Structure-dependent amplification for denoising and background correction in Fourier ptychographic microscopy

Fourier Ptychographic Microscopy (FPM) allows high resolution imaging using iterative phase retrieval to recover an estimate of the complex object from a series of images captured under oblique illumination. FPM is particularly sensitive to noise and uncorrected background signals as it relies on combining information from brightfield and noisy darkfield (DF) images. In this article we consider the impact of different noise sources in FPM and show that inadequate removal of the DF background signal and associated noise are the predominant cause of artefacts in reconstructed images. We propose a simple solution to FPM background correction and denoising that outperforms existing methods in terms of image quality, speed and simplicity, whilst maintaining high spatial resolution and sharpness of the reconstructed image. Our method takes advantage of the data redundancy in real space within the acquired dataset to boost the signal-to-background ratio in the captured DF images, before optimally suppressing background signal. By incorporating differentially denoised images within the classic FPM iterative phase retrieval algorithm, we show that it is possible to achieve efficient removal of background artefacts without suppression of high frequency information. The method is tested using simulated data and experimental images of thin blood films, bone marrow and liver tissue sections. Our approach is non-parametric, requires no prior knowledge of the noise distribution and can be directly applied to other hardware platforms and reconstruction algorithms making it widely applicable in FPM

Digital refocusing and extended depth of field reconstruction in Fourier ptychographic microscopy

Fourier ptychography microscopy (FPM) is a recently developed microscopic imaging method that allows the recovery of a high-resolution complex image by combining a sequence of bright and darkfield images acquired under inclined illumination. The capacity of FPM for high resolution imaging at low magnification makes it particularly attractive for applications in digital pathology which require imaging of large specimens such as tissue sections and blood films. To date most applications of FPM have been limited to imaging thin samples, simplifying both image reconstruction and analysis. In this work we show that, for samples of intermediate thickness (defined here as less than the depth of field of a raw captured image), numerical propagation of the reconstructed complex field allows effective digital refocusing of FPM images. The results are validated by comparison against images obtained with an equivalent high numerical aperture objective lens. We find that post reconstruction refocusing (PRR) yields images comparable in quality to adding a defocus term to the pupil function within the reconstruction algorithm, while reducing computing time by several orders of magnitude. We apply PRR to visualize FPM images of Giemsa-stained peripheral blood films and present a novel image processing pipeline to construct an effective extended depth of field image which optimally displays the 3D sample structure in a 2D image. We also show how digital refocusing allows effective correction of the chromatic focus shifts inherent to the low magnification objective lenses used in FPM setups, improving the overall quality of color FPM images

Optical mesoscopy, machine learning and computational microscopy enable high information content diagnostic imaging of blood films

Automated image-based assessment of blood films has tremendous potential to support clinical haematology within overstretched healthcare systems. To achieve this, efficient and reliable digital capture of the rich diagnostic information contained within a blood film is a critical first step. However, this is often challenging, and in many cases entirely unfeasible, with the microscopes typically used in haematology due to the fundamental trade-off between magnification and spatial resolution. To address this, we investigated three state-of-the-art approaches to microscopic imaging of blood films which leverage recent advances in optical and computational imaging and analysis to increase the information capture capacity of the optical microscope: optical mesoscopy, which uses a giant microscope objective (Mesolens) to enable high resolution imaging at low magnification; Fourier ptychographic microscopy, a computational imaging method which relies on oblique illumination with a series of LEDs to capture high resolution information; and deep neural networks which can be trained to increase the quality of low magnification, low resolution images. We compare and contrast the performance of these techniques for blood film imaging for the exemplar case of Giemsa-stained peripheral blood smears. Using computational image analysis and shape-based object classification we demonstrate their use for automated analysis of red blood cell morphology and visualization and detection of small blood borne parasites such as the malarial parasite Plasmodium falciparum. Our results demonstrate that these new methods greatly increase the information capturing capacity of the light microscope with transformative potential for haematology and more generally across digital pathology. This article is protected by copyright. All rights reserved

Depth-resolved local reflectance spectra measurements in full-field optical coherence tomography

Full-field optical coherence tomography (FF-OCT) is a widely used technique for applications such as biological imaging, optical metrology, and materials characterization, providing structural and spectral information. By spectral analysis of the backscattered light, the technique of spectroscopic-OCT enables the differentiation of structures having different spectral properties, but not the determination of their reflectance spectrum. For surface measurements, this can be achieved by applying a Fourier transform to the interferometric signals and using an accurate calibration of the optical system. An extension of this method is reported for local spectroscopic characterization of transparent samples and in particular for the determination of depth-resolved reflectance spectra of buried interfaces. The correct functioning of the method is demonstrated by comparing the results with those obtained using a program based on electromagnetic matrix methods for stratified media. Experimental measurements of spatial resolutions are provided to demonstrate the smallest structures that can be characterized

Depth-resolved local reflectance spectra measurements in full-field optical coherence tomography

Full-field optical coherence tomography (FF-OCT) is a widely used technique for applications such as biological imaging, optical metrology, and materials characterization, providing structural and spectral information. By spectral analysis of the backscattered light, the technique of spectroscopic-OCT enables the differentiation of structures having different spectral properties, but not the determination of their reflectance spectrum. For surface measurements, this can be achieved by applying a Fourier transform to the interferometric signals and using an accurate calibration of the optical system. An extension of this method is reported for local spectroscopic characterization of transparent samples and in particular for the determination of depth-resolved reflectance spectra of buried interfaces. The correct functioning of the method is demonstrated by comparing the results with those obtained using a program based on electromagnetic matrix methods for stratified media. Experimental measurements of spatial resolutions are provided to demonstrate the smallest structures that can be characterized

Mucolipidosis II : a single causal mutation in the N-acetylglucosamine-1-phosphotransferase gene (GNPTAB) in a French Canadian founder population

Mucolipidosis (ML) II (I-cell disease) is a lysosomal storage disorder caused by a deficiency of UDP-N-acetylglucosamine:lysosomal enzyme N-acetylglucosamine-1-phosphotransferase. MLII is an autosomal recessive disease with a carrier rate estimated at 1/39 in Saguenay-Lac-Saint-Jean (SLSJ) (Quebec, Canada), which is the highest frequency documented worldwide. To identify the causing mutation, we sequenced GNPTAB exons in 27 parents of 16 MLII-deceased children from the SLSJ region as obligatory and potential carriers. We also performed a genealogical reconstruction for each parent to evaluate consanguinity levels and genetic contribution of ancestors. Our goal was to identify which parameters could explain the high MLII frequency observed in the SLSJ population. A single mutation (c.3503_3504delTC) was found in all obligatory carriers. In addition, 11 apparent polymorphisms were identified. The mutation was not detected in genomic DNA of 50 unrelated controls. Genealogical data show six founders (three couples) with a higher probability of having introduced the mutation in the population. The frequency of the mutation was increased as a consequence of this founder effect and of the resulting population structure. We suggest that c.3503_3504delTC is the allele causing MLII in the SLSJ population, and its high carrier rate is most likely explained by a founder effect

Data-driven malaria prevalence prediction in large densely populated urban holoendemic sub-Saharan West Africa

Over 200 million malaria cases globally lead to half-million deaths annually. The development of malaria prevalence prediction systems to support malaria care pathways has been hindered by lack of data, a tendency towards universal "monolithic" models (one-size-fits-all-regions) and a focus on long lead time predictions. Current systems do not provide short-term local predictions at an accuracy suitable for deployment in clinical practice. Here we show a data-driven approach that reliably produces one-month-ahead prevalence prediction within a densely populated all-year-round malaria metropolis of over 3.5 million inhabitants situated in Nigeria which has one of the largest global burdens of P. falciparum malaria. We estimate one-month-ahead prevalence in a unique 22-years prospective regional dataset of > 9 × 10^{4} participants attending our healthcare services. Our system agrees with both magnitude and direction of the prediction on validation data achieving MAE ≤ 6 × 10^{-2}, MSE ≤ 7 × 10^{-3}, PCC (median 0.63, IQR 0.3) and with more than 80% of estimates within a (+ 0.1 to - 0.05) error-tolerance range which is clinically relevant for decision-support in our holoendemic setting. Our data-driven approach could facilitate healthcare systems to harness their own data to support local malaria care pathways

Light and tree size influence belowground development in yellow birch and sugar maple

The effects of light and tree size on the root architecture and mycorrhiza of yellow birch (Betula alleghaniensis Britton) and sugar maple (Acer saccharum Marsh) growing in the understory of deciduous forests in southern Qu