Portable lensless wide-field microscopy imaging platform based on digital inline holography and multi-frame pixel super-resolution

Sobieranski, Antonio Carlos; İnci, Fatih; Tekin, H. Cumhur; Yüksekkaya, Mehmet Emin; Comunello, Eros; Cobra, D.T.; Wangenheim, Aldo von; Demirci, Utkan

doi:10.1038/lsa.2015.119

Cited by 108 publications

(63 citation statements)

References 60 publications

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“…Smartphone and lensless imaging-based disease detection and quantification is another area of immense interest [16,123,124]. The smartphone market is growing at an unprecedented pace.…”

Section: Future Of Paper-based Microfluidic Devicesmentioning

confidence: 99%

Paper-based analytical devices for clinical diagnosis: recent advances in the fabrication techniques and sensing mechanisms

Sher

Zhuang²,

Demirci³

et al. 2017

Expert Review of Molecular Diagnostics

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217

157

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Introduction There is a significant interest in developing inexpensive portable biosensing platforms for various applications including disease diagnostics, environmental monitoring, food safety, and water testing at the point-of-care (POC) settings. Current diagnostic assays available in the developed world require sophisticated laboratory infrastructure and expensive reagents. Hence, they are not suitable for resource-constrained settings with limited financial resources, basic health infrastructure, and few trained technicians. Cellulose and flexible transparency paper-based analytical devices have demonstrated enormous potential for developing robust, inexpensive and portable devices for disease diagnostics. These devices offer promising solutions to disease management in resource-constrained settings where the vast majority of the population cannot afford expensive and highly sophisticated treatment options. Areas covered In this review, the authors describe currently developed cellulose and flexible transparency paper-based microfluidic devices, device fabrication techniques, and sensing technologies that are integrated with these devices. The authors also discuss the limitations and challenges associated with these devices and their potential in clinical settings. Expert commentary In recent years, cellulose and flexible transparency paper-based microfluidic devices have demonstrated the potential to become future healthcare options despite a few limitations such as low sensitivity and reproducibility.

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“…Smartphone and lensless imaging-based disease detection and quantification is another area of immense interest [16,123,124]. The smartphone market is growing at an unprecedented pace.…”

Section: Future Of Paper-based Microfluidic Devicesmentioning

confidence: 99%

Paper-based analytical devices for clinical diagnosis: recent advances in the fabrication techniques and sensing mechanisms

Sher

Zhuang²,

Demirci³

et al. 2017

Expert Review of Molecular Diagnostics

Self Cite

217

157

View full text Add to dashboard Cite

show abstract

“…Existing SR processing techniques applied in lensless imaging systems are mainly based on multi-frame reconstruction, in which multiple LR cell images with sub-pixel motions of the same object are synthesized into one single HR cell image. The sub-pixel motions can be generated by either flowing the cell samples through the microfluidic channel, shifting the light source, or sequentially activating multiple light sources at different locations [7,13,14]. However, the main problem for multi-frame SR is that the system needs to continuously capture, store, and process multiple LR images in order to recover one HR image, which not only limits the practical detection throughput but also requires large storage.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting

Huang

Jiang

Liu

et al. 2016

Sensors

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A lensless blood cell counting system integrating microfluidic channel and a complementary metal oxide semiconductor (CMOS) image sensor is a promising technique to miniaturize the conventional optical lens based imaging system for point-of-care testing (POCT). However, such a system has limited resolution, making it imperative to improve resolution from the system-level using super-resolution (SR) processing. Yet, how to improve resolution towards better cell detection and recognition with low cost of processing resources and without degrading system throughput is still a challenge. In this article, two machine learning based single-frame SR processing types are proposed and compared for lensless blood cell counting, namely the Extreme Learning Machine based SR (ELMSR) and Convolutional Neural Network based SR (CNNSR). Moreover, lensless blood cell counting prototypes using commercial CMOS image sensors and custom designed backside-illuminated CMOS image sensors are demonstrated with ELMSR and CNNSR. When one captured low-resolution lensless cell image is input, an improved high-resolution cell image will be output. The experimental results show that the cell resolution is improved by 4×, and CNNSR has 9.5% improvement over the ELMSR on resolution enhancing performance. The cell counting results also match well with a commercial flow cytometer. Such ELMSR and CNNSR therefore have the potential for efficient resolution improvement in lensless blood cell counting systems towards POCT applications.

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“…Fourth, in this study, sexual assault kit samples were visualized using a standard laboratory microscope. The next generation of this platform can potentially be integrated with a portable imaging system,40 enabling easy access to the technology in remote locations for sexual assault evidence screening. Fifth, we decorated channels with SLeX monomer units in the current study.…”

Section: Resultsmentioning

confidence: 99%

A Novel On‐Chip Method for Differential Extraction of Sperm in Forensic Cases

et al. 2018

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One out of every six American women has been the victim of a sexual assault in their lifetime. However, the DNA casework backlog continues to increase outpacing the nation's capacity since DNA evidence processing in sexual assault casework remains a bottleneck due to laborious and time‐consuming differential extraction of victim's and perpetrator's cells. Additionally, a significant amount (60–90%) of male DNA evidence may be lost with existing procedures. Here, a microfluidic method is developed that selectively captures sperm using a unique oligosaccharide sequence (Sialyl‐LewisX), a major carbohydrate ligand for sperm‐egg binding. This method is validated with forensic mock samples dating back to 2003, resulting in 70–92% sperm capture efficiency and a 60–92% reduction in epithelial fraction. Captured sperm are then lysed on‐chip and sperm DNA is isolated. This method reduces assay‐time from 8 h to 80 min, providing an inexpensive alternative to current differential extraction techniques, accelerating identification of suspects and advancing public safety.

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Portable lensless wide-field microscopy imaging platform based on digital inline holography and multi-frame pixel super-resolution

Cited by 108 publications

References 60 publications

Paper-based analytical devices for clinical diagnosis: recent advances in the fabrication techniques and sensing mechanisms

Paper-based analytical devices for clinical diagnosis: recent advances in the fabrication techniques and sensing mechanisms

Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting

A Novel On‐Chip Method for Differential Extraction of Sperm in Forensic Cases

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