Fully automated analysis of OCT imaging of human kidneys for prediction of post-transplant function

X-Ray-Based 3D Histopathology of the Kidney Using Cryogenic Contrast-Enhanced MicroCT

The kidney's microstructure, which comprises a highly convoluted tubular and vascular network, can only be partially revealed using classical 2D histology. Considering that the kidney's microstructure is closely related to its function and is often affected by pathologies, there is a need for powerful and high-resolution 3D imaging techniques to visualize the microstructure. Here, we present how cryogenic contrast-enhanced microCT (cryo-CECT) allowed 3D visualization of glomeruli, tubuli, and vasculature. By comparing different contrast-enhancing staining agents and freezing protocols, we found that the preferred sample preparation protocol was the combination of staining with 1:2 hafnium(IV)-substituted Wells-Dawson polyoxometalate and freezing by submersion in isopentane at -78°C. This optimized protocol showed to be highly sensitive, allowing to detect small pathology-induced microstructural changes in a mouse model of mild trauma-related acute kidney injury after thorax trauma and hemorrhagic shock. In summary, we demonstrated that cryo-CECT is an effective 3D histopathological tool that allows to enhance our understanding of kidney tissue microstructure and their related function.

Radiomics in Kidney Transplantation: A Scoping Review of Current Applications, Limitations, and Future Directions

Radiomics is increasingly applied to the diagnosis, management, and outcome prediction of various urological conditions. The purpose of this scoping review is to evaluate the current evidence of the application of radiomics in kidney transplantation, especially its utility in diagnostics and therapeutics. An electronic literature search on radiomics in the setting of transplantation was conducted on PubMed, EMBASE, and Scopus from inception to September 23, 2022. A total of 16 studies were included. The most widely studied clinical utility of radiomics in kidney transplantation is its use as an adjunct to diagnose rejection, potentially reducing the need for unnecessary biopsies or guiding decisions for earlier biopsies to optimize graft survival. Technology such as optical coherence tomography is a noninvasive procedure to build high-resolution optical cross-section images of the kidney cortex in situ and in real time, which can provide histopathological information of donor kidney candidates for transplantation, and to predict posttransplant function. This review shows that, although radiomics in kidney transplants is still in its infancy, it has the potential for large-scale implementation. Its greatest potential lies in the correlation with conventional established diagnostic evaluation for living donors and potential in predicting and detecting rejection postoperatively.

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.

Automated robot-assisted wide-field optical coherence tomography using structured light camera

Optical coherence tomography (OCT) is a promising real-time and non-invasive imaging technology widely utilized in biomedical and material inspection domains. However, limited field of view (FOV) in conventional OCT systems hampers their broader applicability. Here, we propose an automated system integrating a structured light camera and robotic arm for large-area OCT scanning. The system precisely detects tissue contours, automates scan path generation, and enables accurate scanning of expansive sample areas. The proposed system consists of a robotic arm, a three-dimensional (3D) structured light camera, and a customized portable OCT probe. The 3D structured light camera is employed to generate a precise 3D point cloud of the sample surface, enabling automatic planning of the scanning path for the robotic arm. Meanwhile, the OCT probe is mounted on the robotic arm, facilitating scanning of the sample along the predetermined path. Continuous OCT B-scans are acquired during the scanning process, facilitating the generation of high-resolution and large-area 3D OCT reconstructions of the sample. We conducted position error tests and presented examples of 3D macroscopic imaging of different samples, such as ex vivo kidney, skin and leaf blade. The robotic arm can accurately reach the planned positions with an average absolute error of approximately 0.16 mm. The findings demonstrate that the proposed system enables the acquisition of 3D OCT images covering an area exceeding 20 cm2, indicating wide-ranging potential for utilization in diverse domains such as biomedical, industrial, and agricultural fields.

In vivo imaging of renal microvasculature in a murine ischemia-reperfusion injury model using optical coherence tomography angiography

Optical coherence tomography angiography (OCTA) provides three-dimensional structural and semiquantitative imaging of microvasculature in vivo. We developed an OCTA imaging protocol for a murine kidney ischemia-reperfusion injury (IRI) model to investigate the correlation between renal microvascular changes and ischemic damage. Mice were divided into mild and moderate IRI groups according to the duration of ischemia (10 and 35 mins, respectively). Each animal was imaged at baseline; during ischemia; and at 1, 15, 30, 45, and 60 mins after ischemia. Amplitude decorrelation OCTA images were constructed with 1.5-, 3.0-, and 5.8-ms interscan times, to calculate the semiquantitative flow index in the superficial (50-70 μm) and the deep (220-340 μm) capillaries of the renal cortex. The mild IRI group showed no significant flow index change in both the superfial and the deep layers. The moderate IRI group showed a significantly decreased flow index from 15 and 45 mins in the superficial and deep layers, respectively. Seven weeks after IRI induction, the moderate IRI group showed lower kidney function and higher collagen deposition than the mild IRI group. OCTA imaging of the murine IRI model revealed changes in superficial blood flow after ischemic injury. A more pronounced decrease in superficial blood flow than in deep blood flow was associated with sustained dysfunction after IRI. Further investigation on post-IRI renal microvascular response using OCTA may improve our understanding of the relationship between the degree of ischemic insult and kidney function.

Feasibility of the soft attention-based models for automatic segmentation of OCT kidney images

Clinically, optical coherence tomography (OCT) has been utilized to obtain the images of the kidney's proximal convoluted tubules (PCTs), which can be used to quantify the morphometric parameters such as tubular density and diameter. Such parameters are useful for evaluating the status of the donor kidney for transplant. Quantifying PCTs from OCT images by human readers is a time-consuming and tedious process. Despite the fact that conventional deep learning models such as conventional neural networks (CNNs) have achieved great success in the automatic segmentation of kidney OCT images, gaps remain regarding the segmentation accuracy and reliability. Attention-based deep learning model has benefits over regular CNNs as it is intended to focus on the relevant part of the image and extract features for those regions. This paper aims at developing an Attention-based UNET model for automatic image analysis, pattern recognition, and segmentation of kidney OCT images. We evaluated five methods including the Residual-Attention-UNET, Attention-UNET, standard UNET, Residual UNET, and fully convolutional neural network using 14403 OCT images from 169 transplant kidneys for training and testing. Our results show that Residual-Attention-UNET outperformed the other four methods in segmentation by showing the highest values of all the six metrics including dice score (0.81 ± 0.01), intersection over union (IOU, 0.83 ± 0.02), specificity (0.84 ± 0.02), recall (0.82 ± 0.03), precision (0.81 ± 0.01), and accuracy (0.98 ± 0.08). Our results also show that the performance of the Residual-Attention-UNET is equivalent to the human manual segmentation (dice score = 0.84 ± 0.05). Residual-Attention-UNET and Attention-UNET also demonstrated good performance when trained on a small dataset (3456 images) whereas the performance of the other three methods dropped dramatically. In conclusion, our results suggested that the soft Attention-based models and specifically Residual-Attention-UNET are powerful and reliable methods for tubule lumen identification and segmentation and can help clinical evaluation of transplant kidney viability as fast and accurate as possible.

Indoor Localization of Hand-Held OCT Probe Using Visual Odometry and Real-Time Segmentation Using Deep Learning

OBJECTIVE

Optical coherence tomography (OCT) is an established medical imaging modality that has found widespread use due to its ability to visualize tissue structures at a high resolution. Currently, OCT hand-held imaging probes lack positional information, making it difficult or even impossible to link a specific image to the location it was originally obtained. In this study, we propose a camera-based localization method to track and record the scanner position in real-time, as well as providing a deep learning-based segmentation method.

METHODS

We used camera-based visual odometry (VO) and simultaneous mapping and localization (SLAM) to compute and visualize the location of a hand-held OCT imaging probe. A deep convolutional neural network (CNN) was used for kidney tubule lumens segmentation.

RESULTS

The mean absolute error (MAE) and the standard deviation (STD) for 1D translation were found to be 0.15 mm and 0.26mm respectively. For 2D translation, the MAE and STD were found to be 0.85 mm and 0.50 mm, respectively. The dice coefficient of the segmentation method was 0.7. The t-statistic of the T-test between predicted and actual average densities and predicted and actual average diameters were 7.7547e-13 and 2.2288e-15 respectively. We also experimented on a preserved kidney utilizing our localization method with automatic segmentation. Comparisons of the average density maps and average diameter maps were made between the 3D comprehensive scan and VO system scan.

CONCLUSION

Our results demonstrate that VO can track the probe location at high accuracy, and provides a user-friendly visualization tool to review OCT 2D images in 3D space. It also indicates that deep learning can provide high accuracy and high speed for segmentation.

SIGNIFICANCE

The proposed methods can be potentially used to predict delayed graft function (DGF) in kidney transplantation.

Advances in Hypothermic and Normothermic Perfusion in Kidney Transplantation

Hypothermic and normothermic machine perfusion in kidney transplantation are purported to exert a beneficial effect on post-transplant outcomes compared to the traditionally used method of static cold storage. Kidney perfusion techniques provide a window for organ reconditioning and quality assessment. However, how best to deliver these preservation methods or improve organ quality has not yet been conclusively defined. This review summarises the promising advances in machine perfusion science in recent years, which have the potential to further improve early graft function and prolong graft survival.

Optical coherence tomography of small intestine allograft biopsies using a handheld surgical probe

SIGNIFICANCE

The current gold standard for monitoring small intestinal transplant (IT) rejection is endoscopic visual assessment and biopsy of suspicious lesions; however, these lesions are only superficially visualized by endoscopy. Invasive biopsies provide a coarse sampling of tissue health without depicting the true presence and extent of any pathology. Optical coherence tomography (OCT) presents a potential alternative approach with significant advantages over traditional white-light endoscopy.

AIM

The aim of our investigation was to evaluate OCT performance in distinguishing clinically relevant morphological features associated with IT graft failure.

APPROACH

OCT was applied to evaluate the small bowel tissues of two rhesus macaques that had undergone IT of the ileum. The traditional assessment from routine histological observation was compared with OCT captured using a handheld surgical probe during the days post-transplant and subsequently was compared with histophaology.

RESULTS

The reported OCT system was capable of identifying major biological landmarks in healthy intestinal tissue. Following IT, one nonhuman primate (NHP) model suffered a severe graft ischemia, and the second NHP graft failed due to acute cellular rejection. OCT images show visual evidence of correspondence with histological signs of IT rejection.

CONCLUSIONS

Results suggest that OCT imaging has significant potential to reveal morphological changes associated with IT rejection and to improve patient outcomes overall.

Deep-learning-aided forward optical coherence tomography endoscope for percutaneous nephrostomy guidance

Percutaneous renal access is the critical initial step in many medical settings. In order to obtain the best surgical outcome with minimum patient morbidity, an improved method for access to the renal calyx is needed. In our study, we built a forward-view optical coherence tomography (OCT) endoscopic system for percutaneous nephrostomy (PCN) guidance. Porcine kidneys were imaged in our experiment to demonstrate the feasibility of the imaging system. Three tissue types of porcine kidneys (renal cortex, medulla, and calyx) can be clearly distinguished due to the morphological and tissue differences from the OCT endoscopic images. To further improve the guidance efficacy and reduce the learning burden of the clinical doctors, a deep-learning-based computer aided diagnosis platform was developed to automatically classify the OCT images by the renal tissue types. Convolutional neural networks (CNN) were developed with labeled OCT images based on the ResNet34, MobileNetv2 and ResNet50 architectures. Nested cross-validation and testing was used to benchmark the classification performance with uncertainty quantification over 10 kidneys, which demonstrated robust performance over substantial biological variability among kidneys. ResNet50-based CNN models achieved an average classification accuracy of 82.6%±3.0%. The classification precisions were 79%±4% for cortex, 85%±6% for medulla, and 91%±5% for calyx and the classification recalls were 68%±11% for cortex, 91%±4% for medulla, and 89%±3% for calyx. Interpretation of the CNN predictions showed the discriminative characteristics in the OCT images of the three renal tissue types. The results validated the technical feasibility of using this novel imaging platform to automatically recognize the images of renal tissue structures ahead of the PCN needle in PCN surgery.

Toward image quality assessment in optical coherence tomography (OCT) of rat kidney

BACKGROUND

Optical coherence tomography (OCT) is a useful tool for the evaluation of structure and function of the kidney, but the image quality can be effected by many factors.

OBJECTIVE

The objective of this study was to assess the image quality of different OCT systems in OCT imaging of the living kidney.

METHODS

One swept-source OCT (SSOCT) of 1300 nm, one spectral domain OCT (SDOCT) of 1300 nm and another of 900 nm were used. A FeO phantom was used to establish the point spread function (PSF). Rat kidneys were imaged for image quality assessment. Light penetration in the kidney and the optical attenuation coefficient were also evaluated. The quantification of uriniferous tubules was carried out via the threshold segmentation of 3D OCT images.

RESULTS

The quality of kidney images was resolution dependent. SDOCT of 900 nm showed higher peak signal-to noise ratio and dynamic range. The spatial resolution in the light field could be derived from the PSF distribution along three mutually orthogonal axes. In conjunction with the PSF, the Lucy-Richardson algorithm could improve image quality but could not reveal more microstructural information. The penetration depth of 1300 nm was deeper than that of 900 nm. The attenuation coefficient of the kidney was 29 cm^-1 at 1300 nm and 50 cm^-1 at 900 nm (P < 0.001). More accurate measurement of uriniferous tubules was achieved with the SDOCT-900 due to its higher resolution.

CONCLUSIONS

Both SSOCT and SDOCT systems could be useful for imaging uriniferous tubules in the superficial layers of the cortex. The OCT image quality was highly correlated with the spatial resolution of OCT system.