Sporadic Creutzfeldt-Jakob disease in adults over 80 years: a 10-year review of United Kingdom surveillance

INTRODUCTION

Sporadic Creutzfeldt-Jakob disease (sCJD) is a rapidly progressive neurodegenerative disease with public health implications. Mean age of onset is 68 years. Age-specific incidence declines after 80 years. This may arise from under-ascertainment or other biological features of the disease. Accurate characterisation of late-onset sCJD is important for early diagnosis, avoiding unnecessary investigations and improving ascertainment for public health purposes.

OBJECTIVE

To phenotype the clinical features and investigation profile of sCJD in adults >80 years.

METHODS

We analysed all probable and definite sCJD cases identified by the UK National CJD Research & Surveillance Unit over a 10-year period (2011-2021). Individuals were grouped by age of onset. Clinical features and investigation profiles were compared.

RESULTS

10.3% (123/1196) had an age of onset over 80. Median survival was shorter (3.2 vs 4.3 months; P < 0.001). Pyramidal signs (48.3% vs 34.2%; P = 0.008) and akinetic mutism (55.1% vs 33.2%; P < 0.001) were more frequent. Psychiatric symptoms (26.3% vs 39.6%; P = 0.01) and cerebellar signs (65.4% vs 78.6%, P = 0.007) were less frequent. Cognitive impairment and myoclonus were highly prevalent regardless of age. Between age groups, the diagnostic sensitivity of cerebrospinal fluid real-time quaking-induced conversion (CSF RT-QuIC) (92.9% vs 91.9%, P = 0.74) was comparable, electroencephalography was superior (41.5% vs 25.4%; P = 0.006) and MRI was inferior (67.8% vs 91.4%; P < 0.001).

CONCLUSIONS

Late-onset sCJD has distinct clinical features, shorter survival and a different profile of investigation sensitivity. CSF RT-QuIC, MRI brain and specialist CJD review is recommended in older adults with a rapidly progressive neurological disorder. Autopsy is valuable when the cause remains elusive.

Characterisation of RT-QuIC negative cases from the UK National CJD Research and Surveillance programme

INTRODUCTION

Incorporation of the real-time quaking-induced conversion (RT-QuIC) assays for diagnosis of sporadic Creutzfeldt-Jakob disease (CJD) has transformed diagnosis largely related to its extremely high specificity. However, the test has a c.10% false-negative result and we aim to characterize the clinical features, investigation profile, and molecular subtype in this cohort of patients.

METHODS

250 individuals diagnosed with definite sporadic CJD were identified from the UK National CJD Research and Surveillance Unit from 2012 to 2023. We compared the clinical features and investigation profile in those with a negative CSF RT-QuIC to those with a positive RT-QuIC.

RESULTS

27 individuals (10.8%) were CSF RT-QuIC negative. Median age of onset was younger (62 years vs 68 years, p = 0.002), median disease duration was longer (4.4 months vs 10.5 months, p < 0.001), and these individuals were less likely to present with gait difficulties (73% vs 93%, p = 0.003) or motor symptoms (62% vs 80%, p = 0.04). The sensitivity of electroencephalography and diffusion-weighted MRI were similar in both groups. In those who were RT-QuIC negative, there was an overrepresentation of the VV1 (32% vs 1%) and MM2 molecular subtypes (21% vs 3%). Co-occurring neurodegenerative disease was found in 33% (9/27) of those who were RT-QuIC negative.

CONCLUSIONS

Individuals with sporadic CJD and a negative CSF RT-QuIC present with younger age of onset, different clinical features and are over-represented with the VV1 and MM2 subtypes of sporadic CJD. Further work is required to better understand the biochemical properties contributing to RT-QuIC negative results in these cases.

Revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomography

BACKGROUND

In vivo imaging of the human retina using adaptive optics optical coherence tomography (AO-OCT) has transformed medical imaging by enabling visualization of 3D retinal structures at cellular-scale resolution, including the retinal pigment epithelial (RPE) cells, which are essential for maintaining visual function. However, because noise inherent to the imaging process (e.g., speckle) makes it difficult to visualize RPE cells from a single volume acquisition, a large number of 3D volumes are typically averaged to improve contrast, substantially increasing the acquisition duration and reducing the overall imaging throughput.

METHODS

Here, we introduce parallel discriminator generative adversarial network (P-GAN), an artificial intelligence (AI) method designed to recover speckle-obscured cellular features from a single AO-OCT volume, circumventing the need for acquiring a large number of volumes for averaging. The combination of two parallel discriminators in P-GAN provides additional feedback to the generator to more faithfully recover both local and global cellular structures. Imaging data from 8 eyes of 7 participants were used in this study.

RESULTS

We show that P-GAN not only improves RPE cell contrast by 3.5-fold, but also improves the end-to-end time required to visualize RPE cells by 99-fold, thereby enabling large-scale imaging of cells in the living human eye. RPE cell spacing measured across a large set of AI recovered images from 3 participants were in agreement with expected normative ranges.

CONCLUSIONS

The results demonstrate the potential of AI assisted imaging in overcoming a key limitation of RPE imaging and making it more accessible in a routine clinical setting.

Creation and Implementation of an Interdisciplinary Workflow for CBCT-Based Online Adaptive Radiotherapy

PURPOSE/OBJECTIVE(S)

CBCT-based online adaptive radiotherapy (OART) is an emerging treatment strategy to replan based on the anatomy of the day while the patient remains on the couch. OART is not just an add-on to the current workflow; it necessitates a new approach across the patient's path of care, from CT simulation to treatment delivery. OART requires the addition of duties to clinical personnel, strategies to create auto-plan templates, and monitoring the "black box" adaptation process. Studies have shown that OART implementation is limited by its resource-intensive nature and the risks associated with the treatment approach. We hypothesized that the implementation of an interdisciplinary, streamlined workflow and checklists would enhance the OART treatment efficiency, prevent medical errors from the adaptation, and minimize the burden on clinicians.

MATERIALS/METHODS

An interdisciplinary OART working group comprising radiation oncologists, medical physicists, dosimetrists, and therapists was created to enable weekly knowledge sharing, workflow design, implementation, and continuous process improvement. 213 adaptive sessions from 5 treatment sites (pancreas, bladder, prostate, rectum, anus) were treated on a CBCT-based OART platform in a single institutional study. An evaluation of the treatment safety and workflow time was performed for each adaptive session.

RESULTS

The OART workflow was divided into four sub-workflows: 1) pre-treatment site-specific template preparation, 2) pre-treatment initial planning and verification, 3) on-treatment procedure, and 4) post-treatment evaluation. The sub-processes involved 4, 8, 13, and 4 separate, sequentially tasks, respectively, and a total of 11 task checklists. The template preparation is a new process developed for site-specific, standardized physician template directives, automated planning template development, and testing for its accuracy and robustness. The planning templates generated high-quality initial plans automatically within minutes once structures were segmented on the planning CT. This process was replicated during treatment using the CBCT. The median (interquartile range) online procedure time, defined as the time from initial CBCT to plan approval, of the five treatment sites (pancreas, bladder, prostate, rectum, anus) was 22.1 (19.2-24.8) min, 16.5 (15.3-17.5) min, 14.7 (13.9-17.4) min, 17 (15.3-19.7) min, and 24 (21.4-25.8) min, respectively. Safety assessment determined that no treatment deviations were observed.

CONCLUSION

Creating an interdisciplinary, standardized workflow and checklists allowed the safe delivery of OART with clinically feasible online procedure time and significantly reduced initial planning time compared with traditional EBRT. The unique workflow is essential to minimize the burden on the care team, increase patient safety, and access to OART.

Energetic Electron Precipitation Driven by Electromagnetic Ion Cyclotron Waves from ELFIN's Low Altitude Perspective

We review comprehensive observations of electromagnetic ion cyclotron (EMIC) wave-driven energetic electron precipitation using data collected by the energetic electron detector on the Electron Losses and Fields InvestigatioN (ELFIN) mission, two polar-orbiting low-altitude spinning CubeSats, measuring 50-5000 keV electrons with good pitch-angle and energy resolution. EMIC wave-driven precipitation exhibits a distinct signature in energy-spectrograms of the precipitating-to-trapped flux ratio: peaks at >0.5 MeV which are abrupt (bursty) (lasting ∼17 s, or Δ L ∼ 0.56 ) with significant substructure (occasionally down to sub-second timescale). We attribute the bursty nature of the precipitation to the spatial extent and structuredness of the wave field at the equator. Multiple ELFIN passes over the same MLT sector allow us to study the spatial and temporal evolution of the EMIC wave - electron interaction region. Case studies employing conjugate ground-based or equatorial observations of the EMIC waves reveal that the energy of moderate and strong precipitation at ELFIN approximately agrees with theoretical expectations for cyclotron resonant interactions in a cold plasma. Using multiple years of ELFIN data uniformly distributed in local time, we assemble a statistical database of ∼50 events of strong EMIC wave-driven precipitation. Most reside at L ∼ 5 - 7 at dusk, while a smaller subset exists at L ∼ 8 - 12 at post-midnight. The energies of the peak-precipitation ratio and of the half-peak precipitation ratio (our proxy for the minimum resonance energy) exhibit an L -shell dependence in good agreement with theoretical estimates based on prior statistical observations of EMIC wave power spectra. The precipitation ratio's spectral shape for the most intense events has an exponential falloff away from the peak (i.e., on either side of ∼ 1.45 MeV). It too agrees well with quasi-linear diffusion theory based on prior statistics of wave spectra. It should be noted though that this diffusive treatment likely includes effects from nonlinear resonant interactions (especially at high energies) and nonresonant effects from sharp wave packet edges (at low energies). Sub-MeV electron precipitation observed concurrently with strong EMIC wave-driven >1 MeV precipitation has a spectral shape that is consistent with efficient pitch-angle scattering down to ∼ 200-300 keV by much less intense higher frequency EMIC waves at dusk (where such waves are most frequent). At ∼100 keV, whistler-mode chorus may be implicated in concurrent precipitation. These results confirm the critical role of EMIC waves in driving relativistic electron losses. Nonlinear effects may abound and require further investigation.

Sporadic Creutzfeldt-Jakob Disease in the young (50 and below): 10-year review of United Kingdom surveillance

INTRODUCTION

Sporadic Creutzfeldt-Jakob Disease (sCJD) is the commonest human prion disease, with a median age of onset of 68 years. We characterise the clinical, investigation, and neuropathological features in young individuals with sCJD using data from UK national CJD surveillance.

METHODS

Referrals between 2011 and 2021 were examined, with definite (post-mortem confirmed) or probable sCJD cases included. Clinical features, MRI, EEG, CSF RT-QuIC, 14-3-3, PRNP sequencing and neuropathological findings were examined. We compared younger (≤ 50 years age of onset) with older individuals. Records of Non-sCJD referrals were also reviewed.

RESULTS

46 (4%) young individuals were identified (age at onset 25-50) from 1178 cases. 15 (33%) were autopsy confirmed. Psychiatric disturbance (37% vs 22%, p = 0.02) and headache (11% vs 3%, p = 0.01) at presentation, and longer disease duration (by 1.45 months, 95% CI 0.43-2.79, logrank p = 0.007) were commoner. CSF RT-QuIC showed lower sensitivity (82% vs 93%, p = 0.02). There was no difference in sensitivity of MR brain or CSF 14-3-3. There were no significant co-pathologies in autopsy-confirmed cases. For non-sCJD referrals, 41 cases were of other CJD subtypes, and 7 non-prion diagnoses.

CONCLUSIONS

Young-onset sCJD is more likely to present with neuropsychiatric symptoms and headache, longer disease duration, and lower sensitivity of RT-QuIC. These findings may be driven by the underlying molecular subtypes. Our results guide the evaluation of younger individuals presenting with rapidly progressive cognitive, neuropsychiatric, and motor decline, and emphasise the need for additional vigilance for atypical features by clinicians and CJD surveillance programmes worldwide.

Extracting spacing-derived estimates of rod density in healthy retinae

Quantification of the rod photoreceptor mosaic using adaptive optics scanning light ophthalmoscopy (AOSLO) remains challenging. Here we demonstrate a method for deriving estimates of rod density and rod:cone ratio based on measures of rod spacing, cone numerosity, and cone inner segment area. Twenty-two AOSLO images with complete rod visualization were used to validate this spacing-derived method for estimating density. The method was then used to estimate rod metrics in an additional 105 images without complete rod visualization. The spacing-derived rod mosaic metrics were comparable to published data from histology. This method could be leveraged to develop large normative databases of rod mosaic metrics, though limitations persist with intergrader variability in assessing cone area and numerosity.

Visualization of erythrocyte stasis in the living human eye in health and disease

Blood cells trapped in stasis have been reported within the microcirculation, but their relevance to health and disease has not been established. In this study, we introduce an in vivo imaging approach that reveals the presence of a previously-unknown pool of erythrocytes in stasis, located within capillary segments of the CNS, and present in 100% of subjects imaged. These results provide a key insight that blood cells pause as they travel through the choroidal microvasculature, a vascular structure that boasts the highest blood flow of any tissue in the body. Demonstration of clinical utility using deep learning reveals that erythrocyte stasis is altered in glaucoma, indicating the possibility of more widespread changes in choroidal microvascular than previously realized. The ability to monitor the choroidal microvasculature at the single cell level may lead to novel strategies for tracking microvascular health in glaucoma, age-related macular degeneration, and other neurodegenerative diseases.

046 Sporadic Creutzfeldt-Jakob disease in the young: a 10 year review of United Kingdom national surveillance

Introduction

Sporadic Creutzfeldt-Jakob Disease (sCJD) is the commonest human prion disease, with a median age of onset of 68 years. We aimed to characterise the clinical, investigation, and neuropatho- logical features in young individuals with sCJD using data derived from national CJD surveillance.

Methods

We interrogated the NCJDRSU database for individuals diagnosed with definite (post-mortem confirmed) or probable sCJD assessed from 2011-2021. We extracted data on clinical features, MRI, EEG, RT-QuIC, 14-3-3, and PRNP sequencing. Neuropathological findings were also studied where available. This data was compared in young individuals (≤50y) compared to all others.

Results

47 young individuals (4%) were identified (age at onset 25-50) from a total of 1178 cases. 14 (29.8%) were autopsy-confirmed. Psychiatric disturbance at presentation (36.2% vs 22%, p = 0.03) and longer disease duration (by 46 days, 95% CI 15-88, p < 0.01) were commoner in this group. CSF RT-QuIC showed lower sensitivity (81% vs 94%, p=0.01) in younger individuals. There was no difference in the sensi- tivity of MR brain, CSF 14-3-3, or EEG. There were no significant neuropathological differences in autopsy confirmed cases.

Conclusions

Younger individuals with sCJD are more likely to present with neuropsychiatric symptoms, longer disease duration, and lower sensitivity of RT-QuIC.

Photoreceptor and Retinal Pigment Epithelium Relationships in Eyes With Vitelliform Macular Dystrophy Revealed by Multimodal Adaptive Optics Imaging

Purpose

To assess the structure of cone photoreceptors and retinal pigment epithelial (RPE) cells in vitelliform macular dystrophy (VMD) arising from various genetic etiologies.

Methods

Multimodal adaptive optics (AO) imaging was performed in 11 patients with VMD using a custom-assembled instrument. Non-confocal split detection and AO-enhanced indocyanine green were used to visualize the cone photoreceptor and RPE mosaics, respectively. Cone and RPE densities were measured and compared across BEST1-, PRPH2-, IMPG1-, and IMPG2-related VMD.

Results

Within macular lesions associated with VMD, both cone and RPE densities were reduced below normal, to 37% of normal cone density (eccentricity 0.2 mm) and to 8.4% of normal RPE density (eccentricity 0.5 mm). Outside of lesions, cone and RPE densities were slightly reduced (both to 92% of normal values), but with high degree of variability in the individual measurements. Comparison of juxtalesional cone and RPE measurements (<1 mm from the lesion edge) revealed significant differences in RPE density across the four genes (P < 0.05). Overall, cones were affected to a greater extent than RPE in patients with IMPG1 and IMPG2 pathogenic variants, but RPE was affected more than cones in BEST1 and PRPH2 VMD. This trend was observed even in contralateral eyes from a subset of five patients who presented with macular lesions in only one eye.

Conclusions

Assessment of cones and RPE in retinal locations outside of the macular lesions reveals a pattern of cone and RPE disruption that appears to be gene dependent in VMD. These findings provide insight into the cellular pathogenesis of disease in VMD.

Visualizing retinal cells with adaptive optics imaging modalities using a translational imaging framework

Adaptive optics reflectance-based retinal imaging has proved a valuable tool for the noninvasive visualization of cells in the living human retina. Many subcellular features that remain at or below the resolution limit of current in vivo techniques may be more easily visualized with the same modalities in an ex vivo setting. While most microscopy techniques provide significantly higher resolution, enabling the visualization of fine cellular detail in ex vivo retinal samples, they do not replicate the reflectance-based imaging modalities of in vivo retinal imaging. Here, we introduce a strategy for imaging ex vivo samples using the same imaging modalities as those used for in vivo retinal imaging, but with increased resolution. We also demonstrate the ability of this approach to perform protein-specific fluorescence imaging and reflectance imaging simultaneously, enabling the visualization of nearly transparent layers of the retina and the classification of cone photoreceptor types.

Automated Iterative Label Transfer Improves Segmentation of Noisy Cells in Adaptive Optics Retinal Images

High quality data labeling is essential for improving the accuracy of deep learning applications in medical imaging. However, noisy images are not only under-represented in training datasets, but also, labeling of noisy data is low quality. Unfortunately, noisy images with poor quality labels are exacerbated by traditional data augmentation strategies. Real world images contain noise and can lead to unexpected drops in algorithm performance. In this paper, we present a non-traditional, purposeful data augmentation method to specifically transfer high quality automated labels into noisy image regions for incorporation into the training dataset. The overall approach is based on the use of paired images of the same cells in which variable image noise results in cell segmentation failures. Iteratively updating the cell segmentation model with accurate labels of noisy image areas resulted in an improvement in Dice coefficient from 77% to 86%. This was achieved by adding only 3.4% more cells to the training dataset, showing that local label transfer through graph matching is an effective augmentation strategy to improve segmentation.

Active Cell Appearance Model Induced Generative Adversarial Networks for Annotation-Efficient Cell Segmentation and Identification on Adaptive Optics Retinal Images

Data annotation is a fundamental precursor for establishing large training sets to effectively apply deep learning methods to medical image analysis. For cell segmentation, obtaining high quality annotations is an expensive process that usually requires manual grading by experts. This work introduces an approach to efficiently generate annotated images, called "A-GANs", created by combining an active cell appearance model (ACAM) with conditional generative adversarial networks (C-GANs). ACAM is a statistical model that captures a realistic range of cell characteristics and is used to ensure that the image statistics of generated cells are guided by real data. C-GANs utilize cell contours generated by ACAM to produce cells that match input contours. By pairing ACAM-generated contours with A-GANs-based generated images, high quality annotated images can be efficiently generated. Experimental results on adaptive optics (AO) retinal images showed that A-GANs robustly synthesize realistic, artificial images whose cell distributions are exquisitely specified by ACAM. The cell segmentation performance using as few as 64 manually-annotated real AO images combined with 248 artificially-generated images from A-GANs was similar to the case of using 248 manually-annotated real images alone (Dice coefficients of 88% for both). Finally, application to rare diseases in which images exhibit never-seen characteristics demonstrated improvements in cell segmentation without the need for incorporating manual annotations from these new retinal images. Overall, A-GANs introduce a methodology for generating high quality annotated data that statistically captures the characteristics of any desired dataset and can be used to more efficiently train deep-learning-based medical image analysis applications.

Counteracting age-related VEGF signaling insufficiency promotes healthy aging and extends life span

Aging is an established risk factor for vascular diseases, but vascular aging itself may contribute to the progressive deterioration of organ function. Here, we show in aged mice that vascular endothelial growth factor (VEGF) signaling insufficiency, which is caused by increased production of decoy receptors, may drive physiological aging across multiple organ systems. Increasing VEGF signaling prevented age-associated capillary loss, improved organ perfusion and function, and extended life span. Healthier aging was evidenced by favorable metabolism and body composition and amelioration of aging-associated pathologies including hepatic steatosis, sarcopenia, osteoporosis, "inflammaging" (age-related multiorgan chronic inflammation), and increased tumor burden. These results indicate that VEGF signaling insufficiency affects organ aging in mice and suggest that modulating this pathway may result in increased mammalian life span and improved overall health.

In-vivo sub-diffraction adaptive optics imaging of photoreceptors in the human eye with annular pupil illumination and sub-Airy detection

Adaptive optics scanning light ophthalmoscopy (AOSLO) allows non-invasive visualization of the living human eye at the microscopic scale; but even with correction of the ocular wavefront aberrations over a large pupil, the smallest cells in the photoreceptor mosaic cannot always be resolved. Here, we synergistically combine annular pupil illumination with sub-Airy disk confocal detection to demonstrate a 33% improvement in transverse resolution (from 2.36 to 1.58 μm) and a 13% axial resolution enhancement (from 37 to 32 μm), an important step towards the study of the complete photoreceptor mosaic in heath and disease. Interestingly, annular pupil illumination also enhanced the visualization of the photoreceptor mosaic in non-confocal detection schemes such as split detection AOSLO, providing a strategy for enhanced multimodal imaging of the cone and rod photoreceptor mosaic.

Persistent Dark Cones in Oligocone Trichromacy Revealed by Multimodal Adaptive Optics Ophthalmoscopy

Dark cone photoreceptors, defined as those with diminished or absent reflectivity when observed with adaptive optics (AO) ophthalmoscopy, are increasingly reported in retinal disorders. However, their structural and functional impact remain unclear. Here, we report a 3-year longitudinal study on a patient with oligocone trichromacy (OT) who presented with persistent, widespread dark cones within and near the macula. Diminished electroretinogram (ERG) cone but normal ERG rod responses together with normal color vision confirmed the OT diagnosis. In addition, the patient had normal to near normal visual acuity and retinal sensitivity. Occasional dark gaps in the photoreceptor layer were observed on optical coherence tomography, in agreement with reflectance AO scanning light ophthalmoscopy, which revealed that over 50% of the cones in the fovea were dark, increasing to 74% at 10° eccentricity. In addition, the cone density was 78% lower than normal histologic value at the fovea, and 20-40% lower at eccentricities of 5-15°. Interestingly, color vision testing was near normal at locations where cones were predominantly dark. These findings illustrate how a retina with predominant dark cones that persist over at least 3 years can support near normal central retinal function. Furthermore, this study adds to the growing evidence that cones can continue to survive under non-ideal conditions.

Integrating adaptive optics-SLO and OCT for multimodal visualization of the human retinal pigment epithelial mosaic

In vivo imaging of human retinal pigment epithelial (RPE) cells has been demonstrated through multiple adaptive optics (AO)-based modalities. However, whether consistent and complete information regarding the cellular structure of the RPE mosaic is obtained across these modalities remains uncertain due to limited comparisons performed in the same eye. Here, an imaging platform combining multimodal AO-scanning light ophthalmoscopy (AO-SLO) with AO-optical coherence tomography (AO-OCT) is developed to make a side-by-side comparison of the same RPE cells imaged across four modalities: AO-darkfield, AO-enhanced indocyanine green (AO-ICG), AO-infrared autofluorescence (AO-IRAF), and AO-OCT. Co-registered images were acquired in five subjects, including one patient with choroideremia. Multimodal imaging provided multiple perspectives of the RPE mosaic that were used to explore variations in RPE cell contrast in a subject-, location-, and even cell-dependent manner. Estimated cell-to-cell spacing and density were found to be consistent both across modalities and with normative data. Multimodal images from a patient with choroideremia illustrate the benefit of using multiple modalities to infer the cellular structure of the RPE mosaic in an affected eye, in which disruptions to the RPE mosaic may locally alter the signal strength, visibility of individual RPE cells, or even source of contrast in unpredictable ways.

Spatially Aware Dense-LinkNet Based Regression Improves Fluorescent Cell Detection in Adaptive Optics Ophthalmic Images

Retinal pigment epithelial (RPE) cells play an important role in nourishing retinal neurosensory photoreceptor cells, and numerous blinding diseases are associated with RPE defects. Their fluorescence signature can now be visualized in the living human eye using adaptive optics (AO) imaging combined with indocyanine green (ICG), which motivates us to develop an automated RPE detection method to improve the quantitative evaluation of RPE status in patients. This paper proposes a spatially-aware, Dense-LinkNet-based regression approach to improve the detection of in vivo fluorescent cell patterns, achieving precision, recall, and F1-Score of 93.6 ± 4.3%, 81.4 ± 9.5%, and 86.7 ± 5.7%, respectively. These results demonstrate the utility of incorporating spatial inputs into a deep learning-based regression framework for cell detection.

Graded Image Generation Using Stratified CycleGAN

In medical imaging, CycleGAN has been used for various image generation tasks, including image synthesis, image denoising, and data augmentation. However, when pushing the technical limits of medical imaging, there can be a substantial variation in image quality. Here, we demonstrate that images generated by CycleGAN can be improved through explicit grading of image quality, which we call stratified CycleGAN. In this image generation task, CycleGAN is used to upgrade the image quality and content of near-infrared fluorescent (NIRF) retinal images. After manual assignment of grading scores to a small subset of the data, semi-supervised learning is applied to propagate grades across the remainder of the data and set up the training data. These scores are embedded into the CycleGAN by adding the grading score as a conditional input to the generator and by integrating an image quality classifier into the discriminator. We validate the efficacy of the proposed stratified CycleGAN by considering pairs of NIRF images at the same retinal regions (imaged with and without correction of optical aberrations achieved using adaptive optics), with the goal being to restore image quality in aberrated images such that cellular-level detail can be obtained. Overall, stratified CycleGAN generated higher quality synthetic images than traditional CycleGAN. Evaluation of cell detection accuracy confirmed that synthetic images were faithful to ground truth images of the same cells. Across this challenging dataset, F1-score improved from 76.9 ± 5.7% when using traditional CycleGAN to 85.0±3.4% when using stratified CycleGAN. These findings demonstrate the potential of stratified Cycle-GAN to improve the synthesis of medical images that exhibit a graded variation in image quality.

Perioperative intravenous contrast administration and the incidence of acute kidney injury after major gastrointestinal surgery: prospective, multicentre cohort study

BACKGROUND

This study aimed to determine the impact of preoperative exposure to intravenous contrast for CT and the risk of developing postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery.

METHODS

This prospective, multicentre cohort study included adults undergoing gastrointestinal resection, stoma reversal or liver resection. Both elective and emergency procedures were included. Preoperative exposure to intravenous contrast was defined as exposure to contrast administered for the purposes of CT up to 7 days before surgery. The primary endpoint was the rate of AKI within 7 days. Propensity score-matched models were adjusted for patient, disease and operative variables. In a sensitivity analysis, a propensity score-matched model explored the association between preoperative exposure to contrast and AKI in the first 48 h after surgery.

RESULTS

A total of 5378 patients were included across 173 centres. Overall, 1249 patients (23·2 per cent) received intravenous contrast. The overall rate of AKI within 7 days of surgery was 13·4 per cent (718 of 5378). In the propensity score-matched model, preoperative exposure to contrast was not associated with AKI within 7 days (odds ratio (OR) 0·95, 95 per cent c.i. 0·73 to 1·21; P = 0·669). The sensitivity analysis showed no association between preoperative contrast administration and AKI within 48 h after operation (OR 1·09, 0·84 to 1·41; P = 0·498).

CONCLUSION

There was no association between preoperative intravenous contrast administered for CT up to 7 days before surgery and postoperative AKI. Risk of contrast-induced nephropathy should not be used as a reason to avoid contrast-enhanced CT.

Professor John Pollard: Half-Century of Australian Neurology

With a career in medicine and academia spanning over five decades, Professor John Pollard has devoted much of his life to the development of Australian neurology. His fascinating career has been punctuated with numerous achievements, particularly in the field of inflammatory neuropathies. Now at the age of 79, he continues to work as an active Consultant Neurologist and Co-Director at Sydney’s Brain and Mind Centre, seeing and treating patients. In May 2019 we caught up with Professor Pollard after a busy afternoon list of clinic.

Deriving Visual Cues from Deep Learning to Achieve Subpixel Cell Segmentation in Adaptive Optics Retinal Images

Direct visualization of photoreceptor cells, specialized neurons in the eye that sense light, can be achieved using adaptive optics (AO) retinal imaging. Evaluating photoreceptor cell morphology in retinal diseases is important for monitoring the onset and progression of blindness, but segmentation of these cells is a critical first step. Most segmentation approaches focus on cell region extraction, without directly considering cell boundary localization. This makes it difficult to track cells that have ambiguous boundaries, which result from low image contrast, anisotropic cell regions, or densely-packed cells whose boundaries appear to touch each other. These are all characteristics of the AO images that we consider here. To address these challenges, we develop an AOSeg-Net method that uses a multi-channel U-Net to predict the spatial probabilities of the cell boundary and obtain cell centroid and region distribution information as a means for facilitating cell segmentation. Five-color theorem guarantees the separation of any touching cells. Finally, a region-based level set algorithm that combines all of these visual cues is used to achieve subpixel cell segmentation. Five-fold cross-validation on 428 high resolution retinal images from 23 human subjects showed that AOSegNet substantially outperformed the only other existing approach with Dice coefficients [%] of 84.7 and 78.4, respectively, and average symmetric contour distances [μm] of 0.59 and 0.80, respectively.

Active Appearance Model Induced Generative Adversarial Network for Controlled Data Augmentation

Data augmentation is an important strategy for enlarging training datasets in deep learning-based medical image analysis. This is because large, annotated medical datasets are not only difficult and costly to generate, but also quickly become obsolete due to rapid advances in imaging technology. Image-to-image conditional generative adversarial networks (C-GAN) provide a potential solution for data augmentation. However, annotations used as inputs to C-GAN are typically based only on shape information, which can result in undesirable intensity distributions in the resulting artificially-created images. In this paper, we introduce an active cell appearance model (ACAM) that can measure statistical distributions of shape and intensity and use this ACAM model to guide C-GAN to generate more realistic images, which we call A-GAN. A-GAN provides an effective means for conveying anisotropic intensity information to C-GAN. A-GAN incorporates a statistical model (ACAM) to determine how transformations are applied for data augmentation. Traditional approaches for data augmentation that are based on arbitrary transformations might lead to unrealistic shape variations in an augmented dataset that are not representative of real data. A-GAN is designed to ameliorate this. To validate the effectiveness of using A-GAN for data augmentation, we assessed its performance on cell analysis in adaptive optics retinal imaging, which is a rapidly-changing medical imaging modality. Compared to C-GAN, A-GAN achieved stability in fewer iterations. The cell detection and segmentation accuracy when assisted by A-GAN augmentation was higher than that achieved with C-GAN. These findings demonstrate the potential for A-GAN to substantially improve existing data augmentation methods in medical image analysis.

Cone Photoreceptor Cell Segmentation and Diameter Measurement on Adaptive Optics Images Using Circularly Constrained Active Contour Model

Purpose

Cone photoreceptor cells can be noninvasively imaged in the living human eye by using nonconfocal adaptive optics scanning ophthalmoscopy split detection. Existing metrics, such as cone density and spacing, are based on simplifying cone photoreceptors to single points. The purposes of this study were to introduce a computer-aided approach for segmentation of cone photoreceptors, to apply this technique to create a normal database of cone diameters, and to demonstrate its use in the context of existing metrics.

Methods

Cone photoreceptor segmentation is achieved through a circularly constrained active contour model (CCACM). Circular templates and image gradients attract active contours toward cone photoreceptor boundaries. Automated segmentation from in vivo human subject data was compared to ground truth established by manual segmentation. Cone diameters computed from curated data (automated segmentation followed by manual removal of errors) were compared with histology and published data.

Results

Overall, there was good agreement between automated and manual segmentations and between diameter measurements (n = 5191 cones) and published histologic data across retinal eccentricities ranging from 1.35 to 6.35 mm (temporal). Interestingly, cone diameter was correlated to both cone density and cone spacing (negatively and positively, respectively; P < 0.01 for both). Application of the proposed automated segmentation to images from a patient with late-onset retinal degeneration revealed the presence of enlarged cones above individual reticular pseudodrusen (average 23.0% increase, P < 0.05).

Conclusions

CCACM can accurately segment cone photoreceptors on split detection images across a range of eccentricities. Metrics derived from this automated segmentation of adaptive optics retinal images can provide new insights into retinal diseases.

3-D printed photoreceptor phantoms for evaluating lateral resolution of adaptive optics imaging systems

With adaptive optics (AO), optical coherence tomography and scanning laser ophthalmoscopy imaging systems can resolve individual photoreceptor cells in living eyes, due to enhanced lateral spatial resolution. However, no standard test method exists for experimentally quantifying this parameter in ophthalmic AO imagers. Here, we present three-dimensional (3-D) printed phantoms, which enable the measurement of lateral resolution in an anatomically relevant manner. We used two-photon polymerization to fabricate two phantoms, which mimic the mosaic of cone photoreceptor outer segments at multiple retinal eccentricities. With these phantoms, we demonstrated that the resolution of two multimodal AO systems is similar to theoretical predictions, with some intriguing speckle effects.

Longitudinal adaptive optics fluorescence microscopy reveals cellular mosaicism in patients

The heterogeneity of individual cells in a tissue has been well characterized, largely using ex vivo approaches that do not permit longitudinal assessments of the same tissue over long periods of time. We demonstrate a potentially novel application of adaptive optics fluorescence microscopy to visualize and track the in situ mosaicism of retinal pigment epithelial (RPE) cells directly in the human eye. After a short, dynamic period during which RPE cells take up i.v.-administered indocyanine green (ICG) dye, we observed a remarkably stable heterogeneity in the fluorescent pattern that gradually disappeared over a period of days. This pattern could be robustly reproduced with a new injection and follow-up imaging in the same eye out to at least 12 months, which enabled longitudinal tracking of RPE cells. Investigation of ICG uptake in primary human RPE cells and in a mouse model of ICG uptake alongside human imaging corroborated our findings that the observed mosaicism is an intrinsic property of the RPE tissue. We demonstrate a potentially novel application of fluorescence microscopy to detect subclinical changes to the RPE, a technical advance that has direct implications for improving our understanding of diseases such as oculocutaneous albinism, late-onset retinal degeneration, and Bietti crystalline dystrophy.

Combining multimodal adaptive optics imaging and angiography improves visualization of human eyes with cellular-level resolution

Visualizing the cellular manifestation of disease has recently been aided by an increasing number of adaptive optics (AO)-based imaging modalities developed for the living human eye. However, simultaneous visualization of multiple, interacting cell types within a complete neural-epithelial-vascular complex has proven challenging. By incorporating AO with indocyanine green angiography, we demonstrate the possibility of imaging photoreceptors, retinal pigment epithelial cells, and choriocapillaris in the living human eye. Unexpectedly, we found that there was uptake of indocyanine green dye into the retinal pigment epithelial cells in the earliest phases of imaging, which formed the basis for devising a strategy to visualize the choriocapillaris. Our results expand the range of applications for an existing, FDA-approved, systemically injected fluorescent dye. The combined multimodal approach can be used to evaluate the complete outer retinal complex at the cellular level, a transformative step toward revealing the in vivo cellular status of neurodegenerative conditions and blinding diseases.

Trans-retinal cellular imaging with multimodal adaptive optics

Adaptive optics (AO), when coupled to different imaging modalities, has enabled resolution of various cell types across the entire retinal depth in the living human eye. Extraction of information from retinal cells is optimal when their optical properties, structure, and physiology are matched to the unique capabilities of each imaging modality. Despite the earlier success of multimodal AO (mAO) approaches, the full capabilities of the individual imaging modalities were often diminished rather than enhanced when integrated into multimodal platforms. Furthermore, many mAO designs added unnecessary complexity, making clinical translation difficult. In this study, we present a novel mAO system that combines two complementary approaches, scanning laser ophthalmoscopy (SLO) and optical coherence tomography (OCT), in one instrument using a simplified optical design, flexible alternation of scanning modes, and independent focus control. The mAO system imaging performance was demonstrated by visualization of cells in their mosaic arrangement across the full depth of the retina in three human subjects, including microglia, nerve fiber bundles, retinal ganglion cells and axons, and capillaries in the inner retina and foveal cones, peripheral rods, and retinal pigment epithelial cells in the outer retina. Multimodal AO is a powerful tool to capture the most complete picture of retinal health.

Noninvasive in vivo characterization of erythrocyte motion in human retinal capillaries using high-speed adaptive optics near-confocal imaging

The flow of erythrocytes in parafoveal capillaries was imaged in the living human eye with an adaptive optics near-confocal ophthalmoscope at a frame rate of 800 Hz with a low coherence near-infrared (NIR) light source. Spatiotemporal traces of the erythrocyte movement were extracted from consecutive images. Erythrocyte velocity was measured using custom software based on the Radon transform. The impact of imaging speed on velocity measurement was estimated using images of frame rates of 200, 400, and 800 Hz. The NIR light allowed for long imaging periods without visually stimulating the retina and disturbing the natural rheological state. High speed near-confocal imaging enabled direct and accurate measurement of erythrocyte velocity, and revealed a distinctively cardiac-dependent pulsatile velocity waveform of the erythrocyte flow in retinal capillaries, disclosed the impact of the leukocytes on erythrocyte motion, and provided new metrics for precise assessment of erythrocyte movement. The approach may facilitate new investigations on the pathophysiology of retinal microcirculation with applications for ocular and systemic diseases.

COMPUTER-AIDED DETECTION OF PATTERN CHANGES IN LONGITUDINAL ADAPTIVE OPTICS IMAGES OF THE RETINAL PIGMENT EPITHELIUM

Retinal pigment epithelium (RPE) defects are indicated in many blinding diseases, but have been difficult to image. Recently, adaptive optics enhanced indocyanine green (AO-ICG) imaging has enabled direct visualization of the RPE mosaic in the living human eye. However, tracking the RPE across longitudinal images on the time scale of months presents with unique challenges, such as visit-to-visit distortion and changes in image quality. We introduce a coarse-to-fine search strategy that identifies paired patterns and measures their changes. First, longitudinal AO-ICG image displacements are estimated through graph matching of affine invariant maximal stable extremal regions in affine Gaussian scale-space. This initial step provides an automatic means to designate the search ranges for finding corresponding patterns. Next, AO-ICG images are decomposed into superpixels, simplified to a pictorial structure, and then matched across visits using tree-based belief propagation. Results from human subjects in comparison with a validation dataset revealed acceptable accuracy levels for the level of changes that are expected in clinical data. Application of the proposed framework to images from a diseased eye demonstrates the potential clinical utility of this method for longitudinal tracking of the heterogeneous RPE pattern.

Attenuating the rate of total body fat accumulation and alleviating liver damage by oral administration of vitamin D-enriched edible mushrooms in a diet-induced obesity murine model is mediated by an anti-inflammatory paradigm shift

Background

Hypovitaminosis D is associated with many features of the metabolic syndrome, including non-alcoholic fatty liver disease. Vitamin D-enriched mushrooms extracts exert a synergistic anti-inflammatory effect. The aim of the present study is to determine the immunomodulatory effect of oral administration of vitamin D-enriched mushrooms extracts on high-fat diet (HFD) animal model of non-alcoholic steatohepatitis (NASH).

Methods

C57BL/6 mice on HFD were orally administered with vitamin D supplement, Lentinula edodes (LE) mushrooms extract, or vitamin D-enriched mushrooms extract for 25 weeks. Mice were studied for the effect of the treatment on the immune system, liver functions and histology, insulin resistance and lipid profile.

Results

Treatment with vitamin D-enriched LE extracts was associated with significant attenuation of the rate of total body fat accumulation, along with a decrease in hepatic fat content as measured by an EchoMRI. Significant alleviation of liver damage manifested by a marked decrease in ALT, and AST serum levels (from 900 and 1021 U/L in the control group to 313 and 340; 294 and 292; and 366 and 321 U/L for ALT and AST, in Vit D, LE and LE + Vit D treated groups, respectively). A corresponding effect on hepatocyte ballooning were also noted. A significant decrease in serum triglycerides (from 103 to 75, 69 and 72 mg/dL), total cholesterol (from 267 to 160, 157 and 184 mg/dL), and LDL cholesterol (from 193 mg/dL to 133, 115 and 124 mg/dL) along with an increase in the HDL/LDL ratio, and improved glucose levels were documented. These beneficial effects were associated with a systemic immunomodulatory effect associated with an increased CD4/CD8 lymphocyte ratio (from 1.38 in the control group to 1.69, 1.71 and 1.63), and a pro- to an anti-inflammatory cytokine shift.

Conclusions

Oral administration of vitamin-D enriched mushrooms extracts exerts an immune modulatory hepato-protective effect in NASH model.

Noninvasive near infrared autofluorescence imaging of retinal pigment epithelial cells in the human retina using adaptive optics

The retinal pigment epithelial (RPE) cells contain intrinsic fluorophores that can be visualized using infrared autofluorescence (IRAF). Although IRAF is routinely utilized in the clinic for visualizing retinal health and disease, currently, it is not possible to discern cellular details using IRAF due to limits in resolution. We demonstrate that the combination of adaptive optics (AO) with IRAF (AO-IRAF) enables higher-resolution imaging of the IRAF signal, revealing the RPE mosaic in the living human eye. Quantitative analysis of visualized RPE cells in 10 healthy subjects across various eccentricities demonstrates the possibility for in vivo density measurements of RPE cells, which range from 6505 to 5388 cells/mm2 for the areas measured (peaking at the fovea). We also identified cone photoreceptors in relation to underlying RPE cells, and found that RPE cells support on average up to 18.74 cone photoreceptors in the fovea down to an average of 1.03 cone photoreceptors per RPE cell at an eccentricity of 6 mm. Clinical application of AO-IRAF to a patient with retinitis pigmentosa illustrates the potential for AO-IRAF imaging to become a valuable complementary approach to the current landscape of high resolution imaging modalities.