Large Area Kidney Imaging for Pre-transplant Evaluation using Real-Time Robotic Optical Coherence Tomography

Optical coherence tomography (OCT) is a high-resolution imaging modality that can be used to image microstructures of human kidneys. These images can be analyzed to evaluate the viability of the organ for transplantation. However, current OCT devices suffer from insufficient field-of-view, leading to biased examination outcomes when only small portions of the kidney can be assessed. Here we present a robotic OCT system where an OCT probe is integrated with a robotic manipulator, enabling wider area spatially-resolved imaging. With the proposed system, it becomes possible to comprehensively scan the kidney surface and provide large area parameterization of the microstructures. We verified the probe tracking accuracy with a phantom as 0.0762±0.0727 mm and demonstrated its clinical feasibility by scanning ex vivo kidneys. The parametric map exhibits fine vasculatures beneath the kidney surface. Quantitative analysis on the proximal convoluted tubule from the ex vivo human kidney yields highly clinical-relevant information.


Introduction
The global shortage of suitable organs has led to a significant backlog of patients with end-stage renal diseases (ESRD) who are awaiting kidney transplantation 1 .This situation is largely due to the lack of a reliable means to assess the viability of available kidneys for transplant 2 , which results in a high discard rate of donor organs 3 .Quantitative evaluation of deceased donor kidneys is commonly performed using the Kidney Donor Profile Index (KDPI) 2 , derived from the donor's medical history, and the pathological scoring [4][5][6] from pre-transplant donor biopsy (PTDB).Nonetheless, the correspondence between KDPI and the success of the transplant has not yet been definitively established [7][8][9][10][11][12] .On the other hand, PTDB is an invasive procedure with restricted sampling volume that can introduce bias to the organ scoring [13][14][15] .A recent comparative study on four commonly used biopsy scoring systems shows none of them is significantly associated with post-transplant graft survival or early graft function 16 .Therefore, there is a strong need for alternative pre-transplant kidney evaluation methods to be developed for predicting future graft function that could help minimize organ discards on deceased-donor kidneys.
Optical coherence tomography (OCT) is a well-established imaging modality capable of providing highresolution, in situ, and real-time 2D cross-sectional imaging (B-scans) of biological samples 17 .Recent studies have demonstrated the ability of OCT to provide non-invasive histopathological information about the kidney [18][19][20][21] .Moreover, pre-transplant OCT imaging of kidneys has been shown to enable the prediction of post-transplant renal function 22 .Such prediction can be made by evaluating the morphology of the proximal convoluted tubule (PCT) lumen structures 22,23 .Hence, incorporating OCT-based scoring as an adjunct evaluation step to the already widely adopted KDPI and PTDB methods could enhance the reliability of the organ quality evaluation outcome.
Unlike PTDB, OCT imaging is non-invasive and contactless, thus it is feasible to scan over the entire kidney surface and create a spatially-resolved score map in order to minimize the risk of getting a biased OCT-based organ quality evaluation.However, this is challenging because most of the reported OCT systems in literature only provide sub-centimeter level narrow lateral field-of-view (FOV) while maintaining a high lateral resolution (e.g., around tens of microns) [24][25][26] , largely due to mechanical constraints posted on the OCT beam sweep angle 27 .Unfortunately, such a limited FOV is inadequate to capture a regular sized human kidney (~120 mm by ~50 mm 28 ).While the emerging miniaturized handheld OCT systems 29,30 allow scanning wider area by positioning the OCT probe at multiple places, the functionality to precisely track the probe in free space is absent, making it difficult to spatially correspond B-scans with the sample.It is, therefore, necessary to investigate new OCT imaging systems capable of wider area imaging and accurate probe localization.
Various attempts have been made to enable large area spatially resolved OCT imaging.Song et al. developed a stationary OCT system using akinetic swept source and wide-angle camera lens to expand the FOV of conventional tabletop OCT 31 .The system achieved ultra-fast (a few seconds) large FOV (200 by 200 mm) imaging, yet resulting in relatively lower resolution.Therefore, the system is not best suited for resolution-sensitive applications such as kidney imaging.Qin et al. presented a hand-held OCT probe 32 which offers unconstrained FOV.The probe is tracked in six degree-of-freedom (DoF) using visual-odometry (VO) techniques 33 through an RGB-depth camera mounted on the probe which perceives the probe's surrounding environment.Yet, the VO algorithm's localization accuracy depends heavily on the detection of trackable environmental features, hence, is not robust across different in-door conditions.The probe tracking accuracy may be further compromised as the scan trajectory generated by human hands contains considerable trembling.To address these drawbacks, robotic OCT (R-OCT) systems providing large FOV with accurate scanning motion and consistent localization accuracy have been investigated.Göb et al. integrated an OCT system with a Cartesian robot platform for large area vascular contrast skin imaging 34 .Nonetheless, the self-built translational stage has restricted range of motion and probe manipulability.Draelos et al. introduced R-OCT system 35,36 using a six DoF robotic arm for OCT probe positioning.The system can follow human head motion in real-time and track the human eye for autonomous ophthalmology OCT exam.Huang et al. presented an R-OCT 37 that scans large tissue and generates wide FOV 3D OCT volume via stitching spatially connected small FOV volumes which were acquired by a 3D OCT probe at multiple predefined locations.Similar volume stitching principle was then applied in other R-OCT systems by He et al. for in vivo whole mouse brain OCT angiography 38 and Göb et al. for large area skin imaging 39 .
However, the volume stitching strategy adopted in most previous R-OCT systems has a practical challenge in clinical kidney imaging.Due to the tradeoff between the OCT's lateral resolution and confocal parameter (depth of field) 26 , when the sample surface is highly uneven (such as in the case of a human kidney), even a small FOV volume is likely to contain suboptimal-quality B-scans where the sample is beyond the depth of field.Besides, to the best of our knowledge, an OCT system with extended FOV that can cover the size of a regular human kidney (~100 mm x 50 mm) with high resolution to resolve renal microstructures has not been reported in the literature.
Herein, we introduce a different solution to the volume stitching based R-OCT systems to specifically address large area OCT imaging for transplant kidney evaluation.With the established real-time communication between the OCT system and the robot controller, the proposed R-OCT system can continuously acquire 2D OCT images and dynamically keeps the scanning sample within the depth of field by optimizing the OCT probe's pose per B-scan.This feature guarantees consistent quality of the Bscans, allowing the system to handle organs with steep surface.Furthermore, our system provides high flexibility in the scanning time since the rate of the robot motion is largely adjustable.Such flexibility allows for quick coverage of the tissue, hence is clinically valuable.The system's imaging accuracy and the ability to scan the whole kidney are validated on customized phantom and kidney samples ex vivo.Finally, we demonstrate large area kidney parameter mappings showing clinically relevant tissue heterogeneity.

R-OCT for Large Area Imaging
Our R-OCT system 40 consists of a seven DoF robotic arm (Panda, Franka Emika, Germany) and a customized end-effector which houses a compact OCT probe (OCTG13 Telesto, Thorlabs, USA).The OCT system was configured to provide a lateral resolution of 2.73 m per pixel and axial resolution of 2.68 m per pixel, respectively.Two workstations, communicating through wireless network, are used for robot motion control and OCT data acquisition respectively.During imaging, the robot performs multiple straight-line motion with predefined start and end positions with respect to the robot base frame, {  } (referred to as scanlines) to cover the entire sample (see Fig. 1b).A fixed distance offset is applied to consecutive scanlines such that the OCT images are overlapped in the lateral direction.While traveling through a scanline, OCT images are streamed and recorded at 20 frames-per-second (fps) with an image size of 1800 pixels (lateral) by 700 pixels (axial).Based on the depth of automatically detected tissue surface in the B-scans, the altitude of the probe is adjusted in real-time to compensate for the uneven surface so that the image quality is consistent.After the scan, all collected OCT images and their corresponding probe poses are archived.To enable intuitive interpretation of the large area imaging outcomes, a series of spatially-resolved parameterization methods are developed to extract anatomical information from the spatially localized OCT images.These visualization methods include: i) depthencoded map (DEPM) for capturing the surface curvature of the sample; ii) axial-attenuation mapping (ATCM) for revealing microstructures beneath the tissue surface; and iii) PCT lumen diameter mapping (DIAM) for extracting and quantifying specific clinically relevant information.As will be discussed in the following content, we mainly used DEPM for validating our R-OCT system's probe localization accuracy.On the other hand, the ATCM and DIAM are used to generate large area parameterizations revealing the anatomical structure under the tissue surface for potential pathological analysis.

Imaging 3D Printed Phantom
The R-OCT system's probe localization accuracy plays a vital role in enabling large area spatiallyresolved OCT scan.To verify the localization accuracy, a specifically designed phantom, fabricated through 3D printing (48 mm by 20 mm) was imaged by the R-OCT system.As shown in Fig. 2a, the phantom has eight extrusions in the form of English letters.Three scanlines were pre-programmed to achieve complete coverage of the phantom (1.5 mm lateral overlap between consecutive scanlines).During the imaging process, the robot traveled continuously at a speed of 0.35 mm s -1 along each scanline, resulting in a total imaging time of 470 seconds (including the time to store the OCT images).The distance between the OCT probe and the phantom surface was automatically regulated by controlling the probe's vertical position to ensure optimal image quality (see "Materials and Methods").The effectiveness of the automatic probe adjustment can be observed through the consistent phantom surface position in the B-scans and the probe trajectory displayed in the sideview (see Fig. 2c and Fig. 2d, respectively).Individual B-scans were spatially aligned using the probe localization information for largearea visualization.We used DEPM of the detected phantom surface to visualize the texture on the phantom (see "Materials and Methods").The phantom DEPM, which depicts a reconstruction of the letters, is presented in Fig. 2b.The pixel resolution of the DEPM is 2.73 m per pixel in lateral and 21.0 m per pix in elevational direction.It can be shown that the R-OCT system successfully covers the entire footprint of the phantom, capturing all eight letter extrusions, which demonstrates its capability for large-area imaging.Such a wide scan area is unattainable with existing tabletop OCT imagers.Furthermore, minimum distortions were found in the shape of the letters, indicating a high degree of probe positioning accuracy.The probe localization accuracy was quantified by comparing the width and height of the letters measured from the phantom DEPM against the same measurements performed directly on the phantom with a caliper.The average errors in letter reconstruction were found to be 0.0762 ± 0.0727 mm in height and 0.1670 ± 0.0941 mm in width (see Fig. 2e).While the overall accuracy is acceptable, a student's t-test concluded that the error in the height of the reconstructed letters is significantly smaller than the error in the width of the letters (P = 0.021).This means that the DEPM has higher precision in the lateral direction than in the elevational direction (~0.1 mm difference).Further analysis on the heterogeneity of the probe tracking accuracy in different directions will be added in the "Discussion" section.

Imaging Ex vivo Porcine Kidney Sample
To illustrate the R-OCT system's capability of visualizing kidney microstructures, an ex vivo study was conducted using a porcine kidney sample with a dimension of 70.8 mm by 50.1 mm as the imaging target.To enhance the observation of well-known renal anatomy, including the renal cortex and medulla, the kidney sample was cut open with a scalpel (see Fig. 3a).The imaging procedure followed a similar protocol in the previous phantom experiment, covering an area of 44 mm by 21 mm.At the visualization step, instead of solely extracting the sample surface, spatially aligned B-scans were further parameterized using the extinction coefficient, calculated per vertical line (A-scans) in the B-scans.The utilization of extinction coefficient has been demonstrated to effectively differentiate tissue by their optical attenuation properties [41][42][43] , thereby facilitating the visualization of the kidney microstructures.Fig. 3c showcases several representative B-scans along with the corresponding extracted extinction coefficients.The ATCM parameterization technique compresses 2D B-scans into 1D vectors, enabling large-area 2D ATCM visualization (see "Materials and Methods" section).Fig. 3b.shows the porcine kidney ATCM with pixel resolution of 2.73 m per pixel in lateral and 29.7 m per pixel in elevational direction.The ATCM reveals the presence of radially distributed straight lines, particularly in the periphery of the ATCM (indicated by the arrows).Similar landmarks can be seen in the photograph of the sample in Fig. 3a.These landmarks can be corresponded to characteristic anatomical features of renal medulla 44 , as the renal medulla consists of a series of renal pyramids that exhibits striations due to the presence of straight tubular structures and blood vessels.This finding proves that the ATCM is able to show kidney morphological features under the surface.

Imaging Ex vivo Human Kidney Sample
In kidney transplantation, the donor kidney needs to be well-protected and examined in situ.Therefore, unlike the open-cut porcine kidney experiment, the imaging of the human kidney was restricted to the organ's surface without damaging the tissue.To validate the R-OCT system under a realistic situation, an intact ex vivo human kidney with dimensions of 106.39 mm by 37.70 mm was imaged by our system.The kidney underwent vascular perfusion to keep the key anatomies from collapse before being preserved in formalin (see "Materials and Methods").During imaging, only the surface of the kidney was exposed to air while the bottom portion remained submerged in formalin (see bottom left in Fig. 4b).Ten scanlines were employed to ensure comprehensive coverage of the kidney (3 mm lateral overlap between consecutive scanlines).Fig. 4a illustrates three representative B-scans along with their corresponding extinction coefficient vectors, revealing the presence of multiple identifiable anatomical landmarks, including the renal capsule, the capsular perforating radiate arteries 44 , and the PCT lumen.The perforating radiate arteries are discernible circular structures exhibiting low extinction coefficient values.Overall, the scan covered an area of 123.61 mm by 53.41 mm, sufficiently covering the footprint of the kidney sample.The whole kidney ATCM (2.73 m per pixel lateral resolution and 29.7 m per pixel elevational resolution, extra areas were cropped), which depicts vessel-shaped microstructures at multiple places on the kidney surface was generated in Fig. 4b.Four regions of interest (ROI) within the whole kidney ATCM were magnified to enable detailed visualization of the fine vasculatures (Fig. 4c).These vasculatures are identified as the perforating arteries located within the renal capsule due to their diameter of around 0.15 mm.Note that the data to produce the ATCM in Fig. 4b was collected with the robot traveling at 0.6 mm s -1 in the elevational direction, resulting in a total imaging time of 2700 seconds (including data storage time).In clinical practice, however, this time is expected to be minimized.To investigate the effect of faster robot scanning speed on the ATCM, an additional study was conducted where the ATCM was downsampled at different intervals to simulate different robot scanning velocities.The intervals were computed given a robot velocity range from 0.6 mm s -1 to 5.1 mm s -1 with an increment of 0.5 mm s -1 , while maintaining a fixed B-scan acquisition rate (20 fps).The maximum speed of 5.1 mm s -1 was selected to accelerate the scan process by 8.5 times in theory, compressing entire procedure to 317 seconds, i.e., 5 min.In practice, this time is expected to be further shortened as the data storage will also be significantly accelerated (scanning at 0.6 mm s -1 results in 40,920 B-scans in total.This number is expected to be reduced to 4,814 when scanning at 5.1 mm s -1 ).Presumably, the ATCM would suffer from distortions when the robot traveled at higher speeds.The distortions can be quantified by the reduction in the image quality of ATCM, which is computed using the structure similarity metric 45 between the downsampled ATCM and the original ATCM (normalized).The degradation in the image quality as the robot travels faster can be observed in Fig. 4d.It can be summarized from Fig. 4e.that the degradation exhibited an exponential trend as the robot's speed increased, reaching a plateau around 0.25 which corresponds to a speed of 3.6 mm s -1 .
Though the whole kidney ATCM can effectively show renal vasculatures within the renal capsule, its sensitivity to PCT lumens with diameters less than ~30 m is limited.However, our previous work has indicated a high correlation between the morphology of the PCT lumens and the occurrence of the delayed graft function (DGF) 22 .As DGF is a critical factor affecting the kidney transplant outcomes, it is necessary to detect the PCT lumens explicitly in B-scans and generate comprehensive parameter maps for lumen diameter to provide direct quantitative organ assessment.For this purpose, we employed a deep learning based semantic segmentation model 46 for automatic PCT lumen detection per B-scan.Fig. 5c illustrates a representative B-scan overlaid with binary lumen segmentation mask.By spatially aligning these segmentation masks, a large area 3D volume containing segmented PCT lumen can be constructed (white clusters in Fig. 5d).The DIAM was then generated subsequently (see "Materials and Methods").The colormap overlay in Fig. 5d represents the DIAM for two illustrative ROIs whose locations on the kidney sample were labeled in Fig. 5a.The average lumen diameter for the ROIs were measured to be 21.28 ± 19.75 m for ROI (1) and 23.29 ± 17.88 m for ROI (2) from the DIAM.The relatively larger standard deviation in the OCT measurements may be stemmed from errors in the automatic lumen segmentation across a large number of frames (300 slices per ROI).To further validate the diameter values, we extracted tissue from these two ROIs by slicing from roughly the same plane for OCT imaging to perform post-scan histopathological analysis.The microscopic pictures for the tissue samples were shown in Fig. 5b (one picture per ROI).The tubule structures were manually labeled with green mask whose average diameters were measured to be 22.38 ± 4.56 m for ROI (1) and 20.67 ± 4.87 m for ROI (2).The average diameter values show a close match across the two modalities.
To examine the change in the mask volume and DIAM, the same robot velocity sweep experiment was conducted.Fig. 5e highlights the change of the top-viewed mask volume as the scan speed increased.Similarly, the distortion in these maps was evaluated using the image quality measure.As shown in Fig. 5f, the mask volume deteriorated rapidly at a higher scan speed.However, little change was captured in the DIAM, showing the level of robustness of these two visualization methods when the elevational imaging resolution changes.

Discussion
In summary, we have developed an R-OCT system that performs large sample imaging for pretransplant kidney quality assessment.We showcased the system's ability to achieve large FOV imaging which is nonviable with existing OCT imaging techniques.The reconstruction of the phantom texture using DEPM showcased the system's sub-millimeter level probe localization accuracy, as well as the probe altitude adjustment ability to deal with the steepness of the sample surface.The whole kidney comprehensive scan shows the potential to apply the R-OCT system in a real application scenario.Furthermore, the large area parameter map offers intuitive visualization on the anatomies under the kidney surface.The porcine kidney ATCM reveals microstructures that can be correlated with the sample photography.The DIAM for the human kidney shows lumen diameter values similar to the numbers reported in the literature 22 .The mean diameter of the lumen structure measured under OCT shows a close match with those measured under microscopic pictures for both ROI (1) and ROI (2).These results demonstrated the feasibility of using the presented large area parameter maps to extract clinically relevant information.
Compared to handheld OCT probes, our R-OCT system shows distinct superiorities in terms of providing stable scan motion and precise probe localization.Unlike most previous R-OCT systems presented in the literature that stitches small FOV OCT volumes to achieve wider area scan, our system features synchronized acquisition of OCT B-scans and their corresponding OCT probe poses.This feature enables us to optimize the probe altitude based on per-sectional imaging feedback in real-time.Therefore, our system can better adapt to tissues with extremely steep surfaces such as a human kidney (the altitude difference in our human kidney sample reached 23.6 mm, way beyond typical OCT penetration depth of ~2 mm).In addition, it eliminates the need for prior information about the sample geometry in order to determine a substantial number of volume acquisition points (our system only requires specifying two points in elevational and lateral directions as the start and the end to generate a scanline).Nor does the system need to orthogonally reorient the probe towards the tissue according to an initial rough volume acuquisition 37 , As a result, the overall imaging workflow becomes more efficient and less sensitive to inaccurately defined trajectory.
However, one drawback associated with the 2D image-based acquisition is that spatial alignment of the OCT images may suffer from lower accuracy in the elevational direction than in the lateral direction, which can be seen from the letter reconstruction result in the phantom imaging experiment.This discrepancy arises because elevational reconstruction accuracy relies solely on the recorded OCT probe pose measured by robot joint motor encoders.In contrast, the reconstruction in the lateral direction depends significantly less on the probe pose measurements but more on the lateral FOV of the B-scans, which inherently provides higher accuracy.To mitigate this issue, scanning the sample bi-directionally (i.e., generating scanlines in orthogonal directions) could be employed, albeit at the cost of prolonging the imaging procedure.
Nevertheless, an unneglectable advantage of our system is the flexibility to adjust the elevational imaging resolution along the scanline direction by altering the robot scan speed.Such flexibility can be utilized by the clinicians to perform quick scans on the organ to receive a general impression on the organ healthiness.Although fewer images can be acquired at a higher scan speed, leading to less elevational pixel resolution and potentially distorted anatomies (see Fig. 4c, Fig. 5e), the DIAM is shown to be minimally affected by the increased scan speed (Fig. 5f).Therefore, they are ideal visualization tools that can be leveraged to provide an overall assessment of the organ.Next, several smaller regions showing suspicious lesion or clinically interested landmarks can be further inspected with dense imaging at a lower scan speed.Such workflow has been implemented in other imaging tasks such as breast ultrasound examination 47 .
For future directions, the software pipeline for OCT data acquisition and OCT-to-robot communication will be further optimized to reach faster B-scan sampling rate, such that the imaging time can be shortened and higher elevational pixel resolution can be achieved.In addition, we will work on establishing an imaging protocol to integrate the current system into the clinical workflow for pretransplant kidney monitoring by maximally utilizing the scan time flexibility of the presented R-OCT system.The protocol is expected to detail the optimal robot scanning speed, overlapped area among scanlines, the sterilization workflow, etc. to make the overall imaging time and outcome meet the clinical expectations.Owing to the limited number of accessible kidney samples, only one human kidney was scanned.However, being able to differentiate diverging quality kidneys with large area OCT based scoring is important to confirm the clinical significance of the parameter maps (ATCM and DIAM).To this end, an increased number of samples involving both high-and low-quality kidneys will be imaged by our R-OCT and compared against histopathological ground truth.
Although the R-OCT system is mainly demonstrated for kidney imaging, we envision such a platform is generalizable to other anatomies such as skin 39 and brain 48 , as well as other applications such as cancer imaging (ATCM has been used in differentiating tumor from normal tissues 49 ).Alternative tissue analysis can be conducted using, for instance, deep-learning based reasoning for tumor diagnosis procedure 50 .It is also worth noting that the robotic arm provides superior dexterity and accuracy in positioning the OCT probe compared to hand-held 32 and translational stage solutions 34 .In the presented experiments, we mainly employed pure translational scan motion to ensure consistent optical attenuation across the tissue for ATCM generation.Nevertheless, the robot could also rotate the probe orthogonally with respect to the sample surface in real-time to prevent defocusing.Such dexterity suggests our platform can be potentially used intraoperatively for surgical navigation 51,52 by providing real-time, multi-location tissue tomography.

OCT and Robotic System Integration
The Spectral Domain OCT (SD-OCT) system used in this paper (1300 nm central wavelength) has been previously used for similar kidney monitoring applications 22 .The robotic arm was designed for applications involving human interaction, with a repeated motion accuracy of 0.1 mm.Its seven DoF allows more generalized use case of the platform (see "Discussion").Two desktop workstations were used for robot motion control (referred to as PC1) and OCT image acquisition (referred to as PC2).Realtime communication between PC1 and PC2 was facilitated by the Robot Operating System (ROS), allowing the utilization of OCT images as feedback for the adjustment of probe altitude.The calibration of the rigid body transformation    ∈ (3), which represents the transformation from the robot base frame {  } to the OCT probe frame {  }, was obtained based on the R-OCT CAD model.The robot was controlled via sending Cartesian velocity commands of {  } relative to {  } at a rate of 1,000 fps using the Franka Control Interface (FCI) programmed in C++ language.The OCT images were streamed to PC2 using the Spectral Radar's (Thorlabs, USA) MATLAB (MathWorks, USA) SDK for data storage and post-processing, i.e., generating the parameter maps.

R-OCT Large Area Scan Procedure
With the sample positioned at a designated region within the sample tray, the robot initiates its operation from a predefined home configuration.To achieve full-coverage of the sample, a set of pre-coded scanlines is utilized.These scanlines are distributed along the y-axis of {  } and points to the x-axis direction; each scanline begins at the same starting point in the x-axis and z-axis directions of {  } with a consistent length.Formally, they can be characterized by a set of parameters: {  ,   ,   , , ,   }, where {  ,   ,   } ∈ ℝ 3×1 represents the starting coordinate under {  } for the first scanline;  ∈ ℝ denotes the length for all scanlines (i.e., the distance to be covered in the x-axis of {  });  ∈ ℝ is the distance to be covered in the y-axis direction;   ∈ ℝ is the desired overlap between consecutive B-scans acquired from adjacent scanlines.The total number of scanlines, denoted as , can be calculated as follows: where   ∈ ℝ is the lateral FOV of the OCT probe.The process of traversing through the -th ( ∈ [1, ]) scanline comprises three steps.
Step1.At initial time  0 , the robot first positions the probe at an entry pose,    [ 0 ], which expanded to: At   , the probe is sufficiently distant from the sample in the vertical direction, such that the sample does not appear in the OCT image.
Step2.Without altering the orientation of the probe with respect to {  }, a landing motion is executed, moving the probe vertically towards the sample at a constant velocity along the z-axis of {  }.When the OCT probe approaches the sample closely enough, the surface of the sample will become detectable in the OCT image with straightforward intensity thresholding technic.The position of the sample inside the image is quantified by the normalized surface depth (NSD), μ, defined as: where H OCT is the axial FOV of the OCT image; h tis corresponds to the depth of the highest point on the detected tissue surface in the B-scan (depicted in Fig. 1c).The sample surface should be positioned reasonably close to the probe to maintain focus yet allowing some distance to avoid the tissue from exceeding the OCT axial FOV or attaching the probe tip.To this end, a threshold NSD (μ = 0.75) is empirically determined as the termination condition for the landing motion, i.e., when μ ≥ 0.75.
Step3.The robot moves the probe at a constant speed,   , along the scanline (in x-axis), capturing Bscans in real-time while recording the probe pose at every time stamp.Throughout the imaging process, the depth of the sample surface in the B-scans (i.e., μ) is regulated via adjusting the probe altitude to ensure consistent image quality.Details regarding the surface depth regulation, referred to as NSD regulation, will be discussed in the next subsection.The robot halts once the probe has traveled a distance of , indicating the completion of one scanline.The probe is then repositioned at the starting point of the next scanline and the same process will be repeated until the last scanline is completed.

Online Probe Altitude Adjustment
As mentioned above, the NSD regulation is employed using a proportional feedback controller that outputs linear velocity along the z-axis of {  } based on the current OCT image.At timestamp , the probe velocity along the z-axis of {  },   [], is given by: where   ∈ [0, 1] is the low-pass filter weight to avoid velocity jittering;   is the velocity gain;  ̃ is the desired NSD (empirically set to 0.75); μ is the NSD computed from the current image; With the velocity controller, the tissue will be maintained at a fixed depth, which can be verified by the example B-scans shown in Fig. 2c, Fig. 3c and Fig. 4a.

Large Area 2D Parameter Map Generation
Here we explain the process to generate post-scan large area parameterized visualization, including DEPM, ATCM and DIAM using the spatially tracked OCT images.For each scanline, the process can be divided into two steps, namely, step 1, which entails generating a large area volume using localized OCT images; and step 2, which involves parameterizing 2D slices of the volume and reprojecting the parameterized volume for 2D visualization.
Step 1 further comprises two sub-steps: i) all OCT images are transformed into a common static coordinate frame ({  }), and ii) the voxelization of the images.The pose of the -th OCT image relative to the robot base, collected at timestamp , is denoted as    [  ].For each pixel in the image, its coordinate   ∈ ℝ 3 under {  } can be transformed to {  } through the equation below (coordinates are augmented for homogeneous matrix multiplication): where   represents the coordinate under {  };   ∈ ℝ 3 is the pixel resolution (pixel per mm).
Applying this operation to all the images yields the spatially aligned OCT images.Second, these images are voxelized by discretizing the pixel coordinates under {  }, resulting in a large area 3D tissue volume, noted as   .
Step 2 involves varying parameterization techniques depending on the specific visualization purposes.For DEPM generation, the depth of the sample surface in each slice of   is extracted using pixel intensity thresholding and projected from the top view (x-y plane of {  }).
In the case of ATCM, each A-scan in every slice of   is parameterized using the extinction coefficient (  ).The value of   is derived by fitting the single scattering model 41 to the A-scan within a given depth window.The single scattering model is written as: where () is the pixel intensity at the imaging depth  in the depth window.The depth window is limited to close to the sample surface (~ 1mm) because most meaningful anatomical features only appear nearsurface.Example intensity fitting results can be found in Fig. 3d.Lastly, the ATCM can be formed by spatially aligning   .
The DIAM generation requires first segmenting the PCT lumens from   to construct a binary mask volume.To accomplish this, we begin by performing contrast enhancement 53 for each slice of   before feeding them into a Residual-Attention-UNET model previously developed 46 to obtain segmentation masks for PCT lumen.This model, pretrained on 14,403 OCT images, achieved an accuracy of 0.82 in terms of Dice index and 0.85 in terms of Intersection over Union (IoU) metric.The resulting mask volume then undergoes a series of slice-by-slice morphological reconstructions (i.e., area closing followed by area opening operation) to ensure the shape consistency of the lumen segmentation mask.The output binary volume,   , is viewed from the top perspective.Next, we calculate the lumen diameter for each slice of   using the centerline algorithm 32 .The top-view projection of   is then downsampled into -by- grids.Within each grid, the diameter of the segmented lumens that fall inside are averaged.Finally, the -by- grid map is upsampled to its original size via bi-cubic interpolation.In our case,  is empirically assigned to be 10.
After obtaining the parameter map from each scanline, the parameter maps are stitched together using the recorded probe poses.Parameter maps from adjacent scanlines are averaged in the overlapped area to form a large area parameter map with smooth transition between scanlines.

Kidney Sample Preparation and Post-scan Histopathology
The fresh porcine kidney sample was obtained from a local slaughterhouse.The experiment-used human kidney sample was obtained from a deceased donor who did not meet the transplant standard.It was preserved by hypothermic machine perfusion (HMP) for keeping the kidney sample's physiological status and then fixed by 10% formalin within 2 days after removing from the donors.This study was approved by the University of Oklahoma and the University of Oklahoma Health Sciences Center Institutional Review Board (IRB) (Study number: IRB #12462).
The post-scan histopathology was performed at the University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA.The sliced kidney samples were processed with H&E staining and imaged with a pre-calibrated microscope with 10X magnification.

Fig. 1
Fig. 1 The Robot OCT (R-OCT) system for large area imaging.a R-OCT system layout and coordinate system definition.{  } is the robot arm's base coordinate frame; {  } is the OCT probe coordinate frame, originated at the top center of the OCT Bscan.b Zoomed-in views of the sample placement region.A kidney sample to be imaged lies inside the sample tray.Yellow arrows represent scanlines.An example B-scan is rendered along the scanlines (top).c OCT probe adjustment scheme using realtime B-scans as control feedback.d post-processing steps after the large area scan completion.   is the homogeneous coordinate transformation from {  } to {  }.    [ 1 ] is the transformation recorded from the robot pose measurement at timestamp  1 ;    [ 2 ] is the transformation recorded at  2 along an adjacent scanline.The OCT B-scans are first spatially localized (left), then parameterized and reprojected for large-area 2D visualization (right).

Fig. 2
Fig. 2 Imaging 3D printed phantom.a Picture of the 3D printed phantom.The phantom has eight letter-shape extrusions (W, P, I, U, M, A, S, S). b reconstructed DEPM of the 3D printed phantom overlaid by the OCT probe scan trajectory.c Representative OCT B-scans from imaging the phantom.The location of these images relative to the sample can be found in b. d The OCT probe scan trajectory viewed from the axial-elevational plane.e Box plot of the errors in restoring the height and width of the letters in DEPM.N=8 in each category.

Fig. 3
Fig. 3 Imaging ex-vivo porcine kidney.a Picture of the porcine kidney sample.b ATCM reconstruction of the kidney sample.c Representative OCT B-scans from imaging the kidney and the per-column (A-scan) extinction coefficient values for each image.The location of these images relative to the sample can be found in b. d center column A-scan of the images in c (marked by white dotted lines).

Fig. 4
Fig. 4 Imaging ex-vivo human kidney.a Representative B-scans acquired from the human kidney sample, along with the per-Ascan extinction coefficient values for each image.b The whole kidney ATCM and a picture of the kidney sample (bottom left).c Four regions of interest (ROI 1-4) zoomed in from c. d ROI 1 downsampled in elevation direction at different intervals to simulate altered robot scan velocities.e Simulated ATCM reconstruction quality change caused by altering the robot scan speed to N=10 different values.

Fig. 5
Fig. 5 Large area parameterization of the ex vivo human kidney.a Picture of the human kidney sample.Two representative ROIs, labeled as (1) and (2) were used to generate DIAM (each ROI is ~ 8 mm by 8 mm).b Post-scan histopathology of the two ROIs.Green masks are the manually labeled tubule structures identified in the microscopic picture.c Representative PCT lumen segmentation in a B-scan acquired from the human kidney sample.The green masks the detected PCT. d Segmented PCT lumen in binary mask, viewed from the top, overlaid by the DIAM for ROI (1) and (2).e PCT lumen mask in ROI (2) downsampled in elevation direction at different intervals to simulate the altered robot scan velocities.f Simulated lumen mask and DIAM quality change caused by altering the robot scan speed to N=10 different values.