Method and apparatus for assessing fibrotic burden of a body organ

Abstract

A renal H-scan carried out using an ultrasound imager includes the step of correcting the data from each ultrasound scanline for temperature and ultrasound attenuation caused by scattering and absorption. A clinical trial in humans demonstrates that the resulting algorithm accurately measures the fibrotic burden in a human kidney, and predicts future kidney function. Method and apparatus are disclosed.

Description

TITLEMETHOD AND APPARATUS FOR ASSESSING FIBROTIC BURDEN OF A BODY ORGAN

[0001] This invention was made with government support under DK126833 awarded by the National Institutes of Health. The government has certain rights in the invention.

[0002] This application claims priority from U.S. Provisional App. No. 63 / 598,598, filed November 14, 2023, which is incorporated herein by reference. FIELD

[0003] This application relates to the field ultrasound apparatus and techniques, and more particularly relates to such apparatus and techniques as used in nephrology.BACKGROUND

[0004] Advanced kidney disease in humans is often treated by kidney transplantation. This procedure involves removing a healthy kidney from a willing donor and implanting the donated kidney into the recipient. It is important to avoid implanting a diseased kidney in a patient.

[0005] Fibrosis is one of the most common and important forms of chronic kidney disease. Fibrosis is irreversible and as the fibrotic burden of a kidney increases, the functionality of the kidney decreases. It is therefore important to evaluate the fibrotic burden of a kidney that is being considered for transplantation.

[0006] At present, the only way to assess the degree of fibrotic burden of a human kidney is to conduct a biopsy. This is unsatisfactory. A biopsy samples less than 1% of the volume of a kidney, and if the fibrotic tissue is located remotely from the biopsy site, the fibrosis will not be detected. Further, biopsy is associated with significant bleeding risk and this disincentivizes a physician from conducting a biopsy. Further, evaluating the results of a biopsy requires the services of an expert renal pathologist. This is not always available, is expensive and can consume considerable valuable time.

[0007] Ultrasound imaging is non-invasive, painless, and relatively inexpensive, and attempts have been made to determine fibrotic burden using ultrasound imaging. Those attempts have been unsuccessful. This is because fibrotic cells are tiny and spatially diffuse and are therefore difficult to resolve in aconventional ultrasound greyscale image. Additionally, changes in brightness levels in an ultrasound greyscale image can occur for many reasons unrelated to fibrosis, whereby image irregularities cannot reliably be attributed to fibrotic tissue as opposed to some other cause.

[0008] It would be advantageous to provide method and apparatus that would make it possible to reliably evaluate the fibrotic burden of a human kidney (or in another body organ or in a body organ of another species such as a mouse) without use of biopsy. It would be further advantageous to do so using ultrasound imaging equipment.SUMMARY

[0009] An aspect of the application is a method of using an ultrasound scanner to assess fibrotic burden of a body organ, the scanner being of a type that creates a raw radiofrequency signal for each ultrasound scanline and uses data from each such raw radiofrequency signal in generation of an ultrasound image, comprising: a. correcting the data from each scanline for at least one of temperature and ultrasound attenuation caused by scattering and absorption, thereby creating corrected data; b. inputting the corrected data from each scanline into an array of N filters to create a plurality N of convolutions; c. determining a maximum value for each convolution, thereby creating N normalized convolution values representing whether scatterers corresponding to such values are fibrotic or non-fibrotic; and d. displaying, on an ultrasound image, indicia of fibrotic and non-fibrotic scatterers at locations corresponding to the filters that generated the corresponding convolutions.

[0010] In certain embodiments, the data is corrected from each scanline for temperature.

[0011] One aspect of the application relates a method of assessing the fibrotic burden of a human kidney before transplanting it into a recipient, comprising: a. removing the kidney from a donor; b. immediately cooling the kidney to 4 °C; c. scanning the kidney using an ultrasound scanner of a type that uses a probe having a center frequency to create a raw radiofrequency signal for each ultrasound scanline and that uses data from such raw radiofrequency signal in the generation of an ultrasound image; d. correcting the data from each scanline for temperature and for ultrasound attenuation caused by scattering and absorption within the kidney to create corrected data; e. inputting the corrected data from each scanline into an array of N filters to create a plurality N of convolutions; f. determining a maximum value foreach convolution, thereby creating N normalized convolution values representing whether scatterers corresponding to such values are fibrotic or non-fibrotic; and g. displaying, on an ultrasound image, indicia of fibrotic and non-fibrotic scatterers at locations corresponding to the filters that generated the corresponding convolutions.

[0012] In certain embodiments, regions of interest are identified within the kidney; and each region of interest is segmented into a plurality of attenuation zones, or an attenuation zone is formed corresponding to each region of interest.

[0013] Another aspect of the application relates an ultrasound scanner adapted for assessing fibrotic burden of a kidney, comprising: a. a probe having a center frequency, the probe creating a raw radiofrequency signal for each ultrasound scanline, the raw radiofrequency signal producing data for generating an ultrasound image;b. means for correcting the data from each scanline for temperature and for attenuation caused by depth of scatterers within the kidney to create corrected data; and c. an array of N filters connected to receive the corrected data from each scanline to create a plurality N of convolutions.

[0014] Another aspect of the application relates to a method of using an ultrasound scanner to assess the degree of tissue abnormality in a body organ, the scanner being of a type that creates a raw radiofrequency signal for each ultrasound scanline and uses data from each such raw radiofrequency signal in the generation of an ultrasound image, comprising: a. correcting the data from each scanline for temperature and for ultrasound attenuation caused by scattering and absorption within the body organ, thereby creating corrected data; b. inputting the corrected data from each scanline into an array of N filters to create a plurality N of convolutions; c. determining a maximum value for each convolution, thereby creating N normalized convolution values representing whether tissues corresponding to such values are normal or abnormal; and d. displaying, on an ultrasound image, indicia of normal and abnormal tissues at locations corresponding to the filters that generated the corresponding convolutions.

[0015] Another aspect of the application relates a method of using an ultrasound scanner to assess and predict the function of a kidney, the scanner being of a type that creates a raw radiofrequency signal for each ultrasound scanline and uses data from each such raw radiofrequency signal in generation of an ultrasound image, comprising: a. correcting the data from each scanline for temperature and for attenuation caused by depth of scatterers within the kidney, thereby creating correcteddata; b. inputting the corrected data from each scanline into an array of N filters to create a plurality N of convolutions; c. determining a maximum value for each convolution, thereby creating N normalized convolution values representing whether scatterers corresponding to such values are fibrotic or non-fibrotic; d. calculating fibrotic burden by determining, within a region of interest, the ratio of fibrotic scatterers to total scatterers; and e. assessing the functioning of the kidney based upon the calculated fibrotic burden.

[0016] Another aspect of the application is a method of using an ultrasound scanner to assess the degree of tissue abnormality in a body organ, the scanner being of a type that creates a raw radiofrequency signal for each ultrasound scanline and uses data from each such raw radiofrequency signal in the generation of an ultrasound image, comprising: a. correcting the data from each scanline for temperature and for ultrasound attenuation caused by scattering and absorption within the body organ, thereby creating corrected data; b. inputting the corrected data from each scanline into an algorithm that associates degree of tissue abnormality with depth within the body; and c. displaying, in an ultrasound image, indicia showing the degree of tissue abnormality within the body at locations corresponding to depth of the tissue within the body.BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

[0018] A preferred embodiment of the invention will be better understood with reference to the exemplary and non-limiting drawings, in which:

[0019] Fig. 1 shows a schematic of a first-in-human transplant kidney H scan imaging study. Kidneys were scanned in 2 longitudinal planes along the longest axis of the kidney, from opposing sides of the kidney (scans #1 and 3) and in 2 transverse planes at the biopsy site in a similar fashion (scans #2 and 4, created in BioRender. Yuen, D. (2023) BioRender.com / o01h847).

[0020] Fig. 2 shows an illustration of the H-scan algorithm. (2A) Attenuation correction of the backscattered RF data 53 is performed by dividing each ROI into 10 zones and re-incorporating the exponential decay of the 54 backscattered power due to depth- and frequency-dependent attenuation. The dominant frequency of the 55 backscattered signals is displayed as a function of axial depth. (2B) A total of 256Gaussian filters were 56 then designed, each with a center frequency ranging from - 70% to +70% of the probe center frequency 57 of 15 MHz. (2C) Each bandpass Gaussian filter was used on the frequency domain of the attenuation- 58 corrected backscattered RF data at each attenuation zone. A convolution was performed with each of the 59 256 filters. The maximum (MAX) of this convolution was identified, and the Gaussian filter index (and 60 corresponding color) was assigned to each depth. Frequencies lower than the center frequency were 61 assigned red colors, and frequencies higher than the center frequency were assigned blue colors. (2D) 62 Representative H-scan color map display from two phantoms of different sized scatterers, denoting larger 63 (more red) and smaller (more blue) scatterer sizes.

[0021] Fig. 3 shows image preprocessing of mouse kidney H-scans. (3 A) Normal kidneys which did not contain 68 hypoechoic inner regions were first manually contoured. Morphological erosion was then used to 69 separate out the outer and inner regions of the kidney. All three masks were used to perform H-scan 70 analysis on the whole kidney, the cortex (outer region), and the medulla (inner region). (3B) Mouse kidneys 71 which contained inner hypoechoic regions were run through morphological opening to exclude these 72 regions before erosion separated the cortex from the medulla. In addition, contouring was also used to 73 segment the entire kidney.

[0022] Fig. 4 shows calculation of temperature-dependent attenuation coefficients for renal H-scan. (4A) Schematic illustrating computation of temperaturedependent attenuation coefficients. (4B) Peak frequency as function of depth for two temperatures relevant to the measurements of kidney transplants (ex-vivo on ice at 4°C and in-vivo at 37°C). The ratio between the slopes of these lines was used to obtain the attenuation coefficient at 4°C, which was then implemented for renal H- scan estimations when imaging was performed in human donor kidneys at 4 °C.

[0023] Fig. 5A shows representative images of (i) PSR-stained sections from sham kidneys as well as fibrotic kidneys on day 7 and day 14 following UUO surgery. Black scale bar: 100 pm. (ii) Representative B-mode and (iii; iv; v) H-scan images of sham kidneys and kidneys at days 7 and 14 8 after UUO surgery. The H-scan maps are shown for the whole kidney and the outer (cortex) and inner 9 regions (medulla). White scale bar: 2 mm.

[0024] Fig. 5B summarizes (vi) H-scan-based fibrosis estimates (% red pixel density) of whole kidney, outer kidney, and inner kidney regions based on images ofleft kidneys of sham mice and day 7 and day 14 post-UUO mice (n = 5 / group). For the whole kidney region of interest, a one-way ANOVA revealed a statistically significant difference between the groups F(2, 14) = 7.11, / ? = 0.0092, with a large effect size / = 0.54. Post-hoc analyses with Tukey’s Honestly Significant Difference analysis, with Bonferroni -corrected significance revealed a significance difference in H-scan %red between Sham vs. Day 7 (p = 0.034), Day 7 vs. Day 14 (p = 0.097), and Sham vs. Day 14 (p = 0.0073). The same analysis and reporting was performed for the outer and inner kidney regions of interest. For the outer kidney region of interest, the H-scan ANOVA statistical results are: F(2, 14) = 6.00, / ? = 0.016, t] = 0.50, Sham vs. Day 7 p = 0.038, Day 7 vs. Day 14 / ? = 0.011, Sham vs. Day 14 / ? = 0.013. For the inner kidney region of interest, the H-scan ANOVA statistical results are: F(2, 14) = 3.81, / ? = 0.042, / = 0.39, Sham vs. Day 7 p = 0.039, Day 7 vs. Day 14 / ? = 0.037, Sham vs. Day 14 / ? = 0.042. Fibrotic burden measurements using gold standard (vii) PSR, (viii) type I collagen, and (ix) a-SMA stains. For PSR staining, the ANOVA statistical results are: Whole kidney: F(2, 14) = 26.21, / ? = 4.18e-5, / = 0.81, Sham vs. Day 7 p = 0.0070, Day 7 vs. Day 14 / ? = 0.020 , Sham vs. Day 14 / ? = 4.05e-5; Outer kidney: F(2, 14) = 21.24, / ? = 1.14e-, 7 = 0.78, Sham vs. Day 7 p = 0.0017, Day 7 vs. Day 14 / ? = 0.023, Sham vs. Day 14 / ? = 0.00010; Inner kidney: F(2, 14) = 15.71, / ? = 4.46e-4, / = 0.72, Sham vs. Day 7 p = 0.0066 , Day 7 vs. Day 14 / ? = 0.026, Sham vs. Day 14 / ? = 0.0004. For type I collagen staining, the ANOVA statistical results are: Whole kidney: F(2, 14) = 10.92, / ? = 0.0020, 7 = 0.54, Sham vs. Day 7 p = 0.0085, Day 7 vs. Day 14 / ? = 0.0093 , Sham vs. Day 14 / ? = 0.0014; Outer kidney: F(2, 14) = 9.95, / ? = 0.0028, ? = 0.62, Sham vs. Day 7 p = 0.0061, Day 7 vs. Day 14 / ? = 0.019, Sham vs. Day 14 / ? = 0.0021; Inner kidney: F(2, 30 14) = 5.71, / ? = 0.018, 7 = 0.49, Sham vs. Day 7 p = 0.0068, Day 7 vs. Day 14 / ? = 0.0079, Sham vs. Day 14 p =0.017. For a-SMA staining, the ANOVA statistical results are: Whole kidney: F(2, 14) = 28.10, / ? = 2.97e-5, 7 = 0.82, Sham vs. Day 7 p = 0.00050, Day 7 vs. Day 14 / ? = 0.016, Sham vs. Day 14 / ? = 2.81e-5; Outer kidney: F(2, 14) = 12.29, / ? = 0.0012, 7 = 0.67, Sham vs. Day 7 p = 0.018, Day 7 vs. Day 14 / ? = 0.0286, Sham vs. Day 14 / ? = 0.0010; Inner kidney: F(2, 14) = 122.99, / ? = 1.01e-8, 7 = 0.95, Sham vs. Day 7 p = 7.87e-8, Day 7 vs. Day 14 / ? = 0.021, Sham vs. Day 14 / ? = 1.72e-8.

[0025] Fig. 5C shows correlation between H-scan based % red pixel density and histologic fibrosis scores derived from (x) PSR, (xi) type I collagen, and (xii) a- SMA staining for the whole kidney, outer and inner regions. Pearson correlationcoefficients (r) and corresponding p values are provided for each comparison in each graph, all of which are statistically significant. Data are presented as mean + / - standard error. * p < 0.05.

[0026] Fig. 6A shows H-scan analysis of human nephrectomy specimens. Portions of the human kidney cortex removed during radical nephrectomy were scanned in a left to right direction (created in BioRender. Yuen, D. (2024) BioRender.com / n061871). (i) Representative B mode and ii) H-scan images of the five specimens ordered in increasing overall fibrotic burden (white scale bar denotes 5 mm), (iii) H-scan pixel histograms showing the % red (larger scatterers) and % blue (smaller scatterers) pixels in each specimen. In (iv), a motor moved the ultrasound probe at 150 pm increments from left to right, thus providing a three-dimensional assessment of fibrotic burden, (v - vi) Representative (v) PSR and (vi) Masson Tri chrome-stained sections from each of the 5 specimens (black scale bar denotes 100 pm). The renal fibrotic burden was then estimated using gold standard histologic quantification of the PSR and Masson Trichrome stained sections. Correlations between H scan % red fibrosis values and gold standard histologic measurements of fibrosis, as assessed by (vii) PSR and (viii) Masson Trichrome stains. Pearson correlation coefficients (r) and corresponding p values are provided in each graph.

[0027] Fig. 7 shows H-scan of human transplant kidneys. Kidneys were scanned in 2 longitudinal planes along the longest axis of the kidney, from opposing sides of the kidney (scans #1 and 3) and in 2 transverse planes at the biopsy site in a similar fashion (scans #2 and 4, created in BioRender. Yuen, D. (2023) BioRender.com / o01h847). Fig. 7A shows (i) representative PSR- and (ii) HPS-stained sections and (iii) H-scan images of a subcapsular cortex region of interest performed at the biopsy site (Scan #2 for the US imaging plane). Scale bar dimensions: 500 pm for (i), 50 pm for (ii) and 10 mm for (iii). Fig. 7B shows quantification of each of the (iv) PSR, (v) HPS, and (vi) H scan images for all 61 donor kidneys, ordered in increasing stain / H-scan % red levels, with each bar denoting a patient enrolled in the study. Fig. 7C shows Comparison of DD and LD biopsy site fibrotic burden as assessed by (vii) PSR, (viii) HPS, and (ix) H-scan. For the LD vs. DD comparison, an independent samples t-test was performed, relevealing no statistical significance between the groups, with the the following test statistics: PSR (p = 0.63, F = 0.24, 95% CI [-2.07 3.42], tf = 0.0075), HPS (p = 0.80, F = 0.059, 95% CI [-6.65 8.49], tf = 0.0018), and H-scan (p = 0.92, F= 0.011, 95% CI [-2.99 3.31], rf = 0.0030). Fig. 7Dshows correlations between the H-scan scores derived from the biopsy site subcapsular cortex ROI with (x) PSR and (xi) HPS histological stains of the same region. Pearson correlation coefficients (r) and corresponding p values are provided in each graph.

[0028] Fig. 8 shows H-scans increasingly further away from the biopsy site correlate progressively more poorly with biopsy measures of fibrosis, reflecting the spatial heterogeneity of fibrosis distribution. Imaging plane schematic and correlation of H-scan with PSR for (i) the cortex and medulla ROI at the biopsy site (created in BioRender. Yuen, D. (2024) BioRender.com / e85p691), (ii) the cortex away from the biopsy site (created in BioRender. Yuen, D. (2024) BioRender.com / d06e028) and (iii) cortex and medulla away from the biopsy site (created in BioRender. Yuen, D. (2024) BioRender.com / g82q619). For both (ii) and (iii), a longitudinal imaging plane was taken through the kidney. Pearson correlation coefficients (r) and corresponding p values are shown in each correlation graph.

[0029] Fig. 9 shows spatial heterogeneity of fibrosis distribution as assessed by HPS. Correlations of the H-scan 45 estimates with HPS for ROI drawn in (i) the cortex and medulla at the biopsy site, (ii) the cortex away 46 from the biopsy site, and (iii) the cortex and medulla away from the biopsy site. Pearson correlation 47 coefficients (r) and corresponding p values are provided for each comparison in each graph, none of 48 which are statistically significant.

[0030] Fig. 10 shows H-scan estimates of whole kidney fibrotic burden correlate with renal function post transplant, whereas localized biopsy -based measures do not. (10A) Average eGFR values between 9 - 12 months post-transplant for living donor (LD) and deceased donor (DD) kidney transplant recipients. (10B) eGFR values at 9 - 12 months post-transplant for KDPI < 85% and KDPI > 85% kidneys. Mean eGFR values at 9 - 12 months post-transplant, organized by quartiles of (IOC) whole kidney H-scan renal fibrosis measurements (created in BioRender. Yuen, D. (2024) BioRender.com / tl7z370), (10D) biopsy site bcortex PSR staining (created in BioRender. Yuen, D. (2024) BioRender.com / k51i049), (10E) % of glomeruli with global glomerulosclerosis (created in BioRender. Yuen, D. (2024) BioRender.com / v37q724), (10F) biopsy site cortex H-scan renal fibrosis measurements (created in BioRender. Yuen, D. (2024) BioRender.com / k76g036). Data are presented as mean + / - standard error. For panels (10A - 10B), a two-tailed Student’s t test was used. An independent samples t-test comparison for LD vs. DDpatients eGFR had the following test statistics: p = 0.0086, F = 3.07, 95% CI [-1.47 21.64], r| = 0.10, whereas for the KDPI comparison had the following statistic: p = 0.033, F = 5.15, 95% CI [2.34, 52.44], r| = 0.20. For panels (IOC - 10F), a one-way ANOVA with post-hoc Tukey’s Honestly Significant Difference analysis was performed with Bonferroni-corrected significance and tests for the effect size. For the whole kidney H-scan, the ANOVA statistical results are: F(3, 52) = 7.36, p = 3.63e-4, r| = 0.31, QI vs. Q2 p = 0.0070, QI vs. Q3 p = 0.025, QI vs. Q4 p = 0.0016, Q2 vs. Q3 p = 0.0071, Q2 vs. Q4 p =0.0019 , Q3 vs. Q4 p = 0.015. For the biopsy site PSR, the ANOVA statistical results are: F(3, 52) =1.44, p = 0.24, r] = 0.081, QI vs. Q2 p = 0.47, QI vs. Q3 p = 0.99, QI vs. Q4 p = 1.00, Q2 vs. Q3 p =0.35, Q2 vs. Q4 p = 0.85, Q3 vs. Q4 p = 1.00. For the biopsy site GS, the ANOVA statistical results are: F(3, 52) = 1.15, p = 0.34, r| = 0.066, QI vs. Q2 p = 0.49, QI vs. Q3 p = 1.00, QI vs. Q4 p = 0.55, Q2 vs. Q3 p = 0.75, Q2 vs. Q4 p = 1.00, Q3 vs. Q4 p = 0.72. For the biopsy site H-scan, the ANOVA statistical 500 results are: F(3, 52) = 0.71, p = 0.55, r] = 0.042, QI vs. Q2 p = 0.73, QI vs. Q3 p = 0.94, QI vs. Q4 p =501 0.99, Q2 vs. Q3 p = 0.96, Q2 vs. Q4 p = 0.57, Q3 vs. Q4 p = 0.81. * denotes p < 0.05.DETAILED DESCRIPTION

[0031] Reference will be made in detail to certain aspects and exemplary embodiments of the application, illustrating examples in the accompanying structures and figures. The aspects of the application will be described in conjunction with the exemplary embodiments, including methods, materials and examples, such description is non-limiting and the scope of the application is intended to encompass all equivalents, alternatives, and modifications, either generally known, or incorporated here. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. One of skill in the art will recognize many techniques and materials similar or equivalent to those described here, which could be used in the practice of the aspects and embodiments of the present application. The described aspects and embodiments of the application are not limited to the methods and materials described.

[0032] As used in this specification and the appended claims, the singular forms "a," "an" and "the" include plural referents unless the content clearly dictates otherwise.

[0033] Ranges may be expressed herein as from "about" one particular value, and / or to "about" another particular value. When such a range is expressed, another embodiment includes from the one particular value and / or to the other particular value. Similarly, when values are expressed as approximations, by use of the antecedent "about," it will be understood that the particular value forms another embodiment. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint. It is also understood that there are a number of values disclosed herein, and that each value is also herein disclosed as "about" that particular value in addition to the value itself. For example, if the value "10" is disclosed, then "about 10" is also disclosed. It is also understood that when a value is disclosed that "less than or equal to" the value, "greater than or equal to the value" are also disclosed, as appropriately understood by the skilled artisan. For example, if the value " 10" is disclosed, the "less than or equal to 10" and “greater or equal to 10” is also disclosed. When two or more value are disclosed, all possible ranges between any two values are disclosed.Overview

[0034] In an immediate sense, this application relates to assessment of the fibrotic burden of the kidney. More particularly, the application relates to assessment of the fibrotic burden of a human kidney. In one specific aspect, the application relates to assessment of the fibrotic burden of a human kidney that is a candidate for transplantation from a donor to a recipient patient.

[0035] Therefore, one object of the application is to provide method and apparatus that makes it possible to reliably evaluate the fibrotic burden of a human kidney (or other tissue irregularity in another human or animal body organ) without use of biopsy. Another object is to do this using ultrasound imaging equipment. Yet another object is to improve on known methods and apparatus for evaluating fibrotic burden.

[0036] The application proceeds from the realization that H-scan (an existing quantitative ultrasound technique) can be modified and adapted to distinguish between fibrotic (or other irregular) tissues and normal tissues. A conventional ultrasound imager can be used to conduct an examination for fibrotic burden, whereby an examination lasting a few minutes can cover the entire kidney (as opposed to the 1% of kidney volume that is sampled by a biopsy) without requiring the services of an expert renal pathologist. And, clinical trials have produced the new and unexpectedresult that results obtained using this modified and adapted technique (here referred to as “renal H-scan”) on human kidneys are tightly correlated with gold standard histologic measures of renal fibrotic burden. Hence, it is possible to quickly and accurately assess the fibrotic burden of a donor kidney before transplantation into a patent. This reduces the risk that a transplant recipient receives a diseased kidney, and also ensures that kidneys that would otherwise have been discarded because of concerns of fibrotic disease are actually used for transplant if this modified and adapted technique (“renal H-scan”) indicates that the fibrotic burden is in fact sufficiently low to enable successful transplant.Materials and MethodsA. Ethics Approvals

[0037] The herein-reported mouse studies were approved by the St. Michael’s Hospital (SMH, Toronto, Canada) Animal Ethics Committee and conformed to the Canadian Council on Animal Care guidelines. The SMH institutional review board approved the human protocols used for these experiments, which adhered to the Declaration of Helsinki. All patients provided written informed consent. The clinical and research activities being reported are consistent with the Principles of the Declaration of Istanbul as outlined in the “Declaration of Istanbul on Organ Trafficking and Transplant Tourism.”B. Unilateral Ureteral Obstruction Model of Kidney Fibrosis

[0038] Six- to eight-week-old male C57BL / 6 mice (Charles River Laboratories) underwent left-sided unilateral ureteral obstruction (UUO) surgery. Briefly, the left kidney and ureter were identified through a left-sided flank incision made in anesthetized mice. To induce fibrosis, the left ureter of n = 10 mice was obstructed using surgical sutures, just distal to the renal pelvis. The contralateral right kidney was not injured, and served as a healthy, non-fibrotic control to the left kidney which developed increasingly progressive fibrosis. In addition, n = 5 mice (sham) also served as controls where a similar flank incision was made without obstructing the ureter. Sham-operated animals (n = 5) were followed for 14 days post-surgery, whereas UUO mice were sacrificed on Day 7 (n = 5) and Day 14 (n = 5) post-surgery. For all mice (n = 15), the left and right kidneys were extracted, and each kidney was imaged prior to histological examinations.C. Human Nephrectomy Specimens

[0039] Residual kidney tissue from radical nephrectomy procedures performed for renal cancer was collected at SMH (n = 5 patients). A portion of the renal cortex and medulla was collected from the non-cancerous pole of the excised kidneys and was submerged in 4 °C phosphate buffered saline (PBS) until ultrasound (“US”) imaging was performed. Fig. 1 schematically shows collection and imaging of the human kidney specimen.D. Preclinical Kidney and Nephrectomy Specimen Imaging

[0040] Imaging was performed using a VevoLAZR-X imaging system equipped with a 15 MHz center frequency linear array probe containing 256 elements (FujiFilm-VisualSonics Inc.). For mouse kidney imaging, each kidney was excised and placed in a container with phosphate-buffered saline kept at 4 °C and 2D B-mode ultrasound images were acquired through the longest longitudinal cross section of the kidney. A total of 59 temporal acquisitions were acquired at an imaging frame rate of 5 Hz. Imaging of human kidney specimens was performed by mounting the probe to a 3D motor capable of performing scanning at a step size of 150 pm, which was well below the elevational resolution of the imaging system. A total of 130 2D scans were acquired, covering the entire length of the specimens at an imaging frame rate of 5 Hz.

[0041] In the preferred embodiment, each ultrasound scanline is generated by the traditional pulse-echo A-scan using a focused transducer (the above-referenced linear array probe). However, this is not required. The scanlines can alternatively be generated from plane wave transmissions with synthetic beamforming.E. Clinical Trial Design

[0042] A prospective cohort study was performed at SMH between December 2021 and May 2023. All patients undergoing kidney transplantation were eligible for the study. The only exclusion criterion was lack of informed consent. The following demographic and clinical characteristics were collected for the donors: age, gender, height, weight, ethnicity, history of hypertension, history of diabetes, cause of death, donor type (neurological determination of death, NDD vs donation after cardiac death, DCD), terminal serum creatinine and CKD-EPI estimated glomerular filtration rate (eGFR), and hepatitis C serology. Similarly, the following information was collected for the recipients: age, gender, height, weight, initial cause of end stage kidney disease, type of dialysis, and dialysis vintage. Cold and warm ischemia times for each kidney were also recorded. Tables 1 and 2 summarize these parameters forthe participants enrolled in the trial. With a single optical mode, pump and Stokes light propagating in the same direction can couple to guided acoustic waves, leading to so-called forward Intra-modal Brillouin interactions. While these interactions open up new frequencies and have enabled new devices, as described in more detail below, they are still limited in frequency, linewidth and coupling, but now by the device geometry.TABLE 1: Recipient characteristicsAll values are mean + / - standard deviation unless otherwise noted.TABLE 2: Transplant kidney and donor characteristics. All values are mean ± standard deviation unless otherwise noted. Abbreviations: DD: deceased donors; LD: living donors; NDD: neurological determinant of death; DCD: donation after cardiac death; CNS: central nervous system; CKD-EPI: chronic kidney disease epidemiology collaboration; eGFR: estimated glomerular filtration rate.All values are mean + / - standard deviation unless otherwise noted.F. Human Transplant Kidney Imaging Protocol

[0043] Following fat removal and kidney inspection on the back table, a small wedge biopsy was performed on the upper pole of each human donor kidney. Each kidney then underwent sterile, blood-free US imaging on the back table (5 minutes in duration) while the recipient was prepared for surgery, and thus did not extend cold ischemia time. Kidneys were kept on ice-cold slush during the imaging, and sterile US gel was used to couple the probe with the kidney. Imaging was performed on either side of the kidney along two planes: (1) the longest longitudinal plane of the kidney (Scans 1 and 3), and (2) transversely at the upper pole at the biopsy site (Scans 2 and 4).G. Development of a Renal H-scan Algorithm

[0044] Fig. 2a provides a schematic illustrating the stepwise development of the renal H-scan algorithm. H-scan relies on quantifying the spectral shifts of reflected echoes from ultrasonic scatterers by using matched filters to extract differences in scatterer size. To extract such spectral shifts, which the study hypothesized would change as renal fibrosis developed, Gaussian functions were first generated and were subsequently convolved with the backscattered US data. In orderto remove the depth- and frequency-dependent attenuation effects on the scatterer size estimations, each region of interest was divided into 10 region of interest (ROI) attenuation zones. Fig. 2a shows representative raw and attenuation-corrected RF data. Attenuation correction was performed by multiplying the spectrum of each of the 10 ROI zones by e+afxz ( a is the attenuation coefficient, f is a transmit frequency of a transducer, and xz is a representative depth for the zth area index). For mouse kidneys and human nephrectomy specimens, the attenuation coefficient chosen was 0.5 dB / cm / MHz and assumed to be uniform throughout the entire kidney. This coefficient was further adjusted to account for the reduced imaging temperature of the human transplanted whole kidneys, as they were imaged at 4 °C. The axial profile of the dominant frequency contained within the RF signal reveals the effect of attenuation correction in the plot shown in Fig. 2a.

[0045] The resulting attenuation-corrected RF backscattered data was then used as the input for the H-scan algorithm. A total of 256 Gaussian-matched filters were created based on the center frequency and bandwidth of the 15 MHz transmit center frequency transducer used in this study, as illustrated in Fig. 2b. Specifically, filter G1 had a center frequency 70% lower than the center frequency of the measured spectrum and a 70% bandwidth. The center frequency of the subsequent matched filters (G2 to G256) was increased proportionally, up to 70% higher than the center frequency of the measured power spectrum. Using each of the matched Gaussian functions as a band pass filter, the frequency spectrum of the attenuation- corrected RF data was filtered for each scanline at each attenuation zone. The inverse Fourier transform converted the data back into the time domain. This is also the equivalent of a convolution of 256 band-passed filtered RF signals in the temporal domain.

[0046] As illustrated in Fig. 2c, this operation resulted in 256 normalized convolution values (band-passed filter output amplitudes). The maximum value of this convolution was then identified, enabling the assignment of the corresponding Gaussian filtered index for a given RF line (shown in the x-axis). Since each of the 256 Gaussian filters has a unique peak frequency, the corresponding peak frequency can then be assigned to each depth in the sample. H-scan color mapping was then performed by matching the 256 frequencies to 256 color levels, from red (color level 1) to blue (color level 256). As such, color levels 1 through 128 correspond to lower frequency components (or larger scatterers), and are pseudo-colorized with various shades of red. Conversely, colors 129 through 256 correspond to higher frequencycomponents (or smaller scatterers), which 710 are denoted by blue colors. The center frequency of G128 was optimized according to the specific target organ and disease under study. For instance, the study has demonstrated in murine liver fibrosis studies that fibrosis with lower steatosis contained more blue colors, while red was more prevalent during fibrosis with higher fat content 6. By setting the mean of the healthy control kidneys to approximately 50% blue and to the 128 color level, the study can visualize various scatterer sizes throughout the kidney.

[0047] Fig. 2(d) shows a representative H-scan image from two reference phantoms of different sizes, chosen to reflect the expected scatterer sizes in the kidney. Both phantoms consisted of glass beads embedded in a homogenous background of microscopic oil droplets in gelatin 7-9. The “large scatterer” phantom contained beads of an average diameter 49.35 ± 10.8 pm. Its speed of sound and attenuation at 10 MHz were 1488 m / s and 0.719 dB / cm, respectively. The beads of the “small scatterer” phantom had an average diameter of 6.15 ± 1.3 pm, a sound speed of 1541 m / s and an attenuation of 0.439 dB / cm at 10 MHz. Each phantom was scanned using linear array probes at 15 MHz transmit center frequency, using a 256- element linear array part of the VevoLAZR-X system. The probe had spatial resolutions of 100 pm, 200 pm and 1.2 mm in the axial, lateral and elevation planes, respectively.H. Image Pre-processing

[0048] Fig. 3 shows a schematic depicting how the H-scan technique was adapted for mouse kidney imaging. The kidney was manually contoured before implementing the H-scan algorithm on the whole organ (Fig. 3a). Ureteral obstruction causes gross dilation of the renal pelvis and medullary calyces, leading to inner hypoechoic areas within the kidney. These areas were excluded from H-scan analysis through morphological opening (Fig. 3b) implemented using built-in Matlab functions (Mathworks version 2021a, Natick, MA, USA). Morphological erosion was then used to separate the kidney's outer (cortical) regions from the inner (medullary) regions using erosion Matlab built-in functions. The ratio between the length from the kidney 735 center to the inner region boundary and the length from the inner to outer region boundary was set to 1. Both the outer and inner regions of the mouse kidney were analyzed, denoting primarily the cortex and medulla, respectively. The morphological opening algorithm was also implemented on human kidney specimens to exclude hypoechoic or low signal -to-noise regions. However, no separation of the cortex andmedulla was performed on the human nephrectomy samples, as in most specimens, only the cortex was sampled. For all kidneys, the percentage of red or blue pixels was defined as the ratio between the number of pixels with color ranges 1-128 (corresponding to red) or 129-256 (corresponding to blue) with the total number of pixels within a ROI. A histogram distribution of the H-scan color levels was also generated to quantify changes in scatterer size for different regions within mouse kidneys or various levels of fibrosis in human kidneys.I. Adaptation of Renal H-scan to Transplanted Kidneys

[0049] Immediately following the nephrectomy, the donated graft was placed within a slushy / crushed ice solution kept at 4 °C. The amount of time the kidney is kept on this solution (until ready to be transplanted) is referred to as cold ischemia and US imaging was performed within this setup without prolonging the cold ischemic time. US attenuation is generally known to be temperature-dependent and the renal H-scan algorithm developed for mouse kidneys was modified to account for this effect by first estimating the attenuation coefficient at 4 °C before correcting the US backscattered data. Such estimations were compared with known attenuation coefficients for kidneys at physiological temperatures, namely 1.0 dB / MHz / cm at 37 °C.

[0050] Fig. 4a shows a schematic of the algorithm for the estimation of the attenuation coefficient from the kidney images acquired on the back table following the nephrectomy. The backscattered US frequency spectrum can be modeled using the Cf-fo)2Gaussian function e 2a2(f is frequency, f0is center frequency for transmission, and <J is bandwidth). The attenuation corrected power spectrum 5( ) can be then described by:where, a is attenuation coefficient, and x is depth. This peak of this frequency spectrum can be identified by computing when the first partial derivative of 5( ) with respect to frequency f at the peak frequency fpbecomes 0:

[0051] Since the first term is 0, the study obtained:

[0052] The solution of this equation can be obtained by taking the first derivative of both sides with respect to x, re-writing the equation as:

[0053] Since the same US transmission was used regardless of temperature, the bandwidth <J for the transmit pulse before attenuation is the same across all temperatures. To calculate dfp / dx for 4°C and 37°C, the peak frequencies along with depth were investigated using the renal H-scan analysis through the first four attenuation blocks in Fig. 2. The frequency measures in Fig. 4(b) outputted the slopes of I = -2.44 and ^1 = -1.45 when the frequency measurement plots are dx I T’=4°C dx \ T=37°C averaged using all enrolled kidneys. Since the attenuation is known at 37°C is known to be 1 dB / MHz / cm, the estimated attenuation coefficient at 4°C was estimated to 1.7 dB / MHz / cm using Equation (5) and the measured slopes calculated in Fig. 4b. This attenuation value was used to perform the attenuation compensation described in the generic H-scan methodology section summarized herein.J. Tissue Collection and Histology Quantification of Fibrosis

[0054] Immediately following US imaging, each kidney sample or biopsy was immersed in 10% neutral buffered formalin for immediate fixation. The formalin- fixed tissues were embedded in parafilm and sectioned. As shown in Fig. 5 A, the mouse and human kidneys were sectioned into approximately thirds, with the mouse kidney sectioned at each of the kidney poles and near the center. Three sections per kidney sample were then stained with picrosirius red (PSR, Millipore Sigma), hematoxylin phloxine saffron (HPS, Vendor), and / or Masson Trichrome (Vendor) to visualize fibrotic matrix. 3-5 random, non-overlapping images were collected at 20* magnification using an Olympus microscope by a blinded observer. Using Aperio Imagescope software (Leica Biosystems), fibrotic burden was then quantified in a blinded fashion by calculating the ratio of positively stained pixels to total pixels, as has been previously performed.1. ResultsA. H-scan accurately tracks mouse kidney fibrosis progression

[0055] To first assess the ability of renal H-scan to quantify kidney fibrosis, we used the unilateral ureteral obstruction (UUO) mouse model to induce progressive kidney fibrosis, which develops over the course of 14 days (Figs. 5A(i) - (ii)). Although conventional B-mode ultrasound imaging of mouse kidneys 7 and 14 days post-UUO detected progressive dilation of the renal pelvis and calyces (Fig. 5 A(iii)), no obvious differences in renal cortical tissue were noted on the B-mode images when compared to healthy sham operated kidneys. In contrast, H-scan imaging of the kidney demonstrated a progressive increase in the number of large ultrasound scatterers (visualized as red pixels on the H-scan images) in the fibrotic left kidneys of UUO mice over the course of 14 days, reflecting increased matrix deposition (Fig. 5A(iv)). Analysis of only the outer kidney region (cortex) and the inner region (medulla) demonstrated similar increases in H-scan % red pixel content (Fig. 5A(iv)(v) - 5B(vi)). Fig. 5B(vi) shows the quantification of fibrotic H-scan red pixel content in kidneys from the sham group compared to the day 7 and day 14 UUO kidneys. In line with the H-scan findings, gold standard histologic fibrosis measurements, as assessed by picrosirius red (PSR), Collagen I, and a-smooth muscle actin (a-SMA) staining, demonstrated a similar rise in fibrotic burden over the course of 14 days (Figs. 5B(vii) - 5B(ix)). Importantly, H-scan % red pixel density correlated tightly with these gold standard histology-based measurements of renal fibrosis (Figs. 5C(x)- 5C(xii)), indicating that H-scan can accurately quantify experimental murine kidney fibrosis and its progression over time (all correlations correlation > 0.91 for whole kidney, outer and inner regions of interest).B. Human nephrectomy fibrosis can be quantified using H-scan

[0056] To test whether the study could replicate the findings in human kidney tissue, the study next scanned a set of human radical nephrectomy specimens. Fig. 6 summarizes the H-scan results for the five human kidney samples that were investigated in this study. Although H-scan imaging demonstrated considerable inter- and intra- sample heterogeneity in % red pixel density (estimate of renal fibrotic burden, the H-scan-based fibrosis estimates again correlated tightly with gold standard histology assessments. Taken together, the data provide the first evidence suggesting that H-scan represents a potential novel non-invasive way to accurately quantify human kidney fibrotic burden.C. Human transplant kidney fibrosis assessment using H-scan

[0057] To determine whether the study can utilize renal H-scan in a clinical setting, the study next conducted a first-in human clinical experiment of this imaging technique in human donor kidneys being used for transplant. A total of 61 donor kidneys were imaged, including 28 deceased donors (DD) and 33 living donors (LD). Each kidney was transplanted into a unique recipient, and thus recipients were enrolled in the trial. Recipient and donor demographic and clinical characteristics are summarized in Tables 1 and 2, respectively. Mean warm ischemia time (+ / - standard deviation) for all kidneys was 37 ± 7 mins, and mean cold ischemia time for the deceased donor kidneys was 549 ± 191 mins.

[0058] Sections from donor kidney wedge biopsy samples were stained to estimate fibrotic burden (Figs. 7A(i) -(ii)), and each kidney was scanned post-biopsy (Fig. 7A(iii)). The study noted considerable heterogeneity in matrix deposition within and between kidney biopsies, with up to 30% and 60% variation in PSR- and hematoxylin phloxin saffron (HPS) measurements of fibrotic burden across the 61 donor kidneys, respectively (Figs. 7B(iv) - 7B(v)).

[0059] Renal H-scanning of each kidney lasted on average 5 minutes and could be performed by resident and staff surgeons with minimal training (Fig. 7A(iii), 7B(vi)). Because of the marked intra-renal spatial heterogeneity that the study had observed in the prior analyses (Fig. 6), the study first focused the attention on the renal H-scans of the subcapsular cortex at the biopsy site (Fig. 7A(iii), 7B(vi)). Interestingly, both renal H-scan and histologic measurements indicated that the fibrotic burden in 195 living donor kidneys was similar to that of deceased donors (Figs. 7C(vii)- (ix)). Importantly, these spatially co-localized analyses demonstrated a strong correlation between renal H-scan estimates and gold standard histologic measurements of renal fibrotic burden (Figs. 7D(x) - (xi)).

[0060] As fibrosis is often heterogeneously distributed throughout the kidney, the study next hypothesized that H scans of larger regions of interest (ROIs) that included more tissue outside of the biopsy site would be less strongly correlated with the highly localized biopsy-based histologic estimates of fibrotic burden. To answer this question, the study compared the histologic fibrosis measurements from the biopsy site (containing only subcapsular cortex) with renal H-scan values from the entire depth of the kidney (cortex and medulla) at the biopsy site (Fig. 8(i)). This analysis was done also when either the cortex (Fig. 8(ii)) or the cortex and medulla were imaged away from the biopsy site using a longitudinal imaging plane (Fig.8(iii)). As shown in Fig. 8(i)and Fig. 9(i), a renal H-scan of both the cortex and medulla at the biopsy site still positively correlated with histologic estimates, although the correlation was weaker than when only the actual site of the biopsy (the cortex) was imaged (Figs. 7D(x) - (xi)). Similarly, both cortical (Fig. 8(ii) and Fig. 9(ii)) or full thickness (Fig. 8(iii) and Fig. 9(iii)) H-scans taken away from the biopsy site, along the longitudinal axis of the kidney, demonstrated even weaker correlations with biopsy-based histologic analyses.D. H-scan results associate with renal function post-transplant

[0061] Given the importance of fibrosis as a marker of chronic injury, the study next hypothesized that H-scan estimates of donor-derived renal fibrotic burden would associate with kidney function post-transplant. At study end, 53 patients (n = 29 LD, n = 24 DD) had reached a minimum of 12 months post-transplant. The mean eGFR in these 53 patients was 60 ± 21 mL / min / 1.73 m2 body surface area between 9 and 12 months post-transplant. As expected, living donor kidney transplant recipients had a higher eGFR when compared to deceased donor kidney recipients (Fig. 10a, 220 p < 0.05). Amongst deceased donor kidney recipients, those who received KDPI < 85% kidneys (n = 21) had, on average, better renal function compared to KDPI > 85% kidney recipients (n = 3, Fig. 10b, p < 0.05). Interestingly, whole kidney H scan fibrosis estimates were negatively associated with eGFR at 9 - 12 months posttransplant (r = -0.53, p = 0.00004), with a stepwise decline in kidney function noted with increasing H-scan quartile (Fig. 10c).

[0062] In contrast, standard histologic fibrosis measurements such as interstitial fibrosis (Fig. lOd) and the % of glomeruli with global glomerulosclerosis (GS; Fig. lOe), a commonly used metric to assess donor kidney quality in the United States, correlated poorly with eGFR at 9 - 12 months post-transplant (PSR: r = 0.01, p = 0.9; GS: r = -0.16, p = 0.3). The study hypothesized that this result might be due to spatial heterogeneity in fibrosis distribution, meaning that fibrosis levels at the biopsy site might not accurately reflect whole kidney fibrotic burden. Therefore, we assessed whether fibrosis estimates derived from H-scans performed only at the biopsy site would also correlate less strongly with eGFR post-transplant. As expected, H-scans performed only at the biopsy site (Fig. 1 Of) also correlated poorly with eGFR at 9 - 12 months post-surgery (r = 0.03, p = 0.8). Taken together, our results indicate that whole kidney H-scan imaging was the only fibrosis measure tested that associatedwith eGFR at 9 - 12 months post-transplant, whereas other more localized measurements (H-scan at the biopsy site, biopsy-based histologic analyses) did not. Analysis

[0063] Here, the study describes renal H-scan, an algorithm that can use standard ultrasound data to quickly, easily, accurately, and non-invasively quantify whole kidney fibrotic burden in both mice and humans. Taking advantage of custom- designed kidney-specific H-scan algorithms that decode raw radiofrequency data generated by ultrasound imaging, the study shows that renal H-scan strongly correlates with renal fibrotic burden. Importantly, H-scan provides highly reproducible estimates of renal fibrosis that not only identify differences in fibrotic burden between kidneys, but also spatially discrete differences within a given kidney (Fig. 5 and Fig. 8). The study further demonstrates the clinical value of this technique, showing that donor kidney fibrotic burden quantified by a pre-transplant H-scan is negatively associated with kidney function post-transplant (Fig. 10).

[0064] Importantly, renal H-scan software can be added to any standard ultrasound workflow, meaning that widely available conventional ultrasound probes can be used with this algorithm to rapidly and easily estimate whole kidney fibrotic burden. Unlike other fibrosis imaging strategies, renal H-scan does not require specialized equipment or potentially harmful contrast agents. However, because it images only the kidney, renal H-scan reports only on kidney fibrosis levels and not on systemic fibrotic burden, which differentiates it from blood-based markers, which often reflect fibrosis in all tissues. Thus, renal H-scan software is ideally positioned for rapid translation as a tool for scientists and clinicians to specifically measure whole kidney fibrotic burden.

[0065] Beyond its ability to quantify whole kidney matrix levels, a key benefit of renal H-scan is its ability to quickly and non-invasively sample tissue across a wide spectrum of length scales, ranging from small areas of interest to the entire kidney (Fig. 5 and Fig. 8). Thus, renal H-scan can also be used to characterize the heterogeneous spatial distribution of matrix deposition within a given kidney. Importantly, this intra-renal heterogeneity has long been a major limitation of histologic analyses of renal fibrotic burden, given that a biopsy samples < 1% of the entire kidney volume and is usually performed only in the cortex. The study demonstrated how this spatial heterogeneity can influence the results of biopsy based histologic analysis, as the H-scan fibrosis measurements correlated tightly withbiopsy-based measures only when the H-scan was performed at the same biopsy site. As the H-scan region of interest was moved further away from the location of the biopsy, the correlation between biopsy-based histologic fibrosis scores and those generated by H-scan progressively worsened. The renal H-scan can be used in the future to better understand how the spatial organization of matrix differs following various types of kidney injury and over time. Moreover, fibrosis is usually the consequence of other underlying diseases that, unlike fibrosis, might be amenable to treatment. Because these treatable diseases typically localize to non-scarred areas of the kidney, renal H-scan might also enable biopsy targeting to locations within the kidney that are less scarred to allow identification of these treatable forms of injury.

[0066] Another clinical advantage of renal H-scan is its ability to be performed rapidly without requiring specialized expertise. Kidney tissue processing, staining, and analysis are time-consuming, ranging from hours for frozen sections to days for formalin-fixed paraffin-embedded tissue, the latter being not possible for donor kidneys given the limited time available for assessment. Histologic fibrosis measurements also ideally require an expert renal pathologist, which adds further time and expertise that is not always available, especially in the context of time-sensitive donor kidney analyses. Automated algorithms for fibrosis quantification of stained tissue sections have been developed, but these generally require whole slide scanning and, at least currently, are still time-intensive and often require human curation. In contrast, a renal H-scan is non-invasive, takes only several minutes, and can be performed with a standard ultrasound probe, which clinicians often have experience using.

[0067] Clinical criteria for assessing donor kidney quality are well established, with perhaps the most widely used being the Kidney Donor Profile Index (KDPI). A high KDPI score is thought to correlate with higher levels of chronic damage in the donor kidney, although to date, it has been impossible to correlate these clinical parameters with a comprehensive histologic analysis because a biopsy samples < 1% of the kidney. In contrast, H-scan measurements correlate directly with whole kidney fibrotic burden, one of the most common and important forms of chronic kidney damage. Importantly, fibrosis estimates from a single longitudinal axis H-scan done prior to transplantation were found to be predictive of kidney function 1-year posttransplant, with higher H-scan-derived fibrosis estimates at the time of transplant correlating with poorer allograft function. Since fibrosis is such an importantbiomarker of chronic renal injury, the results point to H-scan as a tool for assessing donor kidney quality that could significantly impact decisions regarding kidney acceptance and allocation.

[0068] In vivo H-scan imaging of the liver, an organ that is located closer to the skin surface and thus more readily accessible to ultrasound imaging, can accurately assess hepatic fibrotic burden in rodent disease models.

[0069] In summary, this study is the first to report an ultrasound-based H-scan technique that measures the fibrotic content of the kidney. The study shows that renal H-scan is a non-invasive and highly accurate method for measuring fibrotic burden in experimental mouse models of disease, human nephrectomy specimens, and human donor kidneys prior to transplant. The study further demonstrates that renal H-scan can identify small differences in matrix content between kidney samples and even within parts of the same kidney. Finally, since fibrosis is an important biomarker of chronic renal damage, we show that renal H-scan estimates of donor kidney fibrotic burden inversely correlate with post-transplant kidney function. These results show that H-scan are useful for quantifying renal fibrotic burden in pre-clinical and human kidney injury models. In the case of human kidney imaging, the study also demonstrates the clinical importance of renal H-scan as an innovative method to rapidly assess donor kidney quality.

[0070] While various embodiments have been described above, it should be understood that such disclosures have been presented by way of example only and are not limiting. Thus, the breadth and scope of the subject compositions and methods should not be limited by any of the above-described exemplary embodiments but should be defined only in accordance with the following claims and their equivalents.

[0071] The above description is for the purpose of teaching the person of ordinary skill in the art how to practice the present invention, and it is not intended to detail all those obvious modifications and variations of it which will become apparent to the skilled worker upon reading the description. It is intended, however, that all such obvious modifications and variations be included within the scope of the present invention, which is defined by the following claims. The claims are intended to cover the components and steps in any sequence which is effective to meet the objectives there intended, unless the context specifically indicates the contrary.

Claims

WHAT IS CLAIMED IS:

1. A method of using an ultrasound scanner to assess fibrotic burden of a body organ, the scanner being of a type that creates a raw radiofrequency signal for each ultrasound scanline and uses data from each such raw radiofrequency signal in generation of an ultrasound image, comprising: a. correcting the data from each scanline for at least one of temperature and ultrasound attenuation caused by scattering and absorption, thereby creating corrected data; b. inputting the corrected data from each scanline into an array of N filters to create a plurality N of convolutions; c. determining a maximum value for each convolution, thereby creating N normalized convolution values representing whether scatterers corresponding to such values are fibrotic or non-fibrotic; and d. displaying, on an ultrasound image, indicia of fibrotic and non-fibrotic scatterers at locations corresponding to the filters that generated the corresponding convolutions.

2. The method of claim 1, wherein the scatterers are cells.

3. The method of claim 1, wherein the scatterers are fibrotic tissue.

4. The method of claim 1, further comprising the step of calculating fibrotic burden by determining, within a region of interest, the ratio of fibrotic scatterers to total scatterers.

5. The method of claim 1, wherein the organ is a kidney.

6. The method of claim 5, wherein the kidney is a human kidney.

7. The method of claim 5, wherein the kidney is an animal kidney.

8. The method of claim 1, wherein each filter is a bandpass Gaussian filter.

9. The method of claim 8, wherein the ultrasound scanner uses a probe with a center frequency and the Gaussian filters in the array have center frequencies ranging from approximately -X% of the probe center frequency to approximately +X% of the probe center frequency.

10. The method of claim 9, wherein X = 70.

11. A method of assessing the fibrotic burden of a human kidney before transplanting it into a recipient, comprising: a. removing the kidney from a donor; b. immediately cooling the kidney to 4°C;c. scanning the kidney using an ultrasound scanner of a type that uses a probe having a center frequency to create a raw radiofrequency signal for each ultrasound scanline and that uses data from such raw radiofrequency signal in the generation of an ultrasound image; d. correcting the data from each scanline for temperature and for ultrasound attenuation caused by scattering and absorption within the kidney to create corrected data; e. inputting the corrected data from each scanline into an array of N filters to create a plurality N of convolutions; f. determining a maximum value for each convolution, thereby creating N normalized convolution values representing whether scatterers corresponding to such values are fibrotic or non-fibrotic; and g. displaying, on an ultrasound image, indicia of fibrotic and non-fibrotic scatterers at locations corresponding to the filters that generated the corresponding convolutions.

12. The method of claim 11, wherein the indicia are colors.

13. The method of claim 11, wherein the filters are bandpass filters.

14. The method of claim 13, wherein the filters are bandpass Gaussian filters.

15. The method of claim 14, wherein the bandpass Gaussian filters in the array have center frequencies ranging from approximately -X% of the probe center frequency to approximately +X% of the probe center frequency.

16. The method of claim 15, wherein X = 70.

17. The method of claim 11, wherein the scanning step comprises scanning along: a. the longest longitudinal plane of the kidney; and b. transversely at the upper pole of the kidney.

18. The method of claim 11, further comprising: a. identifying regions of interest within the kidney; b. segmenting each region of interest into a plurality of attenuation zones, or forming an attenuation zone corresponding to each region of interest; and c. carrying out the correction step on a zoneby zone basis for each region of interest.

19. The method of claim 18, wherein each region of interest is segmented into ten attenuation zones.

20. An ultrasound scanner adapted for assessing fibrotic burden of a kidney, comprising: a. a probe having a center frequency, the probe creating a raw radiofrequency signal for each ultrasound scanline, the raw radiofrequency signal producing data for generating an ultrasound image; b. means for correcting the data from each scanline for temperature and for attenuation caused by depth of scatterers within the kidney to create corrected data; and c. an array of N filters connected to receive the corrected data from each scanline to create a plurality N of convolutions.

21. The scanner of claim 20, wherein the filters are bandpass filters.

22. The scanner of claim 21, wherein the filters are bandpass Gaussian filters.

23. The scanner of claim 22, wherein the bandpass Gaussian filters in the array have center frequencies ranging from approximately -X% of the probe center frequency to approximately +X% of the probe center frequency.

24. The scanner of claim 23, wherein X = 70.

25. A method of using an ultrasound scanner to assess the degree of tissue abnormality in a body organ, the scanner being of a type that creates a raw radiofrequency signal for each ultrasound scanline and uses data from each such raw radiofrequency signal in the generation of an ultrasound image, comprising: a. correcting the data from each scanline for temperature and for ultrasound attenuation caused by scattering and absorption within the body organ, thereby creating corrected data; b. inputting the corrected data from each scanline into an array of N filters to create a plurality N of convolutions; c. determining a maximum value for each convolution, thereby creating N normalized convolution values representing whether tissues corresponding to such values are normal or abnormal; and d. displaying, on an ultrasound image, indicia of normal and abnormal tissues at locations corresponding to the filters that generated the corresponding convolutions.

26. The method of claim 25, wherein the tissue abnormality is evidenced by abnormal scattering tissues.

27. A method of using an ultrasound scanner to assess and predict the function of a kidney, the scanner being of a type that creates a raw radiofrequency signal for each ultrasound scanline and uses data from each such raw radiofrequency signal in generation of an ultrasound image, comprising: a. correcting the data from each scanline for temperature and for attenuation caused by depth of scatterers within the kidney, thereby creating corrected data; b. inputting the corrected data from each scanline into an array of N filters to create a plurality N of convolutions; c. determining a maximum value for each convolution, thereby creating N normalized convolution values representing whether scatterers corresponding to such values are fibrotic or non-fibrotic; d. calculating fibrotic burden by determining, within a region of interest, the ratio of fibrotic scatterers to total scatterers; and e. assessing the functioning of the kidney based upon the calculated fibrotic burden.

28. The method of claim 1, wherein the body organ is ex vivo.

29. A method of using an ultrasound scanner to assess the degree of tissue abnormality in a body organ, the scanner being of a type that creates a raw radiofrequency signal for each ultrasound scanline and uses data from each such raw radiofrequency signal in the generation of an ultrasound image, comprising: a. correcting the data from each scanline for temperature and for ultrasound attenuation caused by scattering and absorption within the body organ, thereby creating corrected data; b. inputting the corrected data from each scanline into an algorithm that associates degree of tissue abnormality with depth within the body; and c. displaying, in an ultrasound image, indicia showing the degree of tissue abnormality within the body at locations corresponding to depth of the tissue within the body.

30. The method of claim 11, further comprising: a. identifying regions of interest within the kidney; b. segmenting each region of interest into a plurality of anatomical zones, or forming an attenuation zone corresponding to each region of interest; andc. carrying out the correction step on a zone by zone basis for each region of interest.

31. The method of claim 11, further comprising: a. identifying regions of interest within the kidney; b. segmenting each anatomical region of interest into a plurality of anatomical zones, or forming an attenuation zone corresponding to each region of interest; and c. carrying out the correction step on a zone by zone basis for each region of interest.

Images