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To create 3D arrangements of multiple materials in complex geometries, recent work within our lab has pursued the efficient and accurate modeling of nanoparticles and the assembly of micro- and nanostructures using optical tweezers. 

Lensfree holographic microscopy is a compact and cost-effective modality for imaging large fields of view with high resolution. When combined with automated image processing, it can be used for biomolecular sensing where biochemically functionalized micro- and nano-beads are used to label biomolecules of interest. Neural networks for image feature classification provide faster and more robust sensing results than traditional image processing approaches. While neural networks have been widely applied to other types of image classification problems, and even image reconstruction in lensfree holographic microscopy, it is unclear what type of network architecture performs best for the types of small object image classification problems involved in holographic-based sensors. Here, we apply a shallow convolutional neural network to this task, and thoroughly investigate how different layers and hyperparameters affect network performance. Layers include dropout, convolutional, normalization, pooling, and activation. Hyperparameters include dropout fraction, filter number and size, stride, and padding. We ultimately achieve a network accuracy of ∼83%, and find that the choice of activation layer is most important for maximizing accuracy. We hope that these results can be helpful for researchers developing neural networks for similar classification tasks. 

Many clinical procedures and biomedical research workflows rely on microscopy, including diagnosis of cancer, genetic disorders, autoimmune diseases, infections, and quantification of cell culture. Despite its widespread use, traditional image acquisition and review by trained microscopists is often lengthy and expensive, limited to large hospitals or laboratories, precluding use in point‐of‐care settings. In contrast, lensless or lensfree holographic microscopy (LHM) is inexpensive and widely deployable because it can achieve performance comparable to expensive and bulky objective‐based benchtop microscopes while relying on components that cost only a few hundred dollars or less. Lab‐on‐a‐chip integration is practical and enables LHM to be combined with single‐cell isolation, sample mixing, and in‐incubator imaging. Additionally, many manual tasks in conventional microscopy are instead computational in LHM, including image focusing, stitching, and classification. Furthermore, LHM offers a field of view hundreds of times greater than that of conventional microscopy without sacrificing resolution. Here, the basic LHM principles are summarized, as well as recent advances in artificial intelligence integration and enhanced resolution. How LHM is applied to the above clinical and biomedical applications is discussed in detail. Finally, emerging clinical applications, high‐impact areas for future research, and some current challenges facing widespread adoption are identified. 

Fluorescence and, more generally, photoluminescence enable high contrast imaging of targeted regions of interest through the use of photoluminescent probes with high specificity for different targets. Fluorescence can be used for rare cell imaging; however, this often requires a high space-bandwidth product: simultaneous high resolution and large field of view. With bulky traditional microscopes, high space-bandwidth product images require time-consuming mechanical scanning and stitching. Lensfree imaging can compactly and cost-effectively achieve a high space-bandwidth product in a single image through computational reconstruction of images from diffraction patterns recorded over the full field of view of standard image sensors. Many methods of lensfree photoluminescent imaging exist, where the excitation light is filtered before the image sensor, often by placing spectral filters between the sample and sensor. However, the sample-to-sensor distance is one of the limiting factors on resolution in lensfree systems and so more competitive performance can be obtained if this distance is reduced. Here, we show a time-gated lensfree photoluminescent imaging system that can achieve a resolution of 8.77 µm. We use europium chelate fluorophores because of their long lifetime (642 µs) and trigger camera exposure ∼50 µs after excitation. Because the excitation light is filtered temporally, there is no need for physical filters, enabling reduced sample-to-sensor distances and higher resolutions. 

The persistence of the global COVID-19 pandemic caused by the SARS-CoV-2 virus has continued to emphasize the need for point-of-care (POC) diagnostic tests for viral diagnosis. The most widely used tests, lateral flow assays used in rapid antigen tests, and reverse-transcriptase real-time polymerase chain reaction (RT-PCR), have been instrumental in mitigating the impact of new waves of the pandemic, but fail to provide both sensitive and rapid readout to patients. Here, we present a portable lens-free imaging system coupled with a particle agglutination assay as a novel biosensor for SARS-CoV-2. This sensor images and quantifies individual microbeads undergoing agglutination through a combination of computational imaging and deep learning as a way to detect levels of SARS-CoV-2 in a complex sample. SARS-CoV-2 pseudovirus in solution is incubated with acetyl cholinesterase 2 (ACE2)-functionalized microbeads then loaded into an inexpensive imaging chip. The sample is imaged in a portable in-line lens-free holographic microscope and an image is reconstructed from a pixel superresolved hologram. Images are analyzed by a deep-learning algorithm that distinguishes microbead agglutination from cell debris and viral particle aggregates, and agglutination is quantified based on the network output. We propose an assay procedure using two images which results in the accurate determination of viral concentrations greater than the limit of detection (LOD) of 1.27 × 10 3 copies per mL, with a tested dynamic range of 3 orders of magnitude, without yet reaching the upper limit. This biosensor can be used for fast SARS-CoV-2 diagnosis in low-resource POC settings and has the potential to mitigate the spread of future waves of the pandemic.