An automatic method for renal cortex segmentation on CT images: evaluation on kidney donors

UNesT: Local spatial representation learning with hierarchical transformer for efficient medical segmentation

Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis. Transformer reformats the image into separate patches and realizes global communication via the self-attention mechanism. However, positional information between patches is hard to preserve in such 1D sequences, and loss of it can lead to sub-optimal performance when dealing with large amounts of heterogeneous tissues of various sizes in 3D medical image segmentation. Additionally, current methods are not robust and efficient for heavy-duty medical segmentation tasks such as predicting a large number of tissue classes or modeling globally inter-connected tissue structures. To address such challenges and inspired by the nested hierarchical structures in vision transformer, we proposed a novel 3D medical image segmentation method (UNesT), employing a simplified and faster-converging transformer encoder design that achieves local communication among spatially adjacent patch sequences by aggregating them hierarchically. We extensively validate our method on multiple challenging datasets, consisting of multiple modalities, anatomies, and a wide range of tissue classes, including 133 structures in the brain, 14 organs in the abdomen, 4 hierarchical components in the kidneys, inter-connected kidney tumors and brain tumors. We show that UNesT consistently achieves state-of-the-art performance and evaluate its generalizability and data efficiency. Particularly, the model achieves whole brain segmentation task complete ROI with 133 tissue classes in a single network, outperforming prior state-of-the-art method SLANT27 ensembled with 27 networks. Our model performance increases the mean DSC score of the publicly available Colin and CANDI dataset from 0.7264 to 0.7444 and from 0.6968 to 0.7025, respectively. Code, pre-trained models, and use case pipeline are available at: https://github.com/MASILab/UNesT.

Fat quantification in the sacroiliac joint syndesmosis: a new semi-automatic volumetric approach

Fat is physiologically embedded within the interosseous ligaments in the posterior part of the sacroiliac joint (PSIJ). This composite of fat and ligaments is hypothesized to serve a shock-absorbing, stabilizing function for the sacroiliac joint and the lumbopelvic transition region. Using a novel Python-based software (VolSEQ), total PSIJ volume and fat volume were computed semi-automatically. Differences within the cohort and the viability of the program for the quantification of fat in routine computed tomography (CT) scans were assessed. In 37 CT scans of heathy individuals, the PSIJ were first manually segmented as a region of interest in OSIRIX. Within VolSEQ, 'fat' Hounsfield units (- 150 to - 50 HU) are selected and the DICOM file of the patient scan and associated region of interest file from OSIRIX were imported and the pixel sub volumes were then automatically computed. Volume comparisons were made between sexes, sides and ages (≤ 30, 31-64 and > 65 years). PSIJ volumes in both software (VolSeq vs. OSIRIX) were non-different (both 9.7 ± 2.8cm3; p = 0.9). Total PSIJ volume (p = 0.3) and fat volume (p = 0.7) between sexes were non-different. A significant difference in total PSIJ volume between sexes (p < 0.01) but not in fat volume (p = 0.3) was found only in the ≥ 65 years cohort. Fat volume within the PSIJ remains unchanged throughout life. PSIJ volume is sex-dependent after 65 years. VolSEQ is a viable and user-friendly method for sub-volume quantification of tissues in CT.

Automated Segmentation of Kidney Cortex and Medulla in CT Images: A Multisite Evaluation Study

BACKGROUND

In kidney transplantation, a contrast CT scan is obtained in the donor candidate to detect subclinical pathology in the kidney. Recent work from the Aging Kidney Anatomy study has characterized kidney, cortex, and medulla volumes using a manual image-processing tool. However, this technique is time consuming and impractical for clinical care, and thus, these measurements are not obtained during donor evaluations. This study proposes a fully automated segmentation approach for measuring kidney, cortex, and medulla volumes.

METHODS

A total of 1930 contrast-enhanced CT exams with reference standard manual segmentations from one institution were used to develop the algorithm. A convolutional neural network model was trained (n=1238) and validated (n=306), and then evaluated in a hold-out test set of reference standard segmentations (n=386). After the initial evaluation, the algorithm was further tested on datasets originating from two external sites (n=1226).

RESULTS

The automated model was found to perform on par with manual segmentation, with errors similar to interobserver variability with manual segmentation. Compared with the reference standard, the automated approach achieved a Dice similarity metric of 0.94 (right cortex), 0.90 (right medulla), 0.94 (left cortex), and 0.90 (left medulla) in the test set. Similar performance was observed when the algorithm was applied on the two external datasets.

CONCLUSIONS

A fully automated approach for measuring cortex and medullary volumes in CT images of the kidneys has been established. This method may prove useful for a wide range of clinical applications.

Effect of ruptured cavitated bubble cluster on the extent of the cell deformation by ultrasound

In this paper, the bubble-cell model is presented. The effects of the spacing between the bubble population and the cell, the radius of the bubble and the bubble medium on the degree of cell deformation were investigated by solving the Helmholtz equation and the equilibrium of motion equation using COMSOL Multiphysis@ software. The ultrasonic transducer is applied in a round bottom flask with the bubble-cell model on the side of the ultrasonic transducer. When the distance between the bubble cluster and the cell gradually increases, the extent of deformation of the cell is reflected as first increasing and then decreasing, reaching the maximum deformation at D = 2. When the radius of the bubble is changed, there is a "constant frequency" at low frequency ultrasound in any distance case, at which the cell deformation will be violent. However, when the bubble medium is changed, there is no significant change in the degree of deformation of the cells. In other words, changes in the structure of the bubble-cell model affect the degree of cell deformation, but without structural changes, the degree of cell deformation changes very little.

Renal Cortex, Medulla and Pelvicaliceal System Segmentation on Arterial Phase CT Images with Random Patch-based Networks

Renal segmentation on contrast-enhanced computed tomography (CT) provides distinct spatial context and morphology. Current studies for renal segmentations are highly dependent on manual efforts, which are time-consuming and tedious. Hence, developing an automatic framework for the segmentation of renal cortex, medulla and pelvicalyceal system is an important quantitative assessment of renal morphometry. Recent innovations in deep methods have driven performance toward levels for which clinical translation is appealing. However, the segmentation of renal structures can be challenging due to the limited field-of-view (FOV) and variability among patients. In this paper, we propose a method to automatically label the renal cortex, the medulla and pelvicalyceal system. First, we retrieved 45 clinically-acquired deidentified arterial phase CT scans (45 patients, 90 kidneys) without diagnosis codes (ICD-9) involving kidney abnormalities. Second, an interpreter performed manual segmentation to pelvis, medulla and cortex slice-by-slice on all retrieved subjects under expert supervision. Finally, we proposed a patch-based deep neural networks to automatically segment renal structures. Compared to the automatic baseline algorithm (3D U-Net) and conventional hierarchical method (3D U-Net Hierarchy), our proposed method achieves improvement of 0.7968 to 0.6749 (3D U-Net), 0.7482 (3D U-Net Hierarchy) in terms of mean Dice scores across three classes (p-value < 0.001, paired t-tests between our method and 3D U-Net Hierarchy). In summary, the proposed algorithm provides a precise and efficient method for labeling renal structures.

Quantification of fat in the posterior sacroiliac joint region applying a semi-automated segmentation method

BACKGROUND AND OBJECTIVE

Fat within the posterior sacroiliac joint region (PSIJ) is thought to compensate for the incongruent surfaces of the sacrum and ilium posteriorly. Knowledge on the presence of fat in the SIJ could provide useful information about joint physiology and clinical kinematic implications of its presence. This study aimed at quantifying fat within the PSIJ, using a semi-automated method, and to compare the results to a manual segmentation method based on data from frozen cadaveric sections and computed tomography (CT). The results may provide a quicker and more objective method for fat volume quantification.

METHODS

Seventy-eight cadaveric hemipelves were used. Frozen sections were obtained and photographed and CT data obtained from subsamples. A MATLAB routine was deployed to assess fat in the serial sections and CT scans, using masks derived from color thresholds and Hounsfield units, respectively. Regions of interest were created to isolate the PSIJ region before fat volume was computed. A Friedman test was used for the comparison between all masks and the manual method, a Kruskall-Wallis test for comparing the CT results with all masks and the manual method and Bland-Altman plots were used to express the result differences of these methods.

RESULTS

PSIJ fat volume averaged 3.9 ± 2.2, 4.9 ± 2.5, 3.7 ± 2.3 and 7.2 ± 7.3 cm³ for masks 1 (fat mask), 2 (no-fat mask), 3 ('control' fat mask) and CT, respectively. All masks and the CT fat volume were significantly different to the manual segmentation method (p<0.01). Mask 2 differed significantly from masks 1 and 3 (both p<0.01). Bland-Altman plots yielded differences in the measurements between the various methods.

CONCLUSIONS

Manual segmentation of PSIJ fat volume may result in a relative underestimation of the total fat compared to semi-automated or CT-based methods, as fat might not be sufficiently distinguished from surrounding structures. However, the CT-based method resulted in vastly higher variation in the results and warrants further study. The semi-automated approach to quantify fat based on color thresholds presented here is more investigator-independent, time efficient and applicable to CT scans, which provides opportunity to use this technique on various tissue types in vivo.

Automated segmentation of the injured kidney due to abdominal trauma

The objective of this study is to propose and validate a computer-aided segmentation system which performs the automated segmentation of injured kidney in the presence of contusion, peri-, intra-, sub-capsular hematoma, laceration, active extravasation and urine leak due to abdominal trauma. In the present study, total multi-phase CT scans of thirty-seven cases were used; seventeen of them for the development of the method and twenty of them for the validation of the method. The proposed algorithm contains three steps: determination of the kidney mask using Circular Hough Transform, segmentation of the renal parenchyma of the kidney applying the symmetry property to the histogram, and estimation of the kidney volume. The results of the proposed method were compared using various metrics. The kidney quantification led to 92.3 ± 4.2% Dice coefficient, 92.8 ± 7.4%/92.3 ± 5.1% precision/sensitivity, 1.4 ± 0.6 mm/2.0 ± 1.0 mm average surface distance/root-mean-squared error for intact and 87.3 ± 8.4% Dice coefficient, 84.3 ± 13.8%/92.2 ± 3.8% precision/sensitivity and 2.4 ± 2.2 mm/4.0 ± 4.2 mm average surface distance/root-mean-squared error for injured kidneys. The segmentation of the injured kidney was satisfactorily performed in all cases. This method may lead to the automated detection of renal lesions due to abdominal trauma and estimate the intraperitoneal blood amount, which is vital for trauma patients.

CT evaluation of the renal donor and recipient

Proper pre- and post-transplant diagnostic imaging work-up is fundamental in ensuring a successful outcome for renal transplantation. Despite exposure to ionizing radiation, CT has high spatial resolution and is a widely available and fast imaging technique. CT is performed routinely to delineate the anatomy of the kidney, relevant vasculature, and urinary collecting system in the living donor, to assess the iliac vessels in potential recipients prior to surgery, and to assess early and late-term post-transplant complications. The purpose of this article is to outline the optimal CT protocol and the main reportable findings for both the donor and the recipient diagnostic imaging work-up as well as to point out the main issues regarding ionizing radiation exposure and contrast medium injection in these subjects.

A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation

Magnetic resonance imaging (MRI) is the non-invasive modality of choice for body tissue composition analysis due to its excellent soft-tissue contrast and lack of ionizing radiation. However, quantification of body composition requires an accurate segmentation of fat, muscle, and other tissues from MR images, which remains a challenging goal due to the intensity overlap between them. In this study, we propose a fully automated, data-driven image segmentation platform that addresses multiple difficulties in segmenting MR images such as varying inhomogeneity, non-standardness, and noise, while producing a high-quality definition of different tissues. In contrast to most approaches in the literature, we perform segmentation operation by combining three different MRI contrasts and a novel segmentation tool, which takes into account variability in the data. The proposed system, based on a novel affinity definition within the fuzzy connectivity image segmentation family, prevents the need for user intervention and reparametrization of the segmentation algorithms. In order to make the whole system fully automated, we adapt an affinity propagation clustering algorithm to roughly identify tissue regions and image background. We perform a thorough evaluation of the proposed algorithm's individual steps as well as comparison with several approaches from the literature for the main application of muscle/fat separation. Furthermore, whole-body tissue composition and brain tissue delineation were conducted to show the generalization ability of the proposed system. This new automated platform outperforms other state-of-the-art segmentation approaches both in accuracy and efficiency.

Kidney segmentation in ultrasound, magnetic resonance and computed tomography images: A systematic review

BACKGROUND AND OBJECTIVE

Segmentation is an essential step in computer-aided diagnosis and treatment planning of kidney diseases. In recent years, several researchers proposed multiple techniques to segment the kidney in medical images from distinct imaging acquisition systems, namely ultrasound, magnetic resonance, and computed tomography. This article aims to present a systematic review of the different methodologies developed for kidney segmentation.

METHODS

With this work, it is intended to analyze and categorize the different kidney segmentation algorithms, establishing a comparison between them and discussing the most appropriate methods for each modality. For that, articles published between 2010 and 2016 were analyzed. The search was performed in Scopus and Web of Science using the expressions "kidney segmentation" and "renal segmentation".

RESULTS

A total of 1528 articles were retrieved from the databases, and 95 articles were selected for this review. After analysis of the selected articles, the reviewed segmentation techniques were categorized according to their theoretical approach.

CONCLUSIONS

Based on the performed analysis, it was possible to identify segmentation approaches based on distinct image processing classes that can be used to accurately segment the kidney in images of different imaging modalities. Nevertheless, further research on kidney segmentation must be conducted to overcome the current drawbacks of the state-of-the-art methods. Moreover, a standardization of the evaluation database and metrics is needed to allow a direct comparison between methods.

3D marker-controlled watershed for kidney segmentation in clinical CT exams

Background

Image segmentation is an essential and non trivial task in computer vision and medical image analysis. Computed tomography (CT) is one of the most accessible medical examination techniques to visualize the interior of a patient’s body. Among different computer-aided diagnostic systems, the applications dedicated to kidney segmentation represent a relatively small group. In addition, literature solutions are verified on relatively small databases. The goal of this research is to develop a novel algorithm for fully automated kidney segmentation. This approach is designed for large database analysis including both physiological and pathological cases.

Methods

This study presents a 3D marker-controlled watershed transform developed and employed for fully automated CT kidney segmentation. The original and the most complex step in the current proposition is an automatic generation of 3D marker images. The final kidney segmentation step is an analysis of the labelled image obtained from marker-controlled watershed transform. It consists of morphological operations and shape analysis. The implementation is conducted in a MATLAB environment, Version 2017a, using i.a. Image Processing Toolbox. 170 clinical CT abdominal studies have been subjected to the analysis. The dataset includes normal as well as various pathological cases (agenesis, renal cysts, tumors, renal cell carcinoma, kidney cirrhosis, partial or radical nephrectomy, hematoma and nephrolithiasis). Manual and semi-automated delineations have been used as a gold standard. Wieclawek Among 67 delineated medical cases, 62 cases are ‘Very good’, whereas only 5 are ‘Good’ according to Cohen’s Kappa interpretation. The segmentation results show that mean values of Sensitivity, Specificity, Dice, Jaccard, Cohen’s Kappa and Accuracy are 90.29, 99.96, 91.68, 85.04, 91.62 and 99.89% respectively. All 170 medical cases (with and without outlines) have been classified by three independent medical experts as ‘Very good’ in 143–148 cases, as ‘Good’ in 15–21 cases and as ‘Moderate’ in 6–8 cases.

Conclusions

An automatic kidney segmentation approach for CT studies to compete with commonly known solutions was developed. The algorithm gives promising results, that were confirmed during validation procedure done on a relatively large database, including 170 CTs with both physiological and pathological cases.

3D Fast Automatic Segmentation of Kidney Based on Modified AAM and Random Forest

In this paper, a fully automatic method is proposed to segment the kidney into multiple components: renal cortex, renal column, renal medulla and renal pelvis, in clinical 3D CT abdominal images. The proposed fast automatic segmentation method of kidney consists of two main parts: localization of renal cortex and segmentation of kidney components. In the localization of renal cortex phase, a method which fully combines 3D Generalized Hough Transform (GHT) and 3D Active Appearance Models (AAM) is applied to localize the renal cortex. In the segmentation of kidney components phase, a modified Random Forests (RF) method is proposed to segment the kidney into four components based on the result from localization phase. During the implementation, a multithreading technology is applied to speed up the segmentation process. The proposed method was evaluated on a clinical abdomen CT data set, including 37 contrast-enhanced volume data using leave-one-out strategy. The overall true-positive volume fraction and false-positive volume fraction were 93.15%, 0.37% for renal cortex segmentation; 83.09%, 0.97% for renal column segmentation; 81.92%, 0.55% for renal medulla segmentation; and 80.28%, 0.30% for renal pelvis segmentation, respectively. The average computational time of segmenting kidney into four components took 20 seconds.

Progress in Fully Automated Abdominal CT Interpretation

OBJECTIVE

Automated analysis of abdominal CT has advanced markedly over just the last few years. Fully automated assessment of organs, lymph nodes, adipose tissue, muscle, bowel, spine, and tumors are some examples where tremendous progress has been made. Computer-aided detection of lesions has also improved dramatically.

CONCLUSION

This article reviews the progress and provides insights into what is in store in the near future for automated analysis for abdominal CT, ultimately leading to fully automated interpretation.

Synchrotron radiation (SR) diffraction enhanced imaging (DEI) of chronic glomerulonephritis (CGN) mode

OBJECTIVE

The aim of this study is to investigate microstructural changes in chronic glomerulonephritis (CGN) rabbit model under diffraction enhanced imaging (DEI) technology of synchrotron radiation (SR).

MATERIALS AND METHODS

The chronic glomerulonephritis (CGN) models were obtained within two months after 5 New Zealand white rabbits were treated with doxorubicin hydrochloride. Blood exams, urine tests and kidney histological studies were carried out after the 5 rabbits were humanely sacrificed by hyperanesthesia. The kidney tissues were fixed in 4% formalin for one week before DEI experiment, with another 5 normal rabbits used as the control group. The experiment was performed at Beijing Synchrotron Radiation Facility (BSRF) with a 4W1A beam line (beam energy was 14keV). On routine scanning process, the rocking curve was detected, and slope position on the curve was selected to make a 360° spatial CT scan; DEI reconstruction software was used to generate a 3-dimensional image, from which the difference in grey value between the chronic glomerulonephritis (CGN) group and the control group was measured and analyzed using MATLAB and SPSS.

RESULT

Without radio-contrast, DEI provided clear visibility of the microstructures including artery, vein, straight collecting ducts, papillary tubules, glomeruli in both the chronic glomerulonephritis (CGN) group and the control group, with a spatial resolution as low as 10μm. MATLAB grey value extraction and SPSS analysis showed that cortex of CGN group (91 to 112) lost more gray value compared to the control group (121 to 141), T tests P <  0.05. Equivalant cortical ROI (data points 450×80) quantitative analysis showed that gross grey value of CGN group (ranking from 55 to 160) was smaller than the control group (ranking from 75 to 175). DEI images correlated well with pathologic images. Morphological changes in the microstructure of contstartabstractCGN kidney was revealed, due to the advantage of phase-contrast imaging (PCI) mechanism, and the diagnostic value of CGN by synchrotron radiation (SR) phase-contrast imaging (PCI) technology was evaluated.

CONCLUSION

Synchrotron radiation (SR) diffraction enhanced imaging (DEI) experiment makes non-contrast CGN diagnosis possible in the rabbit model studied. With improvement of laboratory equipment and image analyzer in clinical practice, diffraction enhanced imaging (DEI) could fundamentally become a new diagnostic method for CGN.

CT-based renal volume measurements: correlation with renal function in patients with renal tumours

AIM

To evaluate the correlations between renal cortical volume (RCV), renal parenchymal volume (RPV), and renal function in patients with renal tumours before and after laparoscopic partial nephrectomy (LPN).

MATERIALS AND METHODS

Thirty-five patients with a single unilateral renal tumour who had undergone contrast-enhanced computed tomography (CT) and renal nuclear scintigraphy before and after LPN were retrospectively studied. RCV and RPV were calculated as renal volume, excluding tumours or cysts, using a semi-automatic segmentation program. The correlations between RCV, RPV, and glomerular filtration rate (GFR) were undertaken preoperatively and postoperatively using the Pearson correlation coefficient.

RESULTS

Preoperatively, the correlations between RCV and GFR, and RPV and GFR for the operated kidneys was r=0.502 (p=0.002) and 0.527 (p=0.001), respectively, whereas the correlations for the contralateral side were r=0.384 (p=0.023) and r=0.412 (p=0.014). The mean RCV and RPV of the operated kidneys decreased by 27.4% and 24.8%. The mean split GFR of the operated kidneys decreased by 36.4%. Postoperatively, residual RCV (r=0.619, p<0.001) and RPV (r=0.593, p<0.001) correlated moderately with the GFR of the operated kidneys.

CONCLUSIONS

Renal volume, both RCV and RPV, had a moderate relationship with renal function before and after operation. CT-based renal volume measurements could serve as a simple and effective method for estimation of postoperative renal function.

Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors

This paper addresses the automated segmentation of multiple organs in upper abdominal computed tomography (CT) data. The aim of our study is to develop methods to effectively construct the conditional priors and use their prediction power for more accurate segmentation as well as easy adaptation to various imaging conditions in CT images, as observed in clinical practice. We propose a general framework of multi-organ segmentation which effectively incorporates interrelations among multiple organs and easily adapts to various imaging conditions without the need for supervised intensity information. The features of the framework are as follows: (1) A method for modeling conditional shape and location (shape-location) priors, which we call prediction-based priors, is developed to derive accurate priors specific to each subject, which enables the estimation of intensity priors without the need for supervised intensity information. (2) Organ correlation graph is introduced, which defines how the conditional priors are constructed and segmentation processes of multiple organs are executed. In our framework, predictor organs, whose segmentation is sufficiently accurate by using conventional single-organ segmentation methods, are pre-segmented, and the remaining organs are hierarchically segmented using conditional shape-location priors. The proposed framework was evaluated through the segmentation of eight abdominal organs (liver, spleen, left and right kidneys, pancreas, gallbladder, aorta, and inferior vena cava) from 134 CT data from 86 patients obtained under six imaging conditions at two hospitals. The experimental results show the effectiveness of the proposed prediction-based priors and the applicability to various imaging conditions without the need for supervised intensity information. Average Dice coefficients for the liver, spleen, and kidneys were more than 92%, and were around 73% and 67% for the pancreas and gallbladder, respectively.

Renal perfusional cortex volume for arterial input function measured by semiautomatic segmentation technique using MDCT angiographic data with 0.5-mm collimation

OBJECTIVE

The purpose of this study was to evaluate the usefulness of renal perfusional cortex volume for arterial input function.

MATERIALS AND METHODS

This retrospective study included 45 potential kidney donors--33 patients with aortic dissection and 12 patients with renovascular hypertension--who underwent both MDCT angiography with 0.5-mm collimation and renal (99m)Tc-diethylenetriamine pentaacetic acid (DTPA) scanning using the modified Gates method. Each perfusional cortex volume for the arterial input function and parenchymal volume was measured by semiautomatic segmentation using the region-growing technique. Linear regression analysis and correlation coefficients were used to assess the impact of the cortical volume, parenchymal volume, and renal scanning glomerular filtration rate (GFR) on estimated GFR (eGFR) using a modified Modification of Diet in Renal Disease (MDRD) equation.

RESULTS

The correlation coefficient was higher for the total renal DTPA GFR adjusted for body surface area, weight-adjusted perfusion cortex volume, and adjusted total parenchyma volume in rank (r = 0.712, 0.642, 0.510, respectively, p< 0.0001 for each). The coefficient of the right renal perfusional cortex volume percent with a mean value of 52.1% ± 10.1% was 0.826 (p < 0.0001) for the right renal DTPA GFR percent with a mean value of 51.0% ± 12.1% (range, 22.0-89.5%), although the value for the right renal parenchymal volume percent with a mean value of 49.5% ± 5.5% was 0.764 (p < 0.0001).

CONCLUSION

Weight-adjusted perfusional cortex volume for arterial input function can be measured clinically and may replace renal DTPA scanning using the modified Gates method.

X-ray phase-contrast tomography of renal ischemia-reperfusion damage

Purpose

The aim of the study was to investigate microstructural changes occurring in unilateral renal ischemia-reperfusion injury in a murine animal model using synchrotron radiation.

Material and Methods

The effects of renal ischemia-reperfusion were investigated in a murine animal model of unilateral ischemia. Kidney samples were harvested on day 18. Grating-Based Phase-Contrast Imaging (GB-PCI) of the paraffin-embedded kidney samples was performed at a Synchrotron Radiation Facility (beam energy of 19 keV). To obtain phase information, a two-grating Talbot interferometer was used applying the phase stepping technique. The imaging system provided an effective pixel size of 7.5 µm. The resulting attenuation and differential phase projections were tomographically reconstructed using filtered back-projection. Semi-automated segmentation and volumetry and correlation to histopathology were performed.

Results

GB-PCI provided good discrimination of the cortex, outer and inner medulla in non-ischemic control kidneys. Post-ischemic kidneys showed a reduced compartmental differentiation, particularly of the outer stripe of the outer medulla, which could not be differentiated from the inner stripe. Compared to the contralateral kidney, after ischemia a volume loss was detected, while the inner medulla mainly retained its volume (ratio 0.94). Post-ischemic kidneys exhibited severe tissue damage as evidenced by tubular atrophy and dilatation, moderate inflammatory infiltration, loss of brush borders and tubular protein cylinders.

Conclusion

In conclusion GB-PCI with synchrotron radiation allows for non-destructive microstructural assessment of parenchymal kidney disease and vessel architecture. If translation to lab-based approaches generates sufficient density resolution, and with a time-optimized image analysis protocol, GB-PCI may ultimately serve as a non-invasive, non-enhanced alternative for imaging of pathological changes of the kidney.