Optical imaging techniques for point-of-care diagnostics - PubMed (original) (raw)

Review

. 2013 Jan 7;13(1):51-67.

doi: 10.1039/c2lc40864c. Epub 2012 Oct 9.

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Review

Optical imaging techniques for point-of-care diagnostics

Hongying Zhu et al. Lab Chip. 2013.

Erratum in

Abstract

Improving access to effective and affordable healthcare has long been a global endeavor. In this quest, the development of cost-effective and easy-to-use medical testing equipment that enables rapid and accurate diagnosis is essential to reduce the time and costs associated with healthcare services. To this end, point-of-care (POC) diagnostics plays a crucial role in healthcare delivery in both developed and developing countries by bringing medical testing to patients, or to sites near patients. As the diagnosis of a wide range of diseases, including various types of cancers and many endemics, relies on optical techniques, numerous compact and cost-effective optical imaging platforms have been developed in recent years for use at the POC. Here, we review the state-of-the-art optical imaging techniques that can have a significant impact on global health by facilitating effective and affordable POC diagnostics.

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Figures

Figure 1

Figure 1

(A) Shows a picture of the Global Focus microscope. Yellow arrows show the trans-illumination light path of the microscope. (B) Left image is the photograph of M. Tuberculosis bacilli stained with auramine orange, obtained with the Global Focus microscope at 400x magnification. Right image is a digital magnification detail of an M. tuberculosis bacillus. Reprinted from Ref. with permission from PLoS One.

Figure 2

Figure 2

Miniature integrated fluorescent microscope. (A) Schematic illustration of an integrated microscope (shown in cross-section). (B) A photograph of an assembled integrated microscope. Insets, filter cube (bottom left), CMOS camera chip (top right) and PCB holding the LED illumination source (bottom right). Scale bar 5 mm. Reprinted from Ref. with permission from Nature Publishing Group.

Figure 3

Figure 3

(A) Schematic illustration of an integrated tuberculosis diagnosis platform. (B) Array microscope with separated, discontinuous fields of view. (C) Overlapping images of a positive sputum smear sample. Left and right digital images can be added numerically to form an overlapping image. Bacilli in the originally image can be seen in the overlapped image as indicated by the arrow. Reprinted from reference with permission from Elsevier.

Figure 4

Figure 4

(A) (Left) mobile phone microscope prototype, with LED and filters installed, capable of fluorescent imaging. (Right) the bright field and fluorescent imaging of 6 μm beads. (B) Micrographs of peripheral blood smears obtained by the cell phone microscope. Upper row: conventional microscope images. Bottom row: cell phone microscope images. Left column, images of normal blood sample. Center column, images of blood sample with iron deficiency anemia. Right column, images of blood sample with sickle cell anemia. Reprinted from references and with permission from PLoS One.

Figure 5

Figure 5

(A–B) An illustration and photograph of the wide-field fluorescent microscope on a cell-phone. The weight of the entire attachment is ~ 28 grams (~ 1 ounce) and the dimensions of the attachment are ~3.5 × 5.5 × 2.4 cm. (C) Cell-phone images of labeled WBCs (cropped), compressively-decoded (CS) images and conventional fluorescence microscope images of the same labeled WBCs are provided from left-to-right of the panel, respectively. Arrows point to WBCs that are resolved by CS. Reprinted from reference with permission from RSC publishing.

Figure 6

Figure 6

(A–B) An illustration and photograph of the fluorescent imaging flow cytometry on a cell-phone. The entire attachment has dimensions of ~ 35 × 55 × 27.9 mm and a weight of ~ 18 grams. Total white blood cell counting results for a low WBC density sample (5000 cells/μL) (C) and for a higher WBC density sample (7800 cells/μL) (D) obtained from the cell-phone based imaging flow-cytometer Reprinted from reference with permission from ACS publishing.

Figure 7

Figure 7

(A) Schematic of the interferometric reflectance imaging (IRIS). XC: X-cube used to combine the beams of the different LEDs. BS: beam splitter. (B) Interferometric intensity image of 150 nm diameter beads at a wavelength of 635 nm. (C) Response of a single 150 nm particle shown in (B). Reprinted from reference with permission from ACS publishing.

Figure 8

Figure 8

Schematic illustration of the lensfree on-chip holography platform.,,, The objects are placed directly on a digital sensor array with typically Z2~ 1 mm distance to its active area. A partially-coherent light source, such as an LED, is placed Z1~ 40–100 mm away from the objects, and spatially filtered by a pinhole of diameter d~0.05–0.1 mm to record the digital in-line holograms of objects with unit fringe magnification over a large field-of-view (FOV), e.g., 24 mm2.

Figure 9

Figure 9

A photograph (A) and a schematic diagram (B) of the portable multi-height microscope are shown. This microscope images dense samples by recording few intensity measurements with different sample to sensor distances. (C) Imaging results obtained from the microscope shown in (A–B). A full FOV (~30 mm2) hologram of a Pap smear sample is shown in the left panel. The right panel shows zoomed reconstructed amplitude and phase images of region one and two and a microscope comparison images (60 ×, 0.65 NA). Reprinted from reference with permission from RSC publishing.

Figure 10

Figure 10

(A) A schematic diagram of OFM. The apertures (white circles) are fabricated directly on top of the optoelectronic sensor and incorporated in an optofluidic channel (blue lines). (B) A photograph of the OFM. (C) A schematic diagram that shows that by tilting the microscope, gravity can provide the flow of the sample. (D) Block diagram of OFM computational principles. Reprinted from reference with permission from National Academy of Sciences, USA.

Figure 11

Figure 11

The basic configuration of the lensfree detector based on Soller collimator configuration. (A–B) A photograph and schematic configuration of the basic elements of the lensfree detector (the size of the bars are 1 cm), (B) a schematic configuration of the detector. 1. Multi-wavelength LED, 2. Narrow band blue emission filter, 3. Assay microfluidics, 4 and 6. Light collimator, 5. Emission filter, and 7. CCD. (C) a photograph of the assembled lensfree detector. Reprinted from reference with permission from RSC publishing.

Figure 12

Figure 12

Schematic illustration of the cell-based biosensor platform for the detection of cardiotoxicity using webcam based lensfree imaging technique. The white LED illuminates the chamber slide, which containes the ESC-derived carrdiomyocytes. The real-time beating rates of the cardiomyocytes are recorded by the CMOS imaging module taken from a webcam, and analyzed by imaging processing program. Reprinted from reference with permission from RSC publishing

Figure 13

Figure 13

Schematic diagram (A) and picture (B) of the portable fiber-optic fluorescence imaging platform that uses a digital single-lens reflex (DSLR) camera introduced by Shin et. al. (C) In vivo imaging of healthy human oral mucosa. (D) An image of human mucosa that is labeled by proflavine were acquired by the DSLR based micro-endoscope shown in (A) and (B). Reprinted from Ref. with permission from PLoS One.

Figure 14

Figure 14

(Top panel) Shows a cartoon drawing and a photograph of the tunable laser source developed for use in point-of-care SS-OCT systems. (Bottom panel) Shows cross sectional images of the ventral surface of a human forefinger obtained during the forward scan (left) and the backward scan (middle) of the resonant mirror inside the linear cavity, together with a combined image (right). Reprinted from Ref. with permission from Optical Society of America.

Figure 15

Figure 15

(Top panel) Shows a photograph of the handheld SD-OCT scanner unit (left) together with a schematic illustration of the entire OCT system (right). (Bottom panel) Shows in vivo cross-sectional images of normal human tissue obtained by using this OCT system Reprinted from Ref. with permission from IEEE Engineering in Medicine and Biology Society.

Figure 16

Figure 16

CATRA provides an interactive experience to the user to self-evaluate the severity of her/his cataract using a snap-on cell-phone attachment (left). Based on real-time user feedback in response to the digitally projected patterns, opacity and attenuation maps can be generated to quantify the stage of cataracts (right). Refer to Ref. for further details.

Figure 17

Figure 17

(A–B) An RDT reader prototype powered by the cell-phone battery. A cost-effective snap-on attachment (b) is required to convert a cell phone to a smart digital RDT reader (b), which automatically evaluates various RDTs and generates a detailed RDT report.

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