most reported MMF imaging techniques are based on fluorescent imaging for optically visualizing beads and biological specimens\u003csup\u003e21\u003c/sup\u003e. Implementation of compressive imaging through a MMF has been reported in applications such as photoacoustic imaging of blood cells\u003csup\u003e33\u003c/sup\u003e, and epi-fluorescence imaging of Alzheimer\u0026rsquo;s disease (AD) human brain tissue on the slide\u003csup\u003e34\u003c/sup\u003e. It remains of great interest to explore the multifunctionality and modalities of compressive imaging using a single MMF.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this paper, we introduce a dual-modal MMF imaging system based on compressive sensing, capable of both optical imaging and spatial mapping of light-induced photocurrent. Photocurrent mapping through a MMF is achieved by projecting random speckle patterns on the surface of solar cells. Laser beam-induced current mapping is a common method for monitoring the performance of the photovoltaic device by examining defects or degradation in the active layer through its photo response\u003csup\u003e35\u003c/sup\u003e. Unlike traditional scanning mechanisms, which require precise free-space laser alignment and are limited in scanning speed as they direct the beam across the device surface, compressive sensing employs orthogonal patterns to achieve single-pixel mapping with increased speed and resolution\u003csup\u003e36,37\u003c/sup\u003e. To date, compressive current mapping using compressive sensing has relied on projection patterns generated by spatial-light-modulated free-space laser beams\u003csup\u003e36,38,39\u003c/sup\u003e. The spatial resolution is therefore limited to the minimum pixel size (tens of micron) on the spatial light modulator. To the best of our knowledge, compressive photocurrent mapping using a single MMF as a single-pixel light source has not yet been reported. In this work, we show that both optical imaging and photocurrent mapping at the distal end of a thin MMF can be effectively reconstructed using the compressive sensing algorithm.\u003c/p\u003e"},{"header":"Principle","content":"\u003cp\u003eIn this section, the principle of using the principle of compressive sensing using an MMF will be described. The scheme of the method is shown in Fig.\u0026nbsp;1 (a). The process of using an MMF to conduct optical imaging and current mapping can be translated into a linear problem:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${\\text{M}}={\\text{S}} \\times {\\text{O}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{S}} \\in {{\\mathbb{R}}^{{\\text{i}} \\times {\\text{j}}}}\\)\u003c/span\u003e\u003c/span\u003e is the measurement matrix, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{O}} \\in {{\\mathbb{R}}^{\\text{j}}}\\)\u003c/span\u003e\u003c/span\u003eis the vector representing the object to be sampled, and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{M}} \\in {{\\mathbb{R}}^{\\text{i}}}\\)\u003c/span\u003e\u003c/span\u003eis the collected data from measurements. The measurement matrix is derived by scanning \u003cb\u003ei\u003c/b\u003e beam spots at varied positions on the fibre proximal end, resulting in \u003cb\u003ei\u003c/b\u003e distinct speckle patterns at its distal end. Due to variations in excited modes and their random interference when light passes through the fiber, speckle patterns can be uncorrelated to each other\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. This makes them an ideal basis for achieving compressive measurements\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. During pre-calibration, a camera captures these speckle patterns, reflecting the total pixel count of each speckle pattern as \u003cb\u003ej\u003c/b\u003e. The measurement matrix \u003cb\u003eS\u003c/b\u003e (\u003cb\u003ei\u003c/b\u003e \u0026times; \u003cb\u003ej\u003c/b\u003e) is subsequently constructed by flattening and stacking the vector from each speckle pattern. When the sequence of speckle patterns is projected onto the object, the object\u0026rsquo;s signal response to each speckle forms the \u003cb\u003ei\u003c/b\u003e \u0026times;1 measurement vector \u003cb\u003eM\u003c/b\u003e. In compressive sensing, the object \u003cb\u003eO\u003c/b\u003e is acquired using a number of measurements significantly lower than traditionally required by the Nyquist-Shannon sampling theorem, i.e. \u003cb\u003ei\u003c/b\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u0026lt;\u0026thinsp;\u003cb\u003ej\u003c/b\u003e, which lead the linear problem into an ill-posed problem. The compression ratio can be calculated as \u003cb\u003ej\u003c/b\u003e/\u003cb\u003ei\u003c/b\u003e. To recover the spatial information of the object from fewer measurements, TV minimization by augmented Lagrangian and alternating direction algorithm (TVAL3) are used in our work to reconstruct the object image by solving the following optimization problem\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\mathop {\\arg \\hbox{min} }\\limits_{{\\text{O}}} {\\left\\| {\\text{O}} \\right\\|_{{\\text{TV}}}}{\\text{ s}}{\\text{.t}}{\\text{. SO}}={\\text{M, O}} \\geqslant 0.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn our experiments, we use two types of samples to validate the system\u0026rsquo;s optical imaging and electrical sampling capabilities, as shown in Fig.\u0026nbsp;1(b). For fluorescent imaging, we sonicate 20 mg of 10 \u0026micro;m Nile red fluorescent particles in deionized water (SPHERO, FH-1005202, 1.0% w/v concentration) and mix them with 500 mg of Polydimethylsiloxane (PDMS). A droplet of the blend is then deposited onto a 130 \u0026micro;m-thick glass substrate and spin-coated at 6,000 rpm for 60 seconds to ensure an even distribution of the beads. The peak of the emission light from the beads is around 560 nm. For the photovoltaic device spatial characterisation, a thin polymer photomask featuring a negative pattern is applied atop an amorphous solar cell (SANYO, AM-8701) to offer a clearer representation for current mapping, as only the areas exposed to light can produce the light-induced current. The pattern of the mask is designed to be a 3-line pair, akin to a resolution target. The white sections on the mask allow light transmission while the black sections block the light. The width of each line is equal to the distance separating two adjacent lines. Additionally, the length of each line is set to be five times its width, meaning if we define the linewidth as \u0026lsquo;a\u0026rsquo;, the 3-line pair would take up an area of 5a \u0026times; 5a. The mapping ability of the system can then be determined from the minimum distinguishable spacing between two lines. Both samples are mounted on a 3-axis translation stage. The area of interest will be carefully aligned to the illuminated area within the speckle patterns. The reconstructed spatial features will correspond to the speckle pixels, which defines the FOV.\u003c/p\u003e\u003cp\u003eIn this work, we perform the proof-of-concept experiments for dual modalities on two types of the MMF: For photocurrent imaging, we perform current mapping of a solar cell with the features of a few hundreds of microns using an MMF with a diameter of 105 and 200 \u0026micro;m respectively. For optical imaging, we conduct fluorescence imaging on fluorescent beads with a diameter of 10 \u0026micro;m at the tips of both fibre types.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePhotocurrent Mapping\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current response from the solar cell is firstly analysed by illuminating the speckle sequence on the far-field end of the MMF with a core diameter of 105 \u0026mu;m. The current response is measured for each MMF speckle pattern. In Fig.2(a), we demonstrate the current reading versus the MMF speckle index, ranging from 0 to 324. \u0026nbsp;The 350 \u0026mu;m linewidth sample displays a higher average current value compared to the 190 \u0026mu;m linewidth sample, owing to higher light transmission through the line-pair photomask. The optical intensity of each speckle is also measured accordingly using an optical power meter (PM100D, Thorlabs) placed on the camera plane. To further verify the efficient response from the solar cell, we narrow the speckle index\u0026rsquo;s window range of interest and normalise both the current reading and the speckle intensity to a range of 0 to 1, as depicted in Fig.2(b). The current response from both samples matches well with the variance of the speckle intensity. The cross-correlation of the speckle patterns acquired at the fibre distal end is calculated using Pearsson correlation coefficient\u003csup\u003e32\u003c/sup\u003e. The results are depicted in Fig.2(c). The cross-correlation between two distinct speckle patterns is close to zero, which indicates that the speckle patterns are uncorrelated and orthogonal to each other, making them an ideal basis for compressive sensing\u003csup\u003e30,33\u003c/sup\u003e. The calibration time will take no longer than 2 seconds as only 324 frames of speckle patterns will be sequentially played with a speed of 200 fps.\u003c/p\u003e\n\u003cp\u003eFigure.3 (a) and (c) shows the bright-field images of line pairs on the photomask, featuring linewidths of 350 and 190 \u0026mu;m. A white LED is placed in front of the MMF, serving as an incoherent light source that is coupled into the proximal end of the fibre. The reference image is then captured by a camera focused on sample plane 1 (see Methods). We use these images as the ground truth of the excited area of the solar cell. The calibration of the fibre is performed under the same field of view of the camera to capture the image of the speckle pattern at the fibre distal tip which contains 600 \u0026times; 600 pixels. To save the computation resource, we downsize the speckle pattern image to 96 \u0026times; 96 pixels. Thus, the actual compression ratio is around 28 (96 \u0026times; 96/324). The area outside the fibre core is padded with zeros to mitigate the background noise. The boundary of the fibre core is marked in white circle in Fig.3. The current readings from the solar cell and sensing matrix generated from the speckle patterns is then fed into the TVAL3 algorithm. The reconstructed results are shown in Fig.3(b) and (d). The white colour represents the current intensity of the illuminated area of the solar cell, with the peak value normalized to 1. Both images show the current mapping from the solar cell at the same position as the ground truth bright-field image. The line pair are distinguishable to each other, which demonstrate the mapping ability of the fibre. It is worth to note that the reconstructed line pairs are coarse at their edges mainly because of the down sampling of speckle patterns.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the second set of the experiment, finer line-pair features on the photomask with linewidths of 10 and 6 \u0026mu;m are selected for photocurrent mapping on a MMF with a core diameter of 200 \u0026mu;m. However, the negative nature of the photomask and the fine feature sizes within tens of micrometres make it difficult to align the fibre tip to target the transmissive part if the solar cell is attached. To overcome this issue, the photomask is aligned without the solar cell, using a manual translation stage to match the line-pair to the central part of the fibre core. The focused line-pair pattern is magnified by the objective lens and tube lens, and then projected to the camera plane that is conjugate to the solar cell surface (Sample plane 1, see methods). Calibration is performed afterwards by removing the photomask and capturing the speckle patterns at a plane approximately 50 \u0026mu;m away from the MMF distal tip due to the thickness of the photomask.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIdeally, the solar cell should be positioned at sample plane 2, as close to the fibre tip as possible without making contact. The feature mask can be coated on top of the active layer of the solar cell in the future for better indication of actual fine features mapping in the near-field sample plane. The coordinate of the feature mask can be programmed with a motorized translation stage for easier targeting. Here, the photocurrent map of the solar cell is still induced in the far-field with a linewidth of a few hundreds of micrometres according to the magnification factor of the system. The solar cell is acting as a bucket detector in this case\u003csup\u003e42,43\u003c/sup\u003e. The value of the linewidth in the following reconstructions are normalised according to the ground truth.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn Fig.4(b) and (d), we demonstrate the reconstruction results under the full FOV of the fibre core respectively. Yellow dashed lines are plotted across the horizontal axis of the reconstructed line pairs in order to further study the resolution and contrast of the reconstruction results. As shown in Fig. 4(e), the yellow line represents the signal-to-noise ratio on the reconstructed photocurrent map. The normalised width of each line correlates well with the gap between two lines, as the 6 \u0026mu;m 3-line pair occupies a total of 30 \u0026mu;m while the 10 \u0026mu;m pair spans around 50 \u0026mu;m.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFluorescence Imaging\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe optical imaging ability the system is finally demonstrated by performing the CS-based fluorescence imaging at the distal tips of both types of the fibre. The beads slide is inserted to the tips of the fibre (Sample plane 2) with minimal spacing. Figure 5(a) and (c) shows the bright field image of two sets of 10 \u0026mu;m fluorescent beads under the FOV of 105 and 200 \u0026mu;m fibre core respectively. The scale bar in Fig.5 is set to 25 \u0026mu;m to show the difference between two fibre cores. Same sets of the speckle patterns from previous calibrations are then projected to the beads. PMT readings with higher signal-to-noise ratio can be obtained as the fluorescent beads require lower laser energy to be excited in contrast to the laser-induced current on the solar cell. The reconstructed 96 \u0026times; 96 pixels images are shown in Fig.5(b) and (d). The distribution of the beads can be well recovered while a clean and dark background close to zero can be achieved for both fibres.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe have experimentally demonstrated a dual modal MMF-based imaging system that is capable of both photocurrent and optical imaging using compressive sensing. In terms of the spatial characterisation, the imaging FOV is dependent on the core size of fibre, while the spatial resolution is determined by the NA and operating wavelength, calculated as λ/2NA. Theoretically, the fibre used in our work can achieve a spatial resolution at the diffraction limit of 2.66 \u0026micro;m for the 105 \u0026micro;m fibre and 1.21 \u0026micro;m for 200 \u0026micro;m fibre. Our current limitations stem from the size of the photomask (6 \u0026micro;m) placed on top of the solar cell, which is used to mimic the electrical mapping of defects in a solar panel. It is worth to note that spatial characterization beyond the diffraction limit is feasible using the CS-based reconstruction protocol\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. To image at the bare fibre tips, though pre-calibration can be done in seconds, the sampling speed is still limited by the detection module of the system to get the averaged current reading. Real time calibration can be achieved in the case of far-field photocurrent mapping which eliminates the issue of temporal stability of the fibre.\u003c/p\u003e \u003cp\u003eThe proposed system enables the MMF to extend its application beyond optical microscopy, also allowing for detection and spatial characterisation on photocurrent. This advancement could pave the way for the development of a thin fibre endoscope, which is capable of performing optical and photocurrent imaging at the same time. Two potential applications can be further developed based on the proposed system:1) During the fabrication process of photovoltaic devices\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, integrating the MMF with a high-power fibre laser at its output end allows the MMF to be used for locating defects or traps at the interface, monitoring device degradation, and performing laser patterning and ablation with precise targeting. 2) To study the electrical activity of cells, particularly neurons, calcium imaging, measures changes in calcium ion concentration, which are an indirect indicator of electrical activity. With the development of genetically encoded voltage indicators, voltage imaging can provide a more accurate and immediate representation of neuronal activity by directly measuring changes in membrane potential\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The conformational changes in the voltage-sensitive dye are translated into changes in fluorescence occurring at milliseconds level. Therefore, high sampling rate of the imaging probe is necessary to capture the rapid changes in fluorescence\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Compressive sensing-based fibre probe will naturally outperform the raster-scanning one in this application for much smaller number of measurements.\u003c/p\u003e \u003cp\u003eTo conclude, by employing thin MMFs as the imaging probe combined with compressive sensing methods, we achieved micron-level spatial characterization in both photocurrent and optical imaging modalities. The calibration and imaging process can be completed within 2 seconds, with a compression ratio 28 times below the Nyquist-Shannon limit. The proposed dual-modal compressive MMF imaging system offers new possibilities for multi-modal imaging through combined photocurrent and optical signal analysis, as well as for achieving enhanced spatial resolution and high imaging speed in the spatial characterization.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe experimental setup is depicted in Fig.6, consisting of four primary modules:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImaging Probe:\u003c/strong\u003e\u0026nbsp; For this experiment, we select step-index MMFs with core diameters of 105 \u0026mu;m (Thorlabs, FG105LVA, 0.1 NA) and 200 \u0026mu;m (Thorlabs, FG200UEA, 0.22 NA). Both fibres had their acrylate coatings removed and were cleaved at both ends. Each fibre measures 9 cm in length.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCalibration Module:\u003c/strong\u003e A 20 \u0026times; objective lens (OL2, Olympus 20\u0026times; Plan N, 0.4 NA) and a tube lens are placed at the MMF\u0026apos;s distal end. This setup magnifies the output speckle patterns from the MMF to the imaging plane of a CMOS camera (Qimaing, Optimos). The magnification factor of the speckle size on the camera imaging plane is ~22. A beam splitter (BS, Thorlabs, CCM1-BS013) is positioned 10 cm in front of the camera to direct the speckle pattern to the sample plane. Real-time calibration is achievable by maintaining the same optical path length from the BS to both the imaging and sampling planes (marked as sample plane 1). The whole calibration arm will be removed to make space for inserting the sample at the distal end of fibre.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetection Module:\u003c/strong\u003e For fluorescence imaging, emission light from fluorescent beads is travelled back through the same fibre and then reflected by a dichroic mirror (DM, Thorlabs, DMSP550R) set at a 45\u0026deg; angle. This DM acts as a short pass filter with a cutoff wavelength of 533nm. An additional bandpass filter (F1, Edmund Optics, 592nm with 43nm bandwidth) further blocks residual 532 nm excitation light before the beam enters the photomultiplier tube (PMT, Hamamatsu H10722-20-01). A shutter shields the PMT from light exposure during idle times. The voltage reading from the PMT is then send to a USB oscilloscope (Picoscope 2206B) for data logging. The signal is captured at the sampling rate of 200 kS/s and we calculate the averaged value within each illumination period by thresholding at the rising edge of each trigger pulse. The background voltage of the system is also recorded without any sample at the fibre distal in order to increase the signal-to-noise ratio. The measurement vector is then generated by subtracting the background noise. \u0026nbsp;For photovoltaic device mapping, the light-induced short-circuit current is recorded by a digital multimeter (Keithley 2450) connected to the solar cell\u0026apos;s electrodes. The sampling rate is limited to 10 S/s to achieve a high-resolution of current reading.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL.S. acknowledges financial support from Engineering and Physical Sciences Research Council (grant number EP/L022559/1, EP/L022559/2, EP/V050311/1, EP/W004399/1 and EP/Y008405/1), Royal Society (grant number RG130230, IE161214) and H2020 Marie Skłodowska-Curie Actions (grant number 790666).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL.S. and Y.W. conceived the idea. Y.W. performed the experiments and processed the experimental data. H.Z. prepared the imaging sample. S.S. assisted in building up the optical system. Y.W. and L.S. wrote the manuscript. W.W. and L.S. provided overall supervision. All authors contributed to finalizing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eČižm\u0026aacute;r, T. \u0026amp; Dholakia, K. Exploiting multimode waveguides for pure fibre-based imaging. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, (2012).\u003c/li\u003e\n\u003cli\u003ePapadopoulos, I. N., Farahi, S., Moser, C. \u0026amp; Psaltis, D. High-resolution, lensless endoscope based on digital scanning through a multimode optical fiber. \u003cem\u003eBiomed. Opt. 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Commun.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 1\u0026ndash;12 (2024).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5819817/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5819817/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMultimode fibres (MMFs) have gained attention for their potential in high-resolution, minimally invasive imaging applications due to their small diameter and high-density signal transmission. However, challenges such as mode interference, environmental sensitivity, and the need for frequent recalibration have limited their practical imaging applications. Optical imaging using MMFs usually relies on wavefront shaping and raster-scanning techniques, which require extended calibration and imaging time along with high computational resources. Here we present a dual-modal MMF imaging system based on compressive sensing, enabling both optical imaging and photocurrent mapping. By projecting random speckle patterns onto photovoltaic surfaces, photocurrent mapping is achieved without the need for raster scanning. Experimental results demonstrate the system’s capability to achieve micron-level spatial characterization, with spatial resolution determined by the fibre’s numerical aperture and operating wavelength. Calibration and imaging processes are completed within 2 seconds, with a compression ratio 28 times below the Nyquist limit. This dual-modal imaging approach paves the way for fibre-based endoscopes capable of simultaneous optical and electrical characterizations, offering new opportunities in biomedical imaging and material science.\u003c/p\u003e","manuscriptTitle":"Dual modal compressive photocurrent and optical imaging through a multimode fibre","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-24 14:56:36","doi":"10.21203/rs.3.rs-5819817/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"573cb8e0-1a3c-40b9-afb0-573799c5a3e4","owner":[],"postedDate":"March 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":45980858,"name":"Physical sciences/Optics and photonics/Optical techniques/Imaging and sensing"},{"id":45980859,"name":"Physical sciences/Engineering/Biomedical engineering"},{"id":45980860,"name":"Physical sciences/Optics and photonics/Applied optics/Fibre optics and optical communications"}],"tags":[],"updatedAt":"2026-01-14T14:09:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-24 14:56:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5819817","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5819817","identity":"rs-5819817","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}