The potential of artificial intelligence-based applications in kidney pathology.

PURPOSE OF REVIEW: The field of pathology is currently undergoing a significant transformation from traditional glass slides to a digital format dependent on whole slide imaging. Transitioning from glass to digital has opened the field to development and application of image analysis technology, commonly deep learning methods (artificial intelligence [AI]) to assist pathologists with tissue examination. Nephropathology is poised to leverage this technology to improve precision, accuracy, and efficiency in clinical practice. RECENT FINDINGS: Through a multidisciplinary approach, nephropathologists, and computer scientists have made significant recent advances in developing AI technology to identify histological structures within whole slide images (segmentation), quantification of histologic structures, prediction of clinical outcomes, and classifying disease. Virtual staining of tissue and automation of electron microscopy imaging are emerging applications with particular significance for nephropathology. SUMMARY: AI applied to image analysis in nephropathology has potential to transform the field by improving diagnostic accuracy and reproducibility, efficiency, and prognostic power. Reimbursement, demonstration of clinical utility, and seamless workflow integration are essential to widespread adoption.

Development and Validation of a Deep Learning Model to Quantify Glomerulosclerosis in Kidney Biopsy Specimens.

Importance: A chronic shortage of donor kidneys is compounded by a high discard rate, and this rate is directly associated with biopsy specimen evaluation, which shows poor reproducibility among pathologists. A deep learning algorithm for measuring percent global glomerulosclerosis (an important predictor of outcome) on images of kidney biopsy specimens could enable pathologists to more reproducibly and accurately quantify percent global glomerulosclerosis, potentially saving organs that would have been discarded. Objective: To compare the performances of pathologists with a deep learning model on quantification of percent global glomerulosclerosis in whole-slide images of donor kidney biopsy specimens, and to determine the potential benefit of a deep learning model on organ discard rates. Design, Setting, and Participants: This prognostic study used whole-slide images acquired from 98 hematoxylin-eosin-stained frozen and 51 permanent donor biopsy specimen sections retrieved from 83 kidneys. Serial annotation by 3 board-certified pathologists served as ground truth for model training and for evaluation. Images of kidney biopsy specimens were obtained from the Washington University database (retrieved between June 2015 and June 2017). Cases were selected randomly from a database of more than 1000 cases to include biopsy specimens representing an equitable distribution within 0% to 5%, 6% to 10%, 11% to 15%, 16% to 20%, and more than 20% global glomerulosclerosis. Main Outcomes and Measures: Correlation coefficient (r) and root-mean-square error (RMSE) with respect to annotations were computed for cross-validated model predictions and on-call pathologists' estimates of percent global glomerulosclerosis when using individual and pooled slide results. Data were analyzed from March 2018 to August 2020. Results: The cross-validated model results of section images retrieved from 83 donor kidneys showed higher correlation with annotations (r = 0.916; 95% CI, 0.886-0.939) than on-call pathologists (r = 0.884; 95% CI, 0.825-0.923) that was enhanced when pooling glomeruli counts from multiple levels (r = 0.933; 95% CI, 0.898-0.956). Model prediction error for single levels (RMSE, 5.631; 95% CI, 4.735-6.517) was 14% lower than on-call pathologists (RMSE, 6.523; 95% CI, 5.191-7.783), improving to 22% with multiple levels (RMSE, 5.094; 95% CI, 3.972-6.301). The model decreased the likelihood of unnecessary organ discard by 37% compared with pathologists. Conclusions and Relevance: The findings of this prognostic study suggest that this deep learning model provided a scalable and robust method to quantify percent global glomerulosclerosis in whole-slide images of donor kidneys. The model performance improved by analyzing multiple levels of a section, surpassing the capacity of pathologists in the time-sensitive setting of examining donor biopsy specimens. The results indicate the potential of a deep learning model to prevent erroneous donor organ discard.

Deep learning quantification of percent steatosis in donor liver biopsy frozen sections.

BACKGROUND: Pathologist evaluation of donor liver biopsies provides information for accepting or discarding potential donor livers. Due to the urgent nature of the decision process, this is regularly performed using frozen sectioning at the time of biopsy. The percent steatosis in a donor liver biopsy correlates with transplant outcome, however there is significant inter- and intra-observer variability in quantifying steatosis, compounded by frozen section artifact. We hypothesized that a deep learning model could identify and quantify steatosis in donor liver biopsies. METHODS: We developed a deep learning convolutional neural network that generates a steatosis probability map from an input whole slide image (WSI) of a hematoxylin and eosin-stained frozen section, and subsequently calculates the percent steatosis. Ninety-six WSI of frozen donor liver sections from our transplant pathology service were annotated for steatosis and used to train (n = 30 WSI) and test (n = 66 WSI) the deep learning model. FINDINGS: The model had good correlation and agreement with the annotation in both the training set (r of 0.88, intraclass correlation coefficient [ICC] of 0.88) and novel input test sets (r = 0.85 and ICC=0.85). These measurements were superior to the estimates of the on-service pathologist at the time of initial evaluation (r = 0.52 and ICC=0.52 for the training set, and r = 0.74 and ICC=0.72 for the test set). INTERPRETATION: Use of this deep learning algorithm could be incorporated into routine pathology workflows for fast, accurate, and reproducible donor liver evaluation. FUNDING: Mid-America Transplant Society.

Resolution of Murine Toxic Hepatic Injury Quantified With Ultrasound Entropy Metrics.

Image-based classification of liver disease generally lacks specificity for distinguishing between acute, resolvable injury and chronic irreversible injury. We propose that ultrasound radiofrequency data acquired in vivo from livers subjected to toxic drug injury can be analyzed with information theoretic detectors to derive entropy metrics, which classify a statistical distribution of pathologic scatterers that dissipate over time as livers heal. Here we exposed 38 C57BL/6 mice to carbon tetrachloride to cause liver damage, and imaged livers in vivo 1, 4, 8, 12 and 18 d after exposure with a broadband 15-MHz probe. Selected entropy metrics manifested monotonic recovery to normal values over time as livers healed, and were correlated directly with progressive restoration of liver architecture by histologic assessment (r2 ≥ 0.95, p < 0.004). Thus, recovery of normal liver microarchitecture after toxic exposure can be delineated sensitively with entropy metrics.

Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections.

Transplantable kidneys are in very limited supply. Accurate viability assessment prior to transplantation could minimize organ discard. Rapid and accurate evaluation of intra-operative donor kidney biopsies is essential for determining which kidneys are eligible for transplantation. The criterion for accepting or rejecting donor kidneys relies heavily on pathologist determination of the percent of glomeruli (determined from a frozen section) that are normal and sclerotic. This percentage is a critical measurement that correlates with transplant outcome. Inter- and intra-observer variability in donor biopsy evaluation is, however, significant. An automated method for determination of percent global glomerulosclerosis could prove useful in decreasing evaluation variability, increasing throughput, and easing the burden on pathologists. Here, we describe the development of a deep learning model that identifies and classifies non-sclerosed and sclerosed glomeruli in whole-slide images of donor kidney frozen section biopsies. This model extends a convolutional neural network (CNN) pre-trained on a large database of digital images. The extended model, when trained on just 48 whole slide images, exhibits slide-level evaluation performance on par with expert renal pathologists. Encouragingly, the model's performance is robust to slide preparation artifacts associated with frozen section preparation. The model substantially outperforms a model trained on image patches of isolated glomeruli, in terms of both accuracy and speed. The methodology overcomes the technical challenge of applying a pretrained CNN bottleneck model to whole-slide image classification. The traditional patch-based approach, while exhibiting deceptively good performance classifying isolated patches, does not translate successfully to whole-slide image segmentation in this setting. As the first model reported that identifies and classifies normal and sclerotic glomeruli in frozen kidney sections, and thus the first model reported in the literature relevant to kidney transplantation, it may become an essential part of donor kidney biopsy evaluation in the clinical setting.

Antithrombin nanoparticles inhibit stent thrombosis in ex vivo static and flow models.

OBJECTIVE: Despite significant advances in intravascular stent technology, safe prevention of stent thrombosis over prolonged periods after initial deployment persists as a medical need to decrease device failure. The objective of this project was to assess the potential of perfluorocarbon nanoparticles (NP) conjugated with the direct thrombin inhibitor D-phenylalanyl-L-prolyl-L-arginyl chloromethylketone (PPACK-NP) to inhibit stent thrombosis. METHODS: In a static model of stent thrombosis, 3 × 3-mm pieces of stainless steel coronary stents were cut and adsorbed with thrombin to create a procoagulant surface that would facilitate thrombus development. After treatment with PPACK-NP or control NP, stents were exposed to platelet-poor plasma (PPP) or platelet-rich plasma (PRP) for set time points up to 60 minutes. Measurements of final clot weight in grams were used for assessing the effect of NP treatment on limiting thrombosis. Additionally, groups of stents were exposed to flowing plasma containing various treatments (saline, free PPACK, control NP, and PPACK-NP) and generated thrombi were stained and imaged to investigate the treatment effects of PPACK-NP under flow conditions. RESULTS: The static model of stent thrombosis used in this study indicated a significant reduction in thrombus deposition with PPACK-NP treatment (0.00067 ± 0.00026 g; n = 3) compared with control NP (0.0098 ± 0.0015 g; n = 3; P = .026) in PPP. Exposure to PRP demonstrated similar effects with PPACK-NP treatment (0.00033 ± 0.00012 g; n = 3) vs control NP treatment (0.0045 ± 0.00012 g; n = 3; P = .000017). In additional studies, stents were exposed to both PRP pretreated with vorapaxar and PPACK-NP, which illustrated adjunctive benefit to oral platelet inhibitors for prevention of stent thrombosis. Additionally, an in vitro model of stent thrombosis under flow conditions established that PPACK-NP treatment inhibited thrombus deposition on stents significantly. CONCLUSIONS: This study demonstrates that antithrombin perfluorocarbon NPs exert marked focal antithrombin activity to prevent intravascular stent thrombosis and occlusion.

Delivery of a Protease-Activated Cytolytic Peptide Prodrug by Perfluorocarbon Nanoparticles.

Melittin is a cytolytic peptide derived from bee venom that inserts into lipid membranes and oligomerizes to form membrane pores. Although this peptide is an attractive candidate for treatment of cancers and infectious processes, its nonspecific cytotoxicity and hemolytic activity have limited its therapeutic applications. Several groups have reported the development of cytolytic peptide prodrugs that only exhibit cytotoxicity following activation by site-specific proteases. However, systemic administration of these constructs has proven difficult because of their poor pharmacokinetic properties. Here, we present a platform for the design of protease-activated melittin derivatives that may be used in conjunction with a perfluorocarbon nanoparticle delivery system. Although native melittin was substantially hemolytic (HD50: 1.9 µM) and cytotoxic (IC50: 2.4 µM), the prodrug exhibited 2 orders of magnitude less hemolytic activity (HD50: > 100 µM) and cytotoxicity (IC50: > 100 µM). Incubation with matrix metalloproteinase-9 (MMP-9) led to cleavage of the prodrug at the expected site and restoration of hemolytic activity (HD50: 3.4 µM) and cytotoxicity (IC50: 8.1 µM). Incubation of the prodrug with perfluorocarbon nanoparticles led to stable loading of 10,250 peptides per nanoparticle. Nanoparticle-bound prodrug was also cleaved and activated by MMP-9, albeit at a fourfold slower rate. Intravenous administration of prodrug-loaded nanoparticles in a mouse model of melanoma significantly decreased tumor growth rate (p = 0.01). Because MMPs and other proteases play a key role in cancer invasion and metastasis, this platform holds promise for the development of personalized cancer therapies directed toward a patient's individual protease expression profile.

Entropy vs. Energy Waveform Processing: A Comparison Based on the Heat Equation.

Virtually all modern imaging devices collect electromagnetic or acoustic waves and use the energy carried by these waves to determine pixel values to create what is basically an "energy" picture. However, waves also carry "information", as quantified by some form of entropy, and this may also be used to produce an "information" image. Numerous published studies have demonstrated the advantages of entropy, or "information imaging", over conventional methods. The most sensitive information measure appears to be the joint entropy of the collected wave and a reference signal. The sensitivity of repeated experimental observations of a slowly-changing quantity may be defined as the mean variation (i.e., observed change) divided by mean variance (i.e., noise). Wiener integration permits computation of the required mean values and variances as solutions to the heat equation, permitting estimation of their relative magnitudes. There always exists a reference, such that joint entropy has larger variation and smaller variance than the corresponding quantities for signal energy, matching observations of several studies. Moreover, a general prescription for finding an "optimal" reference for the joint entropy emerges, which also has been validated in several studies.

Rapamycin nanoparticles target defective autophagy in muscular dystrophy to enhance both strength and cardiac function.

Duchenne muscular dystrophy in boys progresses rapidly to severe impairment of muscle function and death in the second or third decade of life. Current supportive therapy with corticosteroids results in a modest increase in strength as a consequence of a general reduction in inflammation, albeit with potential untoward long-term side effects and ultimate failure of the agent to maintain strength. Here, we demonstrate that alternative approaches that rescue defective autophagy in mdx mice, a model of Duchenne muscular dystrophy, with the use of rapamycin-loaded nanoparticles induce a reproducible increase in both skeletal muscle strength and cardiac contractile performance that is not achievable with conventional oral rapamycin, even in pharmacological doses. This increase in physical performance occurs in both young and adult mice, and, surprisingly, even in aged wild-type mice, which sets the stage for consideration of systemic therapies to facilitate improved cell function by autophagic disposal of toxic byproducts of cell death and regeneration.

Programmable nanoparticle functionalization for in vivo targeting.

The emerging demand for programmable functionalization of existing base nanocarriers necessitates development of an efficient approach for cargo loading that avoids nanoparticle redesign for each individual application. Herein, we demonstrate in vivo a postformulation strategy for lipidic nanocarrier functionalization with the use of a linker peptide, which rapidly and stably integrates cargos into lipidic membranes of nanocarriers after simple mixing through a self-assembling process. We exemplified this strategy by generating a VCAM-1-targeted perfluorocarbon nanoparticle for in vivo targeting in atherosclerosis (ApoE-deficient) and breast cancer (STAT-1-deficient) models. In the atherosclerotic model, a 4.1-fold augmentation in binding to affected aortas was observed for targeted vs. nontargeted nanoparticles (P<0.0298). Likewise, in the breast cancer model, a 4.9-fold increase in the nanoparticle signal from tumor vasculature was observed for targeted vs. nontargeted nanoparticles (P<0.0216). In each case, the nanoparticle was registered with fluorine ((19)F) magnetic resonance spectroscopy of the nanoparticle perfluorocarbon core, yielding a quantitative estimate of the number of tissue-bound nanoparticles. Because other common nanocarriers with lipid coatings (e.g., liposomes, micelles, etc.) can employ this strategy, this peptide linker postformulation approach is applicable to more than half of the available nanosystems currently in clinical trials or clinical uses.

Postformulation peptide drug loading of nanostructures.

Cytolytic peptides have commanded attention for their anticancer potential because the membrane-disrupting function that produces cell death is less likely to be overcome by resistant mutations. Congruently, peptides that are involved in molecular recognition and biological activities become attractive therapeutic candidates because of their high specificity, better affinity, reduced immunogenicity, and reduced off target toxicity. However, problems of inadequate delivery, rapid deactivation in vivo, and poor bioavailability have limited clinical application. Therefore, peptide drug development for clinical use requires an appropriate combination of an effective therapeutic peptide and a robust delivery methodology. In this chapter, we describe methods for the postformulation insertion of peptide drugs into lipidic nanostructures, the physical characterization of peptide-nanostructure complexes, and the evaluation of their therapeutic effectiveness both in vitro and in vivo.

Use of smoothing splines for analysis of backscattered ultrasonic waveforms: application to monitoring of steroid treatment of dystrophic mice.

Duchenne muscular dystrophy (DMD) is an X-linked genetic disease characterized by progressive weakness and wasting of skeletal and cardiac muscle; boys present with weakness by the age of 5 years and, if left untreated, are unable to walk without assistance by the age of 10 years. Therapy for DMD has been primarily palliative, with oral steroids emerging as a first-line approach even though this treatment has serious side-effects. Consequently, low-cost imaging technology suitable for improved diagnosis and treatment monitoring of DMD would be of great value, especially in remote and underserved areas. Previously, we reported use of the logarithm of the signal energy, log [E(f)], and a new method for ultrasound signal characterization using entropy, H(f), to monitor prednisolone treatment of skeletal muscle in a dystrophin-deficient mouse model. Three groups were studied: mdx mice treated with prednisolone, a control group of mdx mice treated with saline, and a control group of wild-type mice treated with saline. It was found that both log [E(f)] and H(f) were required to statistically differentiate the three groups. In the current study, we show that preprocessing of the raw ultrasound using optimal smoothing splines before computation of either log [E(f)] or a rapidly computable variant of Hf, denoted I(f,∞), permits delineation of all three groups by either metric alone. This opens the way to the ultimate goal of this study, which is identification and implementation of new diagnostically sensitive algorithms on the new generation of low-cost hand-held clinical ultrasonic imaging systems.

Synthesis of NanoQ, a copper-based contrast agent for high-resolution magnetic resonance imaging characterization of human thrombus.

A new site-targeted molecular imaging contrast agent based on a nanocolloidal suspension of lipid-encapsulated, organically soluble divalent copper has been developed. Concentrating a high payload of divalent copper ions per nanoparticle, this agent provides a high per-particle r1 relaxivity, allowing sensitive detection in T1-weighted magnetic resonance imaging when targeted to fibrin clots in vitro. The particle also exhibits a defined clearance and safety profile in vivo.

A fibrin-specific thrombolytic nanomedicine approach to acute ischemic stroke.

AIM: To develop a fibrin-specific urokinase nanomedicine thrombolytic agent. MATERIALS & METHODS: In vitro fibrin-clot dissolution studies were utilized to develop and characterize simultaneous coupling and loading of anti-fibrin monoclonal antibody and urokinase onto perfluorocarbon nanoparticle (NP) surface. In vivo pharmacokinetics and fibrin-specific targeting of the nanolytic agent was studied in dogs. RESULTS: Simultaneous coupling of up to 40 anti-fibrin antibodies and 400 urokinase enzymes per perfluorocarbon NP produced an effective targeted nanolytic agent with no significant surface protein-protein interference. Fibrin clot dissolution was not improved by increasing homing capacity from 10 to 40 antibodies/NP, but increasing enzymatic payload from 100 to 400/NP resulted in maximized lytic effect. Fluorescent microscopy showed that rhodamine-labeled urokinase nanoparticles densely decorated the intraluminal thrombus in canine clots in vivo analogous to the fibrin pattern, while an irrelevant-targeted agent had negligible binding. CONCLUSION: This agent offers a vascularly constrained, simple to administer, low-dose nanomedicine approach that may present an attractive alternative for treating acute stroke victims.

Application of a real-time, calculable limiting form of the Renyi entropy for molecular imaging of tumors.

Previously, we reported new methods for ultrasound signal characterization using entropy, H(f); a generalized entropy, the Renyi entropy, I(f)(r); and a limiting form of Renyi entropy suitable for real-time calculation, I(f),(infinity). All of these quantities demonstrated significantly more sensitivity to subtle changes in scattering architecture than energy-based methods in certain settings. In this study, the real-time calculable limit of the Renyi entropy, I(f),(infinity), is applied for the imaging of angiogenic murine neovasculature in a breast cancer xenograft using a targeted contrast agent. It is shown that this approach may be used to reliably detect the accumulation of targeted nanoparticles at five minutes post-injection in this in vivo model.

Lipid membrane editing with peptide cargo linkers in cells and synthetic nanostructures.

Current strategies for deploying synthetic nanocarriers involve the creation of agents that incorporate targeting ligands, imaging agents, and/or therapeutic drugs into particles as an integral part of the formulation process. Here we report the development of an amphipathic peptide linker that enables postformulation editing of payloads without the need for reformulation to achieve multiplexing capability for lipidic nanocarriers. To exemplify the flexibility of this peptide linker strategy, 3 applications were demonstrated: converting nontargeted nanoparticles into targeting vehicles; adding cargo to preformulated targeted nanoparticles for in vivo site-specific delivery; and labeling living cells for in vivo tracking. This strategy is expected to enhance the clinical application of molecular imaging and/or targeted therapeutic agents by offering extended flexibility for multiplexing targeting ligands and/or drug payloads that can be selected after base nanocarrier formulation.

Molecularly targeted nanocarriers deliver the cytolytic peptide melittin specifically to tumor cells in mice, reducing tumor growth.

The in vivo application of cytolytic peptides for cancer therapeutics is hampered by toxicity, nonspecificity, and degradation. We previously developed a specific strategy to synthesize a nanoscale delivery vehicle for cytolytic peptides by incorporating the nonspecific amphipathic cytolytic peptide melittin into the outer lipid monolayer of a perfluorocarbon nanoparticle. Here, we have demonstrated that the favorable pharmacokinetics of this nanocarrier allows accumulation of melittin in murine tumors in vivo and a dramatic reduction in tumor growth without any apparent signs of toxicity. Furthermore, direct assays demonstrated that molecularly targeted nanocarriers selectively delivered melittin to multiple tumor targets, including endothelial and cancer cells, through a hemifusion mechanism. In cells, this hemifusion and transfer process did not disrupt the surface membrane but did trigger apoptosis and in animals caused regression of precancerous dysplastic lesions. Collectively, these data suggest that the ability to restrain the wide-spectrum lytic potential of a potent cytolytic peptide in a nanovehicle, combined with the flexibility of passive or active molecular targeting, represents an innovative molecular design for chemotherapy with broad-spectrum cytolytic peptides for the treatment of cancer at multiple stages.

Sensitive ultrasonic delineation of steroid treatment in living dystrophic mice with energy-based and entropy-based radio frequency signal processing.

Duchenne muscular dystrophy is a severe wasting disease, involving replacement of necrotic muscle tissue by fibrous material and fatty infiltrates. One primary animal model of this human disease is the X chromosome-linked mdx strain of mice. The goals of the present work were to validate and quantify the capability of both energy and entropy metrics of radio-frequency ultrasonic backscatter to differentiate among normal, dystrophic, and steroid-treated skeletal muscle in the mdx model. Thirteen 12-month-old mice were blocked into three groups: 4 treated mdx-dystrophic that received daily subcutaneous steroid (prednisolone) treatment for 14 days, 4 positive-control mdx-dystrophic that received saline injections for 14 days, and 5 negative-control animals. Biceps muscle of each animal was imaged in vivo using a 40-MHz center frequency transducer in conjunction with a Vevo-660 ultrasound system. Radio-frequency data were acquired (1 GHz, 8 bits) corresponding to a sequence of transverse images, advancing the transducer from "shoulder" to "elbow" in 100-micron steps. Data were processed to generate both "integrated backscatter" (log energy), and "entropy" (information theoretic receiver, H(f)) representations. Analyses of the integrated-backscatter values delineated both treated-and untreated-mdx biceps from normal controls (p < 0.01). Complementary analyses of the entropy images differentiated the steroid-treated and positive-control mdx groups (p < 0.01). To our knowledge, this study represents the first reported use of quantitative ultrasonic characterization of skeletal muscle in mdx mice. Successful differentiation among dystrophic, steroid-treated, and normal tissues suggests the potential for local noninvasive monitoring of disease severity and therapeutic effects.

Sensitive ultrasonic detection of dystrophic skeletal muscle in patients with duchenne muscular dystrophy using an entropy-based signal receiver.

The dystrophinopathies comprise a group of X-linked genetic diseases that feature dystrophin deficiency. Duchenne and Becker muscular dystrophy are characterized by progressive weakness and wasting of skeletal, smooth, and/or cardiac muscle. Duchenne muscular dystrophy (DMD) is the most severe dystrophinopathy, with an incidence of 1:3500 male births. Despite understanding the structural and genetic basis for DMD, the pathogenesis and clinical basis for more severe involvement in specific skeletal muscle groups and the heart are poorly understood. Current techniques, such as strength testing for monitoring progress of disease and therapy in DMD patients, are imprecise and physically demanding for test subjects. Ultrasound is well-suited to detect changes in structure and organization in muscle tissue in a manner that makes low demands on the patient. Therefore, we investigated the use of ultrasound to quantitatively phenotype the remodeling process in patients with DMD. Beam-formed radio-frequency (RF) data were acquired from the skeletal muscles of nine DMD and five normal subjects imaged with a clinical imaging system (HDI5000 w/7 MHz probe applied above left biceps muscle). From these data, images were reconstructed using B-mode (log of analytic signal magnitude) and information-theoretic receivers (H(f)-receiver). H(f) images obtained from dystrophic muscle contained extensive "mottled" regions (i.e., areas with heterogeneous image contrast) that were not readily apparent from the B-Mode images. The 2-D autocorrelation of DMD H(f) images have broader peaks than those of normal subjects, which is indicative of larger scatterer sizes, consistent with pathologic changes of fibers, edema and fatty infiltration. Comparison of the relative peak widths (full width measured at 60% maximum) of the autocorrelation of the DMD and normal H(f) images shows a quantitative difference between the two groups (p < 0.005, student two-tailed paired t-test). Consequently, these imaging techniques may prove useful for longitudinal monitoring of disease progression and therapy.