Real-time corneal segmentation and 3D needle tracking in intrasurgical OCT.

Ophthalmic procedures demand precise surgical instrument control in depth, yet standard operating microscopes supply limited depth perception. Current commercial microscope-integrated optical coherence tomography partially meets this need with manually-positioned cross-sectional images that offer qualitative estimates of depth. In this work, we present methods for automatic quantitative depth measurement using real-time, two-surface corneal segmentation and needle tracking in OCT volumes. We then demonstrate these methods for guidance of ex vivo deep anterior lamellar keratoplasty (DALK) needle insertions. Surgeons using the output of these methods improved their ability to reach a target depth, and decreased their incidence of corneal perforations, both with statistical significance. We believe these methods could increase the success rate of DALK and thereby improve patient outcomes.

Deep learning for the segmentation of preserved photoreceptors on en face optical coherence tomography in two inherited retinal diseases.

The objective quantification of photoreceptor loss in inherited retinal degenerations (IRD) is essential for measuring disease progression, and is now especially important with the growing number of clinical trials. Optical coherence tomography (OCT) is a non-invasive imaging technology widely used to recognize and quantify such anomalies. Here, we implement a versatile method based on a convolutional neural network to segment the regions of preserved photoreceptors in two different IRDs (choroideremia and retinitis pigmentosa) from OCT images. An excellent segmentation accuracy (~90%) was achieved for both IRDs. Due to the flexibility of this technique, it has potential to be extended to additional IRDs in the future.

Optofluidic detection of Zika nucleic acid and protein biomarkers using multimode interference multiplexing.

The recent massive Zika virus (ZIKV) outbreak illustrates the need for rapid and specific diagnostic techniques. Detecting ZIKV in biological samples poses unique problems: antibody detection of ZIKV is insufficient due to cross-reactivity of Zika antibodies with other flaviviruses, and nucleic acid and protein biomarkers for ZIKV are detectable at different stages of infection. Here, we describe a new optofluidic approach for the parallel detection of different molecular biomarkers using multimode interference (MMI) waveguides. We report differentiated, multiplex detection of both ZIKV biomarker types using multi-spot excitation at two visible wavelengths with over 98% fidelity by combining several analysis techniques.

Feasibility of using optical coherence tomography to detect radiation-induced fibrosis and residual cancer extent after neoadjuvant chemo-radiation therapy: an ex vivo study.

Treatment of resectable esophageal cancer includes neoadjuvant chemo-radiation therapy (nCRT) followed by esophagectomy in operable patients. High-risk surgery may have been avoided in patients with a pathological complete response (pCR). We investigated the feasibility of optical coherence tomography (OCT) to detect residual cancer and radiation-induced fibrosis in 10 esophageal cancer patients that underwent nCRT followed by esophagectomy. We compared our OCT findings with histopathology. Overall, OCT was able to differentiate between healthy tissue, fibrotic tissue, and residual cancer with a sensitivity and specificity of 79% and 67%, respectively. Hence, OCT has the potential to add to the assessment of a pCR.

Automated segmentation of retinal layer boundaries and capillary plexuses in wide-field optical coherence tomographic angiography.

Advances in the retinal layer segmentation of structural optical coherence tomography (OCT) images have allowed the separation of capillary plexuses in OCT angiography (OCTA). With the increased scanning speeds of OCT devices and wider field images (≥10 mm on fast-axis), greater retinal curvature and anatomic variations have introduced new challenges. In this study, we developed a novel automated method to segment seven retinal layer boundaries and two retinal plexuses in wide-field OCTA images. The algorithm was initialized by a series of points forming a guidance point array that estimates the location of retinal layer boundaries. A guided bidirectional graph search method consisting of an improvement of our previous segmentation algorithm was used to search for the precise boundaries. We validated the method on normal and diseased eyes, demonstrating subpixel accuracy for all groups. By allowing independent visualization of the superficial and deep plexuses, this method shows potential for the detection of plexus-specific peripheral vascular abnormalities.

Automatic interstitial photodynamic therapy planning via convex optimization.

Finding a high-quality treatment plan is an essential, yet difficult, stage of Photodynamic therapy (PDT) as it will determine the therapeutic efficacy in eradicating malignant tumors. A high-quality plan is patient-specific, and provides clinicians with the number of fiber-based spherical diffusers, their powers, and their interstitial locations to deliver the required light dose to destroy the tumor while minimizing the damage to surrounding healthy tissues. In this work, we propose a general convex light source power allocation algorithm that, given light source locations, guarantees optimality of the resulting solution in minimizing the over/under-dosage of volumes of interest. Furthermore, we provide an efficient framework for source selection with concomitant power reallocation to achieve treatment plans with a clinically feasible number of sources and comparable quality. We demonstrate our algorithms on virtual test cases that model glioblastoma multiforme tumors, and evaluate the performance of four different photosensitizers with different activation wavelengths and specific tissue uptake ratios. Results show an average reduction of the damage to organs-at-risk (OAR) by 29% to 31% with comparable runtime to existing power allocation techniques.

Effect of spatial light distribution on the thermal response of vascular tissue.

The aim of the current study is to investigate the effect of radial and cylindrical light distributions on the response of vascular tissue during 1470-nm irradiation in ex vivo models. Due to a low irradiance (5.3 W/cm2) and wide light distribution, cylindrically diffusing irradiation yielded uniform thermal coagulation while radial irradiation accompanied delamination of layers in leporine veins. Bovine foot model testing verified that the diffusing irradiation was associated with the steady maximum temperature and no tissue attachment, compared with the radial irradiation. The proposed cylindrical light application can be a feasible way to treat varicose veins in an effective manner.

Automatic quantification of choroidal neovascularization lesion area on OCT angiography based on density cell-like P systems with active membranes.

Detecting and quantifying the size of choroidal neovascularization (CNV) is important for the diagnosis and assessment of neovascular age-related macular degeneration. Depth-resolved imaging of the retinal and choroidal vasculature by optical coherence tomography angiography (OCTA) has enabled the visualization of CNV. However, due to the prevalence of artifacts, it is difficult to segment and quantify the CNV lesion area automatically. We have previously described a saliency algorithm for CNV detection that could identify a CNV lesion area with 83% accuracy. However, this method works under the assumption that the CNV region is the most salient area for visual attention in the whole image and consequently, errors occur when this requirement is not met (e.g. when the lesion occupies a large portion of the image). Moreover, saliency image processing methods cannot extract the edges of the salient object very accurately. In this paper, we propose a novel and automatic CNV segmentation method based on an unsupervised and parallel machine learning technique named density cell-like P systems (DEC P systems). DEC P systems integrate the idea of a modified clustering algorithm into cell-like P systems. This method improved the accuracy of detection to 87.2% on 22 subjects and obtained clear boundaries of the CNV lesions.

On the dysfunctional hemoglobins and cyanosis connection: practical implications for the clinical detection and differentiation of methemoglobinemia and sulfhemoglobinemia.

Methemoglobinemia and sulfhemoglobinemia are potentially life-threatening blood-related disorders characterized by similar symptoms and markedly distinct treatment procedures. In this paper, we investigate the causal relationship between these conditions and the onset of cyanosis, which is typically associated with a purple or bluish skin coloration. More specifically, we perform controlled experiments to elicit cyanotic appearances resulting from different severity levels of these disorders and varying physiological conditions. We note that such experiments cannot be induced in living subjects without posing risks to their health. Accordingly, we have resorted to an in silico experimental approach supported by biophysical data reported in the literature. Besides bringing new insights about cyanotic chromatic variations elicited by methemoglobinemia and sulfhemoglobinemia, our investigation provides the basis for the proposition of a cost-effective protocol for the noninvasive detection and differentiation of these disorders. Our experimental results indicate that its sensitivity range is wider than what is provided by similar protocols employed in these tasks. Moreover, it has lower operational requirements than laboratory tests ordered to enable the diagnosis of these conditions. We believe that these aspects make the proposed protocol particularly suitable for deployment at the point of care of medical settings with limited access to laboratory resources.

Real-time localization of the parathyroid gland in surgical field using Raspberry Pi during thyroidectomy: a preliminary report.

We created an auto-para viewer, an autofluorescence imaging device, to localize the parathyroid glands during thyroidectomy using an inexpensive Raspberry Pi. A special emission filter in the auto-para viewer was designed to pass 1/100 of visible light and nearly all infrared light longer than 808 nm. With this emission filter, we simultaneously acquired an autofluorescence image of the parathyroid and a visible light image of the surrounding surgical field. The auto-para viewer displayed four times brighter autofluorescence of the parathyroid glands compared to the background tissues without operating room light. Additionally, it showed two times brighter autofluorescence than the background tissues simultaneously showing the surgical field illuminated by the visible light from the operating room light. The NOIR camera, using the auto-para viewer, could reduce the camera's exposure time so the parathyroid glands to be viewed in real-time, which is expected to prevent unintentional damage to the parathyroid gland during thyroidectomy.

Cross-section and feasibility study on the non-invasive evaluation of muscle hemodynamic responses in Duchenne muscular dystrophy by using a near-infrared diffuse optical technique.

Duchenne muscular dystrophy (DMD) is an X-linked debilitating muscular disease that may decrease nitric oxide (NO) production and lead to functional muscular ischemia. Currently, the 6-minute walk test (6-MWT) and the North Star Ambulatory Assessment (NSAA) are the primary outcome measures in clinical trials, but they are severely limited by the subjective consciousness and mood of patients, and can only be used in older and ambulatory boys. This study proposed using functional near-infrared spectroscopy (fNIRS) to evaluate the dynamic changes in muscle hemodynamic responses (gastrocnemius and forearm muscle) during a 6-MWT and a venous occlusion test (VOT), respectively. Muscle oxygenation of the forearm was evaluated non-invasively before, during and after VOT in all participants (included 30 DMD patients and 30 age-matched healthy controls), while dynamic muscle oxygenation of gastrocnemius muscle during 6-MWT was determined in ambulatory participants (n = 18) and healthy controls (n = 30). The results reveal that impaired muscle oxygenation was observed during 6-MWT in DMD patients that may explain why the DMD patients walked shorter distances than healthy controls. Moreover, the results of VOT implied that worsening muscle function was associated with a lower supply of muscle oxygenation and may provide useful information on the relationship between muscular oxygen consumption and supply for the clinical diagnosis of DMD. Therefore, the method of fNIRS with VOT possesses great potential in future evaluations of DMD patients that implies a good feasibility for clinical application such as for monitoring disease severity of DMD.

Multiple surface segmentation using convolution neural nets: application to retinal layer segmentation in OCT images.

Automated segmentation of object boundaries or surfaces is crucial for quantitative image analysis in numerous biomedical applications. For example, retinal surfaces in optical coherence tomography (OCT) images play a vital role in the diagnosis and management of retinal diseases. Recently, graph based surface segmentation and contour modeling have been developed and optimized for various surface segmentation tasks. These methods require expertly designed, application specific transforms, including cost functions, constraints and model parameters. However, deep learning based methods are able to directly learn the model and features from training data. In this paper, we propose a convolutional neural network (CNN) based framework to segment multiple surfaces simultaneously. We demonstrate the application of the proposed method by training a single CNN to segment three retinal surfaces in two types of OCT images - normal retinas and retinas affected by intermediate age-related macular degeneration (AMD). The trained network directly infers the segmentations for each B-scan in one pass. The proposed method was validated on 50 retinal OCT volumes (3000 B-scans) including 25 normal and 25 intermediate AMD subjects. Our experiment demonstrated statistically significant improvement of segmentation accuracy compared to the optimal surface segmentation method with convex priors (OSCS) and two deep learning based UNET methods for both types of data. The average computation time for segmenting an entire OCT volume (consisting of 60 B-scans each) for the proposed method was 12.3 seconds, demonstrating low computation costs and higher performance compared to the graph based optimal surface segmentation and UNET based methods.

Comparison of speckleplethysmographic (SPG) and photoplethysmographic (PPG) imaging by Monte Carlo simulations and in vivo measurements.

Noncontact photoplethysmography (PPG) is limited by a poor signal-to-noise ratio (SNR). A solution to this limitation is the use of alternate sources of optical contrast to generate a complementary pulsatile waveform. One such source is laser speckle contrast, which is modulated in biological tissues by the flow rate of red blood cells. Averaging a region of interest from a speckle contrast image over time allows for the calculation of a speckleplethysmogram (SPG). Similar to PPG, SPG enables monitoring of heart rate and respiratory rate. A gap in the knowledge base exists as to the precise spatiotemporal relationship between PPG and SPG signals. We have developed an eight-layer tissue model to simulate both PPG and SPG signals in a reflectance geometry via Monte Carlo methods. We modeled PPG by compression of the upper and lower blood nets due to expansion of the larger arterial layer below. The in silico PPG peak-to-peak amplitude percent was greater at 532 nm than at 860 nm (5.6% vs. 3.0%, respectively), which matches trends from the literature. We modeled SPG by changing flow speeds of red blood cells in both the capillaries and arterioles over the cardiac cycle. The in silico SPG peak-to-peak amplitude percent was 24% at 532 nm and 40% at 860 nm. In silico results are similar to in vivo results measured with a two-camera set up for simultaneous imaging of PPG and SPG. Both in silico and in vivo data suggest SPG has a much larger SNR than PPG, which may prove beneficial for noncontact, wide-field optical monitoring of cardiovascular health.

Ex vivo classification of spinal cord tumors using autofluorescence spectroscopy and immunohistochemical indexes.

Spinal cord tumors are complicated and infrequent, which poses a major challenge to surgeons during neurosurgery. Currently, the intraoperative identification of the tissues' pathological properties is usually difficult for surgeons. This issue influences the decision-making in treatment planning. Traditional pathological diagnoses can facilitate judging the tissues' properties, but the diagnosis process is complex and time-consuming. In this study, we evaluated the potential of autofluorescence spectroscopy for the fast pathological diagnosis of specific spinal cord tumors. The spectral properties of six types of spinal cord tumors were acquired ex vivo. Several peak intensity ratios were calculated for classification and then associated with the pathological immunohistochemical indexes. Our results revealed the spectral properties of three types of intramedullary tumors different from those of the other three types of extramedullary tumors. Furthermore, some peak intensity ratios revealed a high correlation with the immunohistochemical index of glial fibrillary acidic protein (GFAP). Thus, we believe that autofluorescence spectroscopy has the potential to provide real-time pathological information of spinal cord tumors and help surgeons validate tumor types and perform precise tumor resection.

Concurrent measurement of skeletal muscle blood flow during exercise with diffuse correlation spectroscopy and Doppler ultrasound.

Noninvasive, direct measurement of local muscle blood flow in humans remains limited. Diffuse correlation spectroscopy (DCS) is an emerging technique to measure regional blood flow at the microvascular level. In order to better understand the strengths and limitations of this novel technique, we performed a validation study by comparing muscle blood flow changes measured with DCS and Doppler ultrasound during exercise. Nine subjects were measured (all males, 27.4 ± 2.9 years of age) for a rhythmic handgrip exercise at 20% and 50% of individual maximum voluntary contraction (MVC), followed by a post-exercise recovery. The results from DCS and Doppler ultrasound were highly correlated (R = 0.99 ± 0.02). DCS was more reliable and less susceptible to motion artifact.

Pneumatic low-coherence interferometry otoscope to quantify tympanic membrane mobility and middle ear pressure.

Pneumatic otoscopy to assess the mobility of the tympanic membrane (TM) is a highly recommended diagnostic method of otitis media (OM), a widespread middle ear infection characterized by the fluid accumulation in the middle ear. Nonetheless, limited depth perception and subjective interpretation of small TM displacements have challenged the appropriate and efficient examination of TM dynamics experienced during OM. In this paper, a pneumatic otoscope integrated with low coherence interferometry (LCI) was adapted with a controlled pressure-generating system to record the pneumatic response of the TM and to estimate middle ear pressure (MEP). Forty-two ears diagnosed as normal (n = 25), with OM (n = 10), or associated with an upper respiratory infection (URI) (n = 7) were imaged with a pneumatic LCI otoscope with an axial, transverse, and temporal resolution of 6 µm, 20 µm, and 1 msec, respectively. The TM displacement under pneumatic pressure transients (a duration of 0.5 sec with an intensity of ± 150 daPa) was measured to compute two metrics (compliance and amplitude ratio). These metrics were correlated with peak acoustic admittance and MEP from tympanometry and statistically compared via Welch's t-test. As a result, the compliance represents pneumatic TM mobility, and the amplitude ratio estimates MEP. The presence of a middle ear effusion (MEE) significantly decreased compliance (p<0.001). The amplitude ratio of the OM group was statistically less than that of the normal group (p<0.01), indicating positive MEP. Unlike tympanometry, pneumatic LCI otoscopy quantifies TM mobility as well as MEP regardless of MEE presence. With combined benefits of pneumatic otoscopy and tympanometry, pneumatic LCI otoscopy may provide new quantitative metrics for understanding TM dynamics and diagnosing OM.

Diffuse optical monitoring of peripheral tissues during uncontrolled internal hemorrhage in a porcine model.

Reliable, continuous and noninvasive blood flow and hemoglobin monitoring in trauma patients remains a critical, but generally unachieved goal. Two optical sensing methods - diffuse correlation spectroscopy (DCS) and diffuse reflectance spectroscopy (DRS) - are used to monitor and detect internal hemorrhage. Specifically, we investigate if cutaneous perfusion measurements acquired using DCS and DRS in peripheral (thighs and ear-lobe) tissues could detect severe hemorrhagic shock in a porcine model. Four animals underwent high-grade hepato-portal injury in a closed abdomen, to induce uncontrolled hemorrhage and were subsequently allowed to bleed for 10 minutes before fluid resuscitation. DRS and DCS measurements of cutaneous blood flow were acquired using fiber optical probes placed on the thigh and earlobe of the animals and were obtained repeatedly starting from 1 to 5 minutes pre-injury, up to several minutes post shock. Clear changes were observed in measured optical spectra across all animals at both sites. DCS-derived cutaneous blood flow decreased sharply during hemorrhage, while DRS-derived vascular saturation and hemoglobin paralleled cardiac output. All derived optical parameters had the steepest changes during the rapid initial hemorrhage unambiguously. This suggests that a combined DCS and DRS based device might provide an easy-to-use, non-invasive, internal-hemorrhage detection system that can be used across a wide array of clinical settings.

Erratum: Raman spectral post-processing for oral tissue discrimination - a step for an automatized diagnostic system: erratum.

[This corrects the article on p. 5218 in vol. 8, PMID: 29188115.].

Diffuse reflectance spectroscopy of human liver tumor specimens - towards a tissue differentiating optical biopsy needle using light emitting diodes.

Significant numbers of liver biopsies fail to yield representative tissue samples. This study was conducted to evaluate the ability of LED-based diffuse reflectance spectroscopy to discriminate tumors from liver parenchyma. Ex vivo spectra were acquired from malignant lesions and liver parenchyma of 32 patients who underwent liver resection using a white light source and several LEDs. Integrated spectra of two combined LEDs with emission peaks at 470 nm and 515 nm were classified with 98.4% sensitivity and 99.2% specificity. The promising results could yield to a simple handheld and cost-efficient tool for real-time tissue differentiation implemented in a biopsy needle.

Comparison of source localization techniques in diffuse optical tomography for fNIRS application using a realistic head model.

Functional near-infrared spectroscopy (fNIRS) is a non-invasive imaging technique that elicits growing interest for research and clinical applications. In the last decade, efforts have been made to develop a mathematical framework in order to image the effective sources of hemoglobin variations in brain tissues. Different approaches can be used to impose additional information or constraints when reconstructing the cerebral images of an ill-posed problem. The goal of this study is to compare the performance and limitations of several source localization techniques in the context of fNIRS tomography using individual anatomical magnetic resonance imaging (MRI) to model light propagation. The forward problem is solved using a Monte Carlo simulation of light propagation in the tissues. The inverse problem has been linearized using the Rytov approximation. Then, Tikhonov regularization applied to least squares, truncated singular value decomposition, back-projection, L1-norm regularization, minimum norm estimates, low resolution electromagnetic tomography and Bayesian model averaging techniques are compared using a receiver operating characteristic analysis, blurring and localization error measures. Using realistic simulations (n = 450) and data acquired from a human participant, this study depicts how these source localization techniques behave in a human head fNIRS tomography. When compared to other methods, Bayesian model averaging is proposed as a promising method in DOT and shows great potential to improve specificity, accuracy, as well as to reduce blurring and localization error even in presence of noise and deep sources. Classical reconstruction methods, such as regularized least squares, offer better sensitivity but higher blurring; while more novel L1-based method provides sparse solutions with small blurring and high specificity but lower sensitivity. The application of these methods is also demonstrated experimentally using visual fNIRS experiment with adult participant.