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Dual-energy computed tomography (CT) can be used in radiotherapy treatment planning for the calculation of absorbed dose distributions. The aim of this work is to evaluate whether there is room for improvement in the accuracy of the Monoenergetic Plus algorithm by Siemens Healthineers. A Siemens SOMATOM Force scanner was used to scan a cylindrical polymethyl methacrylate phantom with four rod-inserts made of different materials. Images were reconstructed using ADMIRE and processed with Monoenergetic Plus. The resulting CT numbers were compared with tabulated values and values simulated by the proof-of-a-concept algorithm DIRA developed by the authors. Both the Monoenergetic Plus and DIRA algorithms performed well; the accuracy of attenuation coefficients was better than about ±1% at the energy of 70 keV. Compared with DIRA, the worse performance of Monoenergetic Plus was caused by its (i) two-material decomposition to iodine and water and (ii) imperfect suppression of the beam hardening artifact in ADMIRE. Funding: CancerfondenSwedish Cancer Society [CAN 2017/1029, CAN 2018/622]; Swedish state government [LiO-602731]; Swedish county councils, the ALF-agreement [LiO-602731]; Patientsakerhetsforskning Region Ostergotland [LiO-724181]; VetenskapsradetSwedish Research Council [VR-NT 2016-05033]

Denoising reconstruction techniques can introduce nonlinear properties into computed tomography (CT) systems. These nonlinear algorithms introduce distortion which affects the assessment of the resolution of the system. The purpose of the present study was to decouple and investigate amplitude modulation and waveform distortion in reconstruction algorithms in CT. The methodology developed by Wells, J. R. and Dobbins, J. T. III [Frequency response and distortion properties of nonlinear image processing algorithms and the importance of imaging context. Med. Phys. 40, 091906 (2013)] was adapted to CT reconstruction algorithms. The CT simulating program ASTRA Toolbox© for MATLAB™ was used for the reconstruction of the sinusoidal wave functions. Filtered back projection and the simultaneous iterative reconstruction technique were investigated with simple nonlinear mechanisms: a median filter and a non-negative constraint, respectively. The native reconstruction algorithms were not free from nonlinear waveform distortion, however, none of the metrics showed any dependence on the contrast-to-noise ratio (CNR). Furthermore, the algorithms including nonlinear mechanisms showed a clear and specific CNR dependence, indicating the necessity for distortion analysis in nonlinear CT reconstruction.

Objective. Neuroadaptive paradigms that systematically assess event-related potential (ERP) features across many different experimental parameters have the potential to improve the generalizability of ERP findings and may help to accelerate ERP-based biomarker discovery by identifying the exact experimental conditions for which ERPs differ most for a certain clinical population. Obtaining robust and reliable ERPs online is a prerequisite for ERP-based neuroadaptive research. One of the key steps involved is to correctly isolate electroencephalography artifacts in real time because they contribute a large amount of variance that, if not removed, will greatly distort the ERP obtained. Another key factor of concern is the computational cost of the online artifact handling method. This work aims to develop and validate a cost-efficient system to support ERP-based neuroadaptive research. Approach. We developed a simple online artifact handling method, single trial PCA-based artifact removal (SPA), based on variance distribution dichotomies to distinguish between artifacts and neural activity. We then applied this method in an ERP-based neuroadaptive paradigm in which Bayesian optimization was used to search individually optimal inter-stimulus-interval (ISI) that generates ERP with the highest signal-to-noise ratio. Main results. SPA was compared to other offline and online algorithms. The results showed that SPA exhibited good performance in both computational efficiency and preservation of ERP pattern. Based on SPA, the Bayesian optimization procedure was able to quickly find individually optimal ISI. Significance. The current work presents a simple yet highly cost-efficient method that has been validated in its ability to extract ERP, preserve ERP effects, and better support ERP-based neuroadaptive paradigm.

Significance: Optical properties (absorption coefficient and scattering coefficient) of tissue are the most critical parameters for disease diagnosis-based optical method. In recent years, researchers proposed spatial frequency domain imaging (SFDI) to quantitatively map tissue optical properties in a broad field of contactless imaging. To solve the limitations in wavebands unsuitable for silicon-based sensor technology, a compressed sensing (CS) algorithm is used to reproduce the original signal by a single-pixel detectors. Currently, the existing single-pixel SFDI method mainly uses a random sampling policy to extract and recover signals in the acquisition stage. However, these methods are memory-hungry and time-consuming, and they cannot generate discernible results under low sampling rate. Explorations on high performance and efficiency single-pixel SFDI are of great significance for clinical application. Aim: Fourier single-pixel imaging can reconstruct signals with less time and space costs and has fewer reconstruction errors. We focus on an SFDI algorithm based on Fourier single-pixel imaging and propose our Fourier single-pixel image-based spatial frequency domain imaging method (FSI-SFDI). Approach: First, we use Fourier single-pixel imaging algorithm to collect and compress signals and SFDI algorithm to generate optical parameters. Given the basis that the main energy of general image signals is concentrated in the range of low frequency of Fourier frequency domain, our FSI-SFDI uses a circular-sampling scheme to sample data points in the low-frequency region. Then, we reconstruct the image details from these points by optimization-based inverse-FFT method. Results: Our algorithm is tested on simulated data. Results show that the root mean square error (RMSE) of optical parameters is lower than 5% when the data reduction is 92%, and it can generate discernible optical parameter image with low sampling rate. We can observe that our FSI-SFDI primarily recovers the optical properties while keeping the RMSE under the upper bound of 4.5% when we use an image with 512×512 resolution as the example for calculation and analysis. Not only that but also our algorithm consumes less space and time for an image with 256×256 resolution, the signal reconstruction takes only 1.65 ms, and requires less RAM memory. Compared to CS-SFDI method, our FSI-SFDI can reduce the required number of measurements through optimizing algorithm. Conclusions: Moreover, FSI-SFDI is capable of recovering high-quality resolvable images with lower sampling rate, higher-resolution images with less memory and time consumed than previous CS-SFDI method, which is very promising for clinical data collection and medical analysis.

Paper spray ionization has been used as a fast sampling/ionization method for the direct mass spectrometric analysis of biological samples at ambient conditions. Here, we demonstrated that by utilizing paper spray ionization-mass spectrometry (PSI-MS) coupled with field asymmetric waveform ion mobility spectrometry (FAIMS), predictive metabolic and lipidomic profiles of routine breast core needle biopsies could be obtained effectively. By the combination of machine learning algorithms and pathological examination reports, we developed a classification model, which has an overall accuracy of 87.5% for an instantaneous differentiation between cancerous and noncancerous breast tissues utilizing metabolic and lipidomic profiles. Our results suggested that paper spray ionization-ion mobility spectrometry-mass spectrometry (PSI-IMS-MS) is a powerful approach for rapid breast cancer diagnosis based on altered metabolic and lipidomic profiles.

Significance: High-speed three-dimensional (3D) super-resolution microscopy is a unique tool to investigate various biological phenomena; yet the technology is not broadly adopted due to its high cost and complex system design. Aim: We present a compact, low-cost, and high-speed 3D structured illumination microscopy (SIM) based on a digital micromirror device and binary holography to visualize fast biological events with super-resolution. Approach: The 3D SIM uses a digital micromirror device to generate three laser foci with individually controllable positions, phases, and amplitudes via binary holography at the back aperture of objective lens to form optimal 3D structured patterns. Fifteen raw images are sequentially recorded and processed by the 3D SIM algorithm to reconstruct a super-resolved image. Results: Super-resolution 3D imaging at a speed of 26.7 frames per second is achieved with a lateral and axial resolution of 155 and 487 nm, which corresponds to a 1.65- and 1.63-times resolution enhancement, respectively, comparing with standard deconvolution microscopy. Conclusions: The 3D SIM realizes fast super-resolution imaging with optimal 3D structured illumination, which may find important applications in biophotonics.

Significance: Fourier ptychography microscopy (FPM) is a computational optical imaging technology that employs angularly varying illuminations and a phase retrieval algorithm to achieve a wide field of view and high-resolution imaging simultaneously. In the FPM, LED position error will reduce the quality of the reconstructed high-resolution image. To correct the LED positions, current methods consider each of the LED positions as independent and use an optimization algorithm to get each of the positions. When the positional misalignment is large or the search position falls into a local optimal value, the current methods may lack stability and accuracy. Aim: We improve the model of the LED position and propose an accurate and stable two-step correction scheme (tcFPM) to calibrate the LED position error. Approach: The improved LED positions model combines the overall offset, which represents the relative deviation of the LED array and the optical axis, with the slight deviation of each LED’s independent position. In the tcFPM, the overall offset of the LED array is corrected at first, which obtains an approximate value of the overall offset of the LED array. Then the position of each LED is precisely adjusted, which obtains the slight offset of each LED. Results: This LED position error model is more in line with the actual situation. The simulation and experimental results show that the method has high accuracy in correcting the LED position. Furthermore, the reconstruction process of tcFPM is more stable and significantly improves the quality of the reconstruction results, which is compared with some LED position error correction methods. Conclusions: An LED position error correction technology is proposed, which has a stable iterative process and improves the reconstruction accuracy of complex amplitude.

Significance: Early detection of oral cancer is vital for high-risk patients, and machine learning-based automatic classification is ideal for disease screening. However, current datasets collected from high-risk populations are unbalanced and often have detrimental effects on the performance of classification. Aim: To reduce the class bias caused by data imbalance. Approach: We collected 3851 polarized white light cheek mucosa images using our customized oral cancer screening device. We use weight balancing, data augmentation, undersampling, focal loss, and ensemble methods to improve the neural network performance of oral cancer image classification with the imbalanced multi-class datasets captured from high-risk populations during oral cancer screening in low-resource settings. Results: By applying both data-level and algorithm-level approaches to the deep learning training process, the performance of the minority classes, which were difficult to distinguish at the beginning, has been improved. The accuracy of “premalignancy” class is also increased, which is ideal for screening applications. Conclusions: Experimental results show that the class bias induced by imbalanced oral cancer image datasets could be reduced using both data- and algorithm-level methods. Our study may provide an important basis for helping understand the influence of unbalanced datasets on oral cancer deep learning classifiers and how to mitigate.

Significance: As linear array transducers are widely used in clinical ultrasound imaging, photoacoustic tomography (PAT) with linear arrays is similarly suitable for clinical applications. However, due to the limited-view problem, a linear array has limited performance and leads to artifacts and blurring, which has hindered its broader application. There is a need to address the limited-view problem in PAT imaging with linear arrays. Aim: We investigate potential approaches for improving PAT reconstruction from linear array, by optimizing the detection geometry and implementing iterative reconstruction. Approach: PAT imaging with a single-array, dual-probe configurations in parallel-shape and L-shape, and square-shape configuration are compared in simulations and phantom experiments. An iterative model-based algorithm based on the variance-reduced stochastic gradient descent (VR-SGD) method is implemented. The optimum configuration found in simulation is validated on phantom experiments. Results: PAT imaging with dual-probe detection and VR-SGD algorithm is found to improve the limited-view problem compared to a single probe and provide comparable performance as full-view geometry in simulation. This configuration is validated in experiments where more complete structure is obtained with reduced artifacts compared with a single array. Conclusions: PAT with dual-probe detection and iterative reconstruction is a promising solution to the limited-view problem of linear arrays.

This work was supported by the EPSRC (Grant No. EP/K503162/1). Significance : Optical microscopy is characterized by the ability to get high resolution, below 1 μm, high contrast, functional and quantitative images. The use of shaped illumination, such as with lightsheet microscopy, has led to greater three-dimensional isotropic resolution with low phototoxicity. However, in most complex samples and tissues, optical imaging is limited by scattering. Many solutions to this issue have been proposed, from using passive approaches such as Bessel beam illumination to active methods incorporating aberration correction, but making fair comparisons between different approaches has proven to be challenging. Aim : We present a phase-encoded Monte Carlo radiation transfer algorithm (φMC) capable of comparing the merits of different illumination strategies or predicting the performance of an individual approach. Approach : We show that φMC is capable of modeling interference phenomena such as Gaussian or Bessel beams and compare the model with experiment. Results : Using this verified model, we show that, for a sample with homogeneously distributed scatterers, there is no inherent advantage to illuminating a sample with a conical wave (Bessel beam) instead of a spherical wave (Gaussian beam), except for maintaining a greater depth of focus. Conclusion : φMC is adaptable to any illumination geometry, sample property, or beam type (such as fractal or layered scatterer distribution) and as such provides a powerful predictive tool for optical imaging in thick samples. Publisher PDF

Significance: Artificial skin (AS) is widely used in dermatology, pharmacology, and toxicology, and has great potential in transplant medicine, burn wound care, and chronic wound treatment. There is a great demand for high-quality AS product and a non-invasive detection method is highly desirable. Aim: To quantify the constructure parameters (i.e., thickness and surface roughness) of AS samples in the culture cycle and explore the growth regularities using optical coherent tomography (OCT). Approach: An adaptive interface detection algorithm is developed to recognize surface points in each A-scan, offering a rapid method to calculate parameters without constructing OCT B-scan pictures and further achieving realizing real-time quantification of AS thickness and surface roughness. Experiments on standard roughness plates and H&E-staining microscopy were performed as a verification. Results: As applied on the whole cycle of AS culture, our method’s results show that during the air–liquid culture, the surface roughness of the skin first decreases and then exhibits an increase, which implies coincidence with the degree of keratinization under a microscope. And normal and typical abnormal samples can be differentiated by thickness and roughness parameters during the culture cycle. Conclusions: The adaptive interface detection algorithm is suitable for high-sensitivity, fast detection, and quantification of the interface with layered characteristic tissues, and can be used for non-destructive detection of the growth regularity of AS sample thickness and roughness during the culture cycle.

Significance: Polyp size is important for selecting the surveillance interval or treatment policy. Nevertheless, it is challenging to accurately estimate the polyp size during endoscopy. An easy and cost-effective function to assist in polyp size estimation is required. Aim: To propose a virtual scale function for endoscopy and evaluate its performance and expected accuracy. Approach: An adaptive virtual scale behavior was demonstrated. The measurement error of the virtual scale along the distance between the tip of the endoscope and the object plane was evaluated using graph paper. The accuracy of polyp size estimation by an expert endoscopist was compared with the accuracy of the biopsy forceps method using phantom images. Results: The measurement errors of the virtual scale were ≤ 0.7 mm when the distance to the graph paper, which faced the tip of the endoscope, varied from 4 to 30 mm. The accuracy with the virtual scale was significantly higher than that obtained with biopsy forceps (5.3 ± 5.5 % versus 11.9 ± 9.4 % , P

Quantifying parenchymal tissue changes in the lungs is imperative in furthering the study of radiation induced lung damage (RILD). Registering lung images from different time-points is a key step of this process. Traditional intensity-based registration approaches fail this task due to the considerable anatomical changes that occur between timepoints. This work proposes a novel method to successfully register longitudinal pre- and post-radiotherapy (RT) lung computed tomography (CT) scans that exhibit large changes due to RILD, by extracting consistent anatomical features from CT (lung boundaries, main airways, vessels) and using these features to optimise the registrations. Pre-RT and 12 month post-RT CT pairs from fifteen lung cancer patients were used for this study, all with varying degrees of RILD, ranging from mild parenchymal change to extensive consolidation and collapse. For each CT, signed distance transforms from segmentations of the lungs and main airways were generated, and the Frangi vesselness map was calculated. These were concatenated into multi-channel images and diffeomorphic multichannel registration was performed for each image pair using NiftyReg. Traditional intensity-based registrations were also performed for comparison purposes. For the evaluation, the pre- and post-registration landmark distance was calculated for all patients, using an average of 44 manually identified landmark pairs per patient. The mean (standard deviation) distance for all datasets decreased from 15.95 (8.09) mm pre-registration to 4.56 (5.70) mm post-registration, compared to 7.90 (8.97) mm for the intensity-based registrations. Qualitative improvements in image alignment were observed for all patient datasets. For four representative subjects, registrations were performed for three additional follow-up timepoints up to 48 months post-RT and similar accuracy was achieved. We have demonstrated that our novel multichannel registration method can successfully align longitudinal scans from RILD patients in the presence of large anatomical changes such as consolidation and atelectasis, outperforming the traditional registration approach both quantitatively and through thorough visual inspection.

Significance: Quantitative stiffness information can be a powerful aid for tumor or fibrosis diagnosis. Currently, very promising elastography approaches developed for non-contact biomedical imaging are based on transient shear-waves imaging. Transient elastography offers quantitative stiffness information by tracking the propagation of a wave front. The most common method used to compute stiffness from the acquired propagation movie is based on shear-wave time-of-flight calculations. Aim: We introduce an approach to transient shear-wave elastography with spatially coherent sources, able to yield full-field quantitative stiffness maps with reduced artifacts compared to typical artifacts observed in time-of-flight. Approach: A noise-correlation algorithm developed for passive elastography is adapted to spatially coherent narrow or any band sources. This noise-correlation-inspired (NCi) method is employed in parallel with a classic time-of-flight approach. Testing is done on simulation images, experimental validation is conducted with a digital holography setup on controlled homogeneous samples, and full-field quantitative stiffness maps are presented for heterogeneous samples and ex-vivo biological tissues. Results: The NCi approach is first validated on simulations images. Stiffness images processed by the NCi approach on simulated inclusions display significantly less artifacts than with a time-of-flight reconstruction. The adaptability of the NCi algorithm to narrow or any band shear-wave sources was tested successfully. Experimental testing on homogeneous samples demonstrates similar values for both the time-of-flight and the NCi approach. Soft inclusions in agarose sample could be resolved using the NCi method and feasibility on ex-vivo biological tissues is presented. Conclusions: The presented NCi approach was successful in computing quantitative full-field stiffness maps with narrow and broadband source signals on simulation and experimental images from a digital holography setup. Results in heterogeneous media show that the NCi approach could provide stiffness maps with less artifacts than with time-of-flight, demonstrating that a NCi algorithm is a promising approach for shear-wave transient elastography with spatially coherent sources.

Significance: Screening and early detection of oral potentially malignant lesions (OPMLs) are of great significance in reducing the mortality rates associated with head and neck malignancies. Intra-oral multispectral optical imaging of tissues in conjunction with cloud-based machine learning (CBML) can be used to detect oral precancers at the point-of-care (POC) and guide the clinician to the most malignant site for biopsy. Aim: Develop a bimodal multispectral imaging system (BMIS) combining tissue autofluorescence and diffuse reflectance (DR) for mapping changes in oxygenated hemoglobin (HbO2) absorption in the oral mucosa, quantifying tissue abnormalities, and guiding biopsies. Approach: The hand-held widefield BMIS consisting of LEDs emitting at 405, 545, 575, and 610 nm, 5MPx monochrome camera, and proprietary Windows-based software was developed for image capture, processing, and analytics. The DR image ratio (R610/R545) was compared with pathologic classification to develop a CBML algorithm for real-time assessment of tissue status at the POC. Results: Sensitivity of 97.5% and specificity of 92.5% were achieved for discrimination of OPML from patient normal in 40 sites, whereas 82% sensitivity and 96.6% specificity were obtained for discrimination of abnormal (OPML + SCC) in 89 sites. Site-specific algorithms derived for buccal mucosa (27 sites) showed improved sensitivity and specificity of 96.3% for discrimination of OPML from normal. Conclusions: Assessment of oral cancer risk is possible by mapping of HbO2 absorption in tissues, and the BMIS system developed appears to be suitable for biopsy guidance and early detection of oral cancers.

Significance: The Mueller matrix decomposition method is widely used for the analysis of biological samples. However, its presumed sequential appearance of the basic optical effects (e.g., dichroism, retardance, and depolarization) limits its accuracy and application. Aim: An approach is proposed for detecting and classifying human melanoma and non-melanoma skin cancer lesions based on the characteristics of the Mueller matrix elements and a random forest (RF) algorithm. Approach: In the proposal technique, 669 data points corresponding to the 16 elements of the Mueller matrices obtained from 32 tissue samples with squamous cell carcinoma (SCC), basal cell carcinoma (BCC), melanoma, and normal features are input into an RF classifier as predictors. Results: The results show that the proposed model yields an average precision of 93%. Furthermore, the classification results show that for biological tissues, the circular polarization properties (i.e., elements m44, m34, m24, and m14 of the Mueller matrix) dominate the linear polarization properties (i.e., elements m13, m31, m22, and m41 of the Mueller matrix) in determining the classification outcome of the trained classifier. Conclusions: Overall, our study provides a simple, accurate, and cost-effective solution for developing a technique for classification and diagnosis of human skin cancer.

Significance: Speckle noise limits the diagnostic capabilities of optical coherence tomography (OCT) images, causing both a reduction in contrast and a less accurate assessment of the microstructural morphology of the tissue. Aim: We present a speckle-noise reduction method for OCT volumes that exploits the advantages of adaptive-noise wavelet thresholding with a wavelet compounding method applied to several frames acquired from consecutive positions. The method takes advantage of the wavelet representation of the speckle statistics, calculated properly from a homogeneous sample or a region of the noisy volume. Approach: The proposed method was first compared quantitatively with different state-of-the-art approaches by being applied to three different clinical dermatological OCT volumes with three different OCT settings. The method was also applied to a public retinal spectral-domain OCT dataset to demonstrate its applicability to different imaging modalities. Results: The results based on four different metrics demonstrate that the proposed method achieved the best performance among the tested techniques in suppressing noise and preserving structural information. Conclusions: The proposed OCT denoising technique has the potential to adapt to different image OCT settings and noise environments and to improve image quality prior to clinical diagnosis based on visual assessment.

Reducing radiation-induced side effects is one of the most important challenges in paediatric cancer treatment. Recently, there has been growing interest in using spatial normalisation to enable voxel-based analysis of radiation-induced toxicities in a variety of patient groups. The need to consider three-dimensional distribution of doses, rather than dose-volume histograms, is desirable but not yet explored in paediatric populations. In this paper, we investigate the feasibility of atlas construction and spatial normalisation in paediatric radiotherapy. We used planning computed tomography (CT) scans from twenty paediatric patients historically treated with craniospinal irradiation to generate a template CT that is suitable for spatial normalisation. This childhood cancer population representative template was constructed using groupwise image registration. An independent set of 53 subjects from a variety of childhood malignancies was then used to assess the quality of the propagation of new subjects to this common reference space using deformable image registration (i.e. spatial normalisation). The method was evaluated in terms of overall image similarity metrics, contour similarity and preservation of dose-volume properties. After spatial normalisation, we report a dice similarity coefficient of 0.95 ± 0.05, 0.85 ± 0.04, 0.96 ± 0.01, 0.91 ± 0.03, 0.83 ± 0.06 and 0.65 ± 0.16 for brain and spinal canal, ocular globes, lungs, liver, kidneys and bladder. We then demonstrated the potential advantages of an atlas-based approach to study the risk of second malignant neoplasms after radiotherapy. Our findings indicate satisfactory mapping between a heterogeneous group of patients and the template CT. The poorest performance was for organs in the abdominal and pelvic region, likely due to respiratory and physiological motion and to the highly deformable nature of abdominal organs. More specialised algorithms should be explored in the future to improve mapping in these regions. This study is the first step toward voxel-based analysis in radiation-induced toxicities following paediatric radiotherapy.

Significance: Dental caries is the most common oral disease, with significant effects on healthcare systems and quality of life. Developing diagnostic methods for early caries detection is key to reducing this burden and enabling non-invasive treatment as opposed to the drill-and-fill approach. Aim: The application of a thermophotonic-based 3D imaging modality [enhanced truncated-correlation photothermal coherence tomography (eTC-PCT)] to early dental caries is investigated. To this end, the detection threshold, sensitivity, and 3D lesion reconstruction capability of eTC-PCT in imaging artificially generated caries and surface erosion are evaluated. Approach: eTC-PCT employs a diode laser with pulsed excitation, a mid-IR camera, and an in-house developed image reconstruction algorithm to produce depth-resolved 2D images and 3D reconstructions. Starting with healthy teeth, dental caries and surface erosion are simulated in vitro through application of specific demineralizing/eroding acidic solutions. Results: eTC-PCT can detect artificial caries as early as 2 days after onset of artificial demineralization and after 45 s of surface erosion, with a laser power equivalent to 64% of maximum permissible exposure. In both cases, the lesion is not visible to the eye and undetected by x-rays. eTC-PCT is capable of monitoring lesion progression in 2-day increments and generating 3D tomographic reconstructions of the advancing lesion. Conclusions: eTC-PCT shows great potential for further development as a dental imaging modality combining low detection threshold, high sensitivity to lesion progression, 3D reconstruction capability, and lack of ionizing radiation. These features enable early diagnosis and frequent monitoring, making eTC-PCT a promising technology for facilitating preventive dentistry.

Significance: Coherence, a fundamental property of waves and fields, plays a key role in photoacoustic image reconstruction. Previously, techniques such as short-lag spatial coherence (SLSC) and filtered delay, multiply, and sum (FDMAS) have utilized spatial coherence to improve the reconstructed resolution and contrast with respect to delay-and-sum (DAS). While SLSC uses spatial coherence directly as the imaging contrast, FDMAS employs spatial coherence implicitly. Despite being more robust against noise, both techniques have their own drawbacks: SLSC does not preserve a relative signal magnitude, and FDMAS shows a reduced contrast-to-noise ratio. Aim: To overcome these limitations, our aim is to develop a beamforming algorithm—generalized spatial coherence (GSC)—that unifies SLSC and FDMAS into a single equation and outperforms both beamformers. Approach: We demonstrated the application of GSC in photoacoustic computed tomography (PACT) through simulation and experiments and compared it to previous beamformers: DAS, FDMAS, and SLSC. Results: GSC outperforms the imaging metrics of previous state-of-the-art coherence-based beamformers in both simulation and experiments. Conclusions: GSC is an innovative reconstruction algorithm for PACT, which combines the strengths of FDMAS and SLSC expanding PACT’s applications.

Significance: Photoacoustic imaging (PAI) can be used to infer molecular information about myocardial health non-invasively in vivo using optical excitation at ultrasonic spatial resolution. For clinical and preclinical linear array imaging systems, conventional delay-and-sum (DAS) beamforming is typically used. However, DAS cardiac PA images are prone to artifacts such as diffuse quasi-static clutter with temporally varying noise-reducing myocardial signal specificity. Typically, multiple frame averaging schemes are utilized to improve the quality of cardiac PAI, which affects the spatial and temporal resolution and reduces sensitivity to subtle PA signal variation. Furthermore, frame averaging might corrupt myocardial oxygen saturation quantification due to the presence of natural cardiac wall motion. In this paper, a spatiotemporal singular value decomposition (SVD) processing algorithm is proposed to reduce DAS PAI artifacts and subsequent enhancement of myocardial signal specificity. Aim: Demonstrate enhancement of PA signals from myocardial tissue compared to surrounding tissues and blood inside the left-ventricular (LV) chamber using spatiotemporal SVD processing with electrocardiogram (ECG) and respiratory signal (ECG-R) gated in vivo murine cardiac PAI. Approach: In vivo murine cardiac PAI was performed by collecting single wavelength (850 nm) photoacoustic channel data on eight healthy mice. A three-dimensional (3D) volume of complex PAI data over a cardiac cycle was reconstructed using a custom ECG-R gating algorithm and DAS beamforming. Spatiotemporal SVD was applied on a two-dimensional Casorati matrix generated using the 3D volume of PAI data. The singular value spectrum (SVS) was then filtered to remove contributions from diffuse quasi-static clutter and random noise. Finally, SVD processed beamformed images were derived using filtered SVS and inverse SVD computations. Results: Qualitative comparison with DAS and minimum variance (MV) beamforming shows that SVD processed images had better myocardial signal specificity, contrast, and target detectability. DAS, MV, and SVD images were quantitatively evaluated by calculating contrast ratio (CR), generalized contrast-to-noise ratio (gCNR), and signal-to-noise ratio (SNR). Quantitative evaluations were done at three cardiac time points (during systole, at end-systole (ES), and during diastole) identified from co-registered ultrasound M-Mode image. Mean CR, gCNR, and SNR values of SVD images at ES were 245, 115.15, and 258.17 times higher than DAS images with statistical significance evaluated with one-way analysis of variance. Conclusions: Our results suggest that significantly better-quality images can be realized using spatiotemporal SVD processing for in vivo murine cardiac PAI.

Significance: Digital holographic microscopy is widely used to get the quantitative phase information of transparent cells. Aim: However, the sample phase is superimposed with aberrations. To quantify the phase information, aberrations need to be fully compensated. Approach: We propose a technique to obtain aberration-free phase imaging, using the derivative-based principal component analysis (dPCA). Results: With dPCA, almost all aberrations can be extracted and compensated without requirements on background segmentation, making it efficient and convenient. Conclusions: It solves the problem that the conventional principal component analysis (PCA) algorithm cannot compensate the common but intricate higher order cross-term aberrations, such as astigmatism and coma. Moreover, the dPCA strategy proposed here is not only suitable for aberration compensation but also applicable for other cases where there exist cross-terms that cannot be analyzed with the PCA algorithm.

Significance: Frequency domain diffuse optical spectroscopy (FD-DOS) uses intensity modulated light to measure the absorption and reduced scattering coefficients of turbid media such as biological tissue. Some FD-DOS instruments utilize a single modulation frequency, whereas others use hundreds of frequencies. The effect of modulation frequency choice and measurement bandwidth on optical property (OP) extraction accuracy has not yet been fully characterized. Aim: We aim to assess the effect of modulation frequency selection on OP extraction error and develop a high-speed look-up table (LUT) approach for OP estimation. Approach: We first used noise-free simulations of light transport in homogeneous media to determine optimized iterative inversion model parameters and developed a new multi-frequency LUT method to increase the speed of inversion. We then used experimentally derived noise models for two FD-DOS instruments to generate realistic simulated data for a broad range of OPs and modulation frequencies to test OP extraction accuracy. Results: We found that repeated measurements at a single low-frequency (110 MHz) yielded essentially identical OP errors as a broadband frequency sweep (35 evenly spaced frequencies between 50 and 253 MHz) for these noise models. The inclusion of modulation frequencies >300 MHz diminished overall performance for one of the instruments. Additionally, we developed a LUT inversion algorithm capable of increasing inversion speeds by up to 6 × , with 1000 inversions / s and ∼1 % error when a single modulation frequency was used. Conclusion: These results suggest that simpler single-frequency systems are likely sufficient for many applications and pave the way for a new generation of simpler digital FD-DOS systems capable of rapid, large-volume measurements with real-time feedback.

Significance: Digital holographic microscopy (DHM) is a promising technique for the study of semitransparent biological specimen such as red blood cells (RBCs). It is important and meaningful to detect and count biological cells at the single cell level in biomedical images for biomarker discovery and disease diagnostics. However, the biological cell analysis based on phase information of images is inefficient due to the complexity of numerical phase reconstruction algorithm applied to raw hologram images. New cell study methods based on diffraction pattern directly are desirable. Aim: Deep fully convolutional networks (FCNs) were developed on raw hologram images directly for high-throughput label-free cell detection and counting to assist the biological cell analysis in the future. Approach: The raw diffraction patterns of RBCs were recorded by use of DHM. Ground-truth mask images were labeled based on phase images reconstructed from RBC holograms using numerical reconstruction algorithm. A deep FCN, which is UNet, was trained on the diffraction pattern images to achieve the label-free cell detection and counting. Results: The implemented deep FCNs provide a promising way to high-throughput and label-free counting of RBCs with a counting accuracy of 99% at a throughput rate of greater than 288 cells per second and 200 μm×200 μm field of view at the single cell level. Compared to convolutional neural networks, the FCNs can get much better results in terms of accuracy and throughput rate. Conclusions: High-throughput label-free cell detection and counting were successfully achieved from diffraction patterns with deep FCNs. It is a promising approach for biological specimen analysis based on raw hologram directly.

Significance: Large space-bandwidth product is highly desirable in many biomedical imaging. Fourier ptychographic microscopy (FPM) is a computational imaging technique that can significantly increase the space-bandwidth product of a standard microscope. The illuminator of a Fourier ptychographic microscope is not flexible at present, and it is inconvenient to meet different imaging needs. Aim: An illuminator based on a two-axis motorized rotation stage was presented to provide a more flexible illuminating way with the goal of meeting different imaging needs. Approach: The illuminator adopts a concentric illuminating method to provide coherent illumination in any direction on the sample plane. The sampling pattern can be freely designed and changed according to the parameters of the imaging system. A dither removing algorithm was proposed to remove the potential dither influence introduced in the image acquisition process. Results: The illuminator could be conveniently integrated into different imaging systems. The feasibility and flexibility were demonstrated by applying it to imaging systems with numerical aperture of 0.045 and 0.01. The resolution gain is about 4- and 13-fold, respectively. The effectiveness of the dither removing algorithm was validated in both simulation and experiment. Conclusions: A more flexible illuminator for FPM was presented to meet different imaging needs. A dither removing algorithm was proposed to remove dither influence.

Significance: The ability to detect and localize specific molecules through tissue is important for elucidating the molecular basis of disease and treatment. Unfortunately, most current molecular imaging tools in tissue either lack high spatial resolution (e.g., diffuse optical fluorescence tomography or positron emission tomography) or lack molecular sensitivity (e.g., micro-computed tomography, μCT). X-ray luminescence imaging emerged about 10 years ago to address this issue by combining the molecular sensitivity of optical probes with the high spatial resolution of x-ray imaging through tissue. In particular, x-ray luminescence computed tomography (XLCT) has been demonstrated as a powerful technique for the high-resolution imaging of deeply embedded contrast agents in three dimensions (3D) for small-animal imaging. Aim: To facilitate the translation of XLCT for small-animal imaging, we have designed and built a small-animal dedicated focused x-ray luminescence tomography (FXLT) scanner with a μCT scanner, synthesized bright and biocompatible nanophosphors as contrast agents, and have developed a deep-learning-based reconstruction algorithm. Approach: The proposed FXLT imaging system was designed using computer-aided design software and built according to specifications. NaGdF4 nanophosphors doped with europium or terbium were synthesized with a silica shell for increased biocompatibility and functionalized with biotin. A deep-learning-based XLCT image reconstruction was also developed based on the residual neural network as a data synthesis method of projection views from few-view data to enhance the reconstructed image quality. Results: We have built the FXLT scanner for small-animal imaging based on a rotational gantry. With all major imaging components mounted, the motor controlling the gantry can be used to rotate the system with a high accuracy. The synthesized nanophosphors displayed distinct x-ray luminescence emission, which enables multi-color imaging, and has successfully been bound to streptavidin-coated substrates. Lastly, numerical simulations using the proposed deep-learning-based reconstruction algorithm has demonstrated a clear enhancement in the reconstructed image quality. Conclusions: The designed FXLT scanner, synthesized nanophosphors, and deep-learning-based reconstruction algorithm show great potential for the high-resolution molecular imaging of small animals.

For the validation of optical diagnostic technologies, experimental results need to be benchmarked against the gold standard. Currently, the gold standard for tissue characterization is assessment of hematoxylin and eosin (H&E)-stained sections by a pathologist. When processing tissue into H&E sections, the shape of the tissue deforms with respect to the initial shape when it was optically measured. We demonstrate the importance of accounting for these tissue deformations when correlating optical measurement with routinely acquired histopathology. We propose a method to register the tissue in the H&E sections to the optical measurements, which corrects for these tissue deformations. We compare the registered H&E sections to H&E sections that were registered with an algorithm that does not account for tissue deformations by evaluating both the shape and the composition of the tissue and using microcomputer tomography data as an independent measure. The proposed method, which did account for tissue deformations, was more accurate than the method that did not account for tissue deformations. These results emphasize the need for a registration method that accounts for tissue deformations, such as the method presented in this study, which can aid in validating optical techniques for clinical use. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

Since our previous publication of organ dose for the pediatric CT cohort in the UK, there have been questions about the magnitude of uncertainty in our dose estimates. We therefore quantified shared and unshared uncertainties in empirical CT parameters extracted from 1073 CT films (1978–2008) from 36 hospitals in the study and propagated these uncertainties into organ doses using Monte Carlo random sampling and NCICT organ dose calculator. The average of 500 median brain and marrow doses for the full cohort was 35 (95% confidence interval: 30–40) mGy and 6 (5–7) mGy, respectively. We estimated that shared uncertainty contributed ~99% of coefficient of variation of median brain doses in brain scans compared to unshared uncertainty (1% contribution). We found that the previous brain doses were slightly underestimated for 1990 compared to the results in the current study due to the revised CTDI models based on CT films.

Significance: The measurement of human vital signs based on photoplethysmography imaging (PPGI) can be severely affected by the interference of various factors in the measurement process; therefore, a lot of complex signal processing techniques are used to remove the influence of the interference. Aim: We comprehensively analyze several methods for color channel combination in the color spaces currently used in PPGI and determine the combination method that can improve the quality of the pulse signal, which results in a modified plane-orthogonal-to-skin based method (POS). Approach: Based on the analysis of the previous studies, 13 methods for color channel combination in the different color spaces, which can be seen as having potential abilities in measuring vital signs, were compared by employing the average value of signal-to-noise ratio (SNR) and the box-plot in the public databases UBFC-RPPG and PURE. In addition, the pulse signal was extracted through the dual-color space transformation (sRGB → intensity normalized RGB → YCbCr) and fine-tuning on the CbCr plane. Results: Among the 13 methods for color channel combination, the signal extracted by the Cb+Cr combination in the YCbCr color space includes the most pulse information. Furthermore, the average SNR of the modified POS for all the used databases is improved by 69.3% compared to POS. Conclusions: The methods using prior knowledge are not only simple to calculate but can significantly increase the SNR, which will provide a great help in the practical use of vital sign measurements based on PPGI.

Significance: Non-contact, camera-based heart rate variability estimation is desirable in numerous applications, including medical, automotive, and entertainment. Unfortunately, camera-based HRV accuracy and reliability suffer due to two challenges: (a) darker skin tones result in lower SNR and (b) relative motion induces measurement artifacts. Aim: We propose an algorithm HRVCam that provides sufficient robustness to low SNR and motion-induced artifacts commonly present in imaging photoplethysmography (iPPG) signals. Approach: HRVCam computes camera-based HRV from the instantaneous frequency of the iPPG signal. HRVCam uses automatic adaptive bandwidth filtering along with discrete energy separation to estimate the instantaneous frequency. The parameters of HRVCam use the observed characteristics of HRV and iPPG signals. Results: We capture a new dataset containing 16 participants with diverse skin tones. We demonstrate that HRVCam reduces the error in camera-based HRV metrics significantly (more than 50% reduction) for videos with dark skin and face motion. Conclusion: HRVCam can be used on top of iPPG estimation algorithms to provide robust HRV measurements making camera-based HRV practical.

Significance: The potential of fluorescence lifetime imaging microscopy (FLIM) is recently being recognized, especially in biological studies. However, FLIM does not directly measure the lifetimes, rather it records the fluorescence decay traces. The lifetimes and/or abundances have to be estimated from these traces during the phase of data processing. To precisely estimate these parameters is challenging and requires a well-designed computer program. Conventionally employed methods, which are based on curve fitting, are computationally expensive and limited in performance especially for highly noisy FLIM data. The graphical analysis, while free of fit, requires calibration samples for a quantitative analysis. Aim: We propose to extract the lifetimes and abundances directly from the decay traces through machine learning (ML). Approach: The ML-based approach was verified with simulated testing data in which the lifetimes and abundances were known exactly. Thereafter, we compared its performance with the commercial software SPCImage based on datasets measured from biological samples on a time-correlated single photon counting system. We reconstructed the decay traces using the lifetime and abundance values estimated by ML and SPCImage methods and utilized the root-mean-squared-error (RMSE) as marker. Results: The RMSE, which represents the difference between the reconstructed and measured decay traces, was observed to be lower for ML than for SPCImage. In addition, we could demonstrate with a three-component analysis the high potential and flexibility of the ML method to deal with more than two lifetime components. Conclusions: The ML-based approach shows great performance in FLIM data analysis.

Automatic segmentation of bones in computed tomography (CT) images is used for instance in beam hardening correction algorithms where it improves the accuracy of resulting CT numbers. Of special interest are pelvic bones, which—because of their strong attenuation—affect the accuracy of brachytherapy in this region. This work evaluated the performance of the JJ2016 algorithm with the performance of MK2014v2 and JS2018 algorithms; all these algorithms were developed by authors. Visual comparison, and, in the latter case, also Dice similarity coefficients derived from the ground truth were used. It was found that the 3D-based JJ2016 performed better than the 2D-based MK2014v2, mainly because of the more accurate hole filling that benefitted from information in adjacent slices. The neural network-based JS2018 outperformed both traditional algorithms. It was, however, limited to the resolution of 1283 owing to the limited amount of memory in the graphical processing unit (GPU). Funding: VetenskapsradetSwedish Research Council [VR-NT 2016-05033]

Photoacoustic tomography (PAT) is an imaging technique that enables us to visualize vasculature networks at high resolution at depths much greater than optical microscopy.1,2 PAT can also resolve tissue chromophores such as oxyhemoglobin and deoxyhemoglobin, which allows estimation of tissue oxygentation levels.1,3 Accurate estimation of tissue oxygentation is very important in the fields of neuroimaging, cancer diagnostics, and critical care medicine.4,5 PAT involves collection of broadband acoustic waves generated by thermoelastic expansion of tissue upon absorption of light (illuminated at near-infrared wavelengths).6,7 PAT image reconstruction involves recovery of the acoustic source distribution from the detected broadband acoustic waves, which can be performed using analytical or model-based methods.8 Analytical methods are computationally less expensive compared to model-based image reconstruction.8,9 In contrary, model-based schemes are known to generate accurate images compared to analytical schemes, specifically in scenarios where irregular acquisition geometry is involved or limited data is acquired.9,10 Even with the model-based schemes, PAT inversion becomes difficult when the number of ultrasound detector positions is limited, and the situation gets aggravated due to the presence of noise in the data. More recently deep learning-based image postprocessing schemes have been developed for artifact removal and for limited data situations in PAT/microscopy;11–13 however, these schemes were trained on simulated data and generating large experimental data is very difficult. Segmentation approaches were proposed to delineate skin lining in mesoscopic photoacoustic images.14 Active contours-based image segmentation was used to obtain the PAT region of interest (ROI), the obtained ROI was then used to perform fluence correction to make PAT more quantitative.15,16 Different computational techniques have been reported in the literature to obtain vascular network in photoacoustic imaging. Vesselness filter has been proposed to enable accurate recovery of vasculature in PAT;17–19 however, questions have been raised on the accuracy of using the vesselness filter (due to generation of artificial vasculature).20 Probabilistic approach involving different steps such as smoothing and filtering the data, clustering, vessel-segmentation using clusters, and morphological filling was developed for segmenting vessels in photoacoustic images.21 More recently convolution neural network-based approaches were also developed for jointly performing the image reconstruction and segmentation.22 Further, deep learning-based approach was proposed for segmenting animal boundary using multi-modal photoacoustic and ultrasound data.23 Further, deep learning-based method has also been developed for animal brain imaging.24 Notably segmenting vasculature is very important in the context of diagnosis and surgical guidance, specifically for cardiovascular diseases.25,26 In this work, we developed a binary tomography approach wherein we assume the reconstruction distribution contains only two unknowns namely the background and the absorbers. The major contributions of this work include: (a) application of binary tomographic methods for photoacoustic tomographic imaging involving spherical wave propagation as opposed to straight line approximation applied in x-ray computed tomography; (b) showing the utility of binary tomographic methods for improving photoacoustic tomographic imaging of vasculature; and (c) establishing the binary tomographic method superiority in photoacoustic tomographic imaging for providing approximate priors to improve model-based image reconstruction. Binary tomography problem is solved based on a dual optimization approach with an auxiliary variable posed as a constrained optimization problem. We compared the binary tomography algorithm against standard backprojection approach and model-based reconstruction schemes such as Tikhonov regularization and sparsity-based regularization.27 The performance of these different algorithms was validated with in-silico data, physical phantom, and in-vivo brain vasculature imaging data.

Traditional filtered back projection (FBP) reconstruction methods have served the computed tomography (CT) community wellfor over 40 years. With the increased use of CT during the last decades, efforts to minimise patient exposure, while maintainingsufficient or improved image quality, have led to the development of model-based iterative reconstruction (MBIR) algorithms fromseveral vendors. The usefulness of the advanced modeled iterative reconstruction (ADMIRE) (Siemens Healthineers) MBIR inabdominal CT is reviewed and its noise suppression and/or dose reduction possibilities explored. Quantitative and qualitativemethods with phantom and human subjects were used. Assessment of the quality of phantom images will not always correlatepositively with those of patient images, particularly at the higher strength of the ADMIRE algorithm. With few exceptions,ADMIRE Strength 3 typically allows for substantial noise reduction compared to FBP and hence to significant (≈30%) patientdose reductions. The size of the dose reductions depends on the diagnostic task. Funding: ALF-grant from Region Ostergotland [LiO-602731, LIO-697941]; FoU-grant from Region Ostergotland [LIO-724631, LIO-620341]; RFoU-grant from Region Ostergotland; Medical Faculty at Linkoping University

Significance: Placenta is an essential organ for fetal development and successful reproduction. Placental insufficiency can lead to fetal hypoxia and, in extreme cases anoxia, leading to fetal death. Of the 145 million deliveries per year worldwide, ∼15 million neonates are small for gestational age and, therefore, at risk for antepartum and intrapartum hypoxia. Clinical methods to assess placental function largely rely on the assessment of fetal heart rate changes but do not assess placental oxygenation. Near-infrared spectroscopy (NIRS) allows non-invasive, real-time assessment of tissue oxygenation in intact organs, which can be used to assess placental oxygenation. However, tissue optical properties can affect the accuracy of methods to measure tissue oxygenation. Aim: This study was performed to estimate the scattering coefficient of the human placenta. We have computed the scattering coefficients of the human placenta for the range of 659 to 840 nm using two methods of diffuse reflectance spectroscopy (DRS). Approach: Measurements were performed using an in-house DRS device and a well-established frequency-domain diffuse optical spectroscopic system (DOSI). Measurements were performed in eight placentas obtained after cesarean deliveries. Placentas were perfused with normal saline to minimize the effects of absorption due to blood. Three sites per placenta were measured. Absorption and scattering coefficients were then calculated from the measured reflectance using the random walk theory for DRS and frequency-domain algorithm for DOSI. Results: Average reduced scattering coefficient (μs ′ ) was 0.943 ± 0.015 mm − 1 at 760 nm and 0.831 ± 0.009 mm − 1 at 840 nm, and a power function μs ′ = 1.6619 (λ/500 nm) − 1.426 was derived for the human placental scattering coefficient. Conclusion: We report for the first time the scattering coefficient of the human placenta. This information can be used to assess baseline scattering and improve measurements of placental oxygen saturation with NIRS.

Significance: Melanoma is a deadly cancer that physicians struggle to diagnose early because they lack the knowledge to differentiate benign from malignant lesions. Deep machine learning approaches to image analysis offer promise but lack the transparency to be widely adopted as stand-alone diagnostics. Aim: We aimed to create a transparent machine learning technology (i.e., not deep learning) to discriminate melanomas from nevi in dermoscopy images and an interface for sensory cue integration. Approach: Imaging biomarker cues (IBCs) fed ensemble machine learning classifier (Eclass) training while raw images fed deep learning classifier training. We compared the areas under the diagnostic receiver operator curves. Results: Our interpretable machine learning algorithm outperformed the leading deep-learning approach 75% of the time. The user interface displayed only the diagnostic imaging biomarkers as IBCs. Conclusions: From a translational perspective, Eclass is better than convolutional machine learning diagnosis in that physicians can embrace it faster than black box outputs. Imaging biomarkers cues may be used during sensory cue integration in clinical screening. Our method may be applied to other image-based diagnostic analyses, including pathology and radiology.

The purpose was to evaluate the spatial resolution in 111In-octreotide single-photom emission computed tomography (SPECT)/computed tomography (CT) imaging following reconstructions with three different ordered subset expectation maximizations (OSEM) reconstruction algorithms; attenuation corrected (AC) OSEM, AC OSEM with resolution recovery (ACRR OSEM) and Monte Carlo-based OSEM reconstruction (MC OSEM). SPECT/CT imaging of a triple line phantom containing 111In in air and water was performed. The spatial resolution, represented by the full width at half maximum (FWHM) of a line profile, was determined for each line, for X and Y direction and for all reconstructions. The mean FWHM was 12.2 mm (±standard deviation [SD] 3.7 mm) for AC OSEM, 9.3 mm (±SD 2.5 mm) for ACRR OSEM and 8.2 mm (±SD 2.0 mm) for MC OSEM. MC-based SPECT/CT reconstruction clearly improves the spatial resolution in 111In-octreotide imaging and since MC simulations can be performed for all photon energies MC OSEM has the potential to improve SPECT/CT imaging overall.

Objective. To develop an automated system to classify the severity of hypoxic-ischaemic encephalopathy injury (HIE) in neonates from the background electroencephalogram (EEG). Approach. By combining a quadratic time–frequency distribution (TFD) with a convolutional neural network, we develop a system that classifies 4 EEG grades of HIE. The network learns directly from the two-dimensional TFD through 3 independent layers with convolution in the time, frequency, and time–frequency directions. Computationally efficient algorithms make it feasible to transform each 5 min epoch to the time–frequency domain by controlling for oversampling to reduce both computation and computer memory. The system is developed on EEG recordings from 54 neonates. Then the system is validated on a large unseen dataset of 338 h of EEG recordings from 91 neonates obtained across multiple international centres. Main results. The proposed EEG HIE-grading system achieves a leave-one-subject-out testing accuracy of 88.9% and kappa of 0.84 on the development dataset. Accuracy for the large unseen test dataset is 69.5% (95% confidence interval, CI: 65.3%–73.6%) and kappa of 0.54, which is a significant ( P < 0.001 ) improvement over a state-of-the-art feature-based method with an accuracy of 56.8% (95% CI: 51.4%–61.7%) and kappa of 0.39. Performance of the proposed system was unaffected when the number of channels in testing was reduced from 8 to 2—accuracy for the large validation dataset remained at 69.5% (95% CI: 65.5%–74.0%). Significance. The proposed system outperforms the state-of-the-art machine learning algorithms for EEG grade classification on a large multi-centre unseen dataset, indicating the potential to assist clinical decision making for neonates with HIE.

Visualization of 3D high-speed biological events, e.g., signaling of neural circuits, has been the driving force of many emerging 3D imaging techniques, such as random-access microscopy1,2 and light-sheet microscopy (LSM).3,4 The former approach selectively scans the laser beam in the regions of interest to monitor biological events at speeds up to 10-s kHz,1,2 and the latter approach exploits a light sheet to parallelly illuminate and record 2D images at frame rates up to 100-s Hz;3,4 accordingly, clear 3D images can be obtained through the application of custom-designed phase masks3 or synchronization with an axial scanner,4 achieving a volume image rate of ∼30 Hz. Light-field microscopy (LFM)5 is a high-speed 3D imaging technique that captures a volume image via a single exposure from a camera, where the imaging speed is limited by cameras. Briefly, a 3D image in LFM can be restored from a recorded “light-field image” that simultaneously contains the positions of objects, i.e., 2D image, and angles of illumination, i.e., another 2D image, which is termed a “4D” image. Comparing with other fast fluorescent imaging methods, LFM achieves the highest volume imaging rate at the expense of low spatial and angular resolution,5,6 which prevents the broad adoption of LFM in biological studies. To address the issue, both computational and optical methods have been developed. For example, deconvolution algorithms have been applied to LFM7 for imaging neuronal activities at high speed.8–10 Although simple and effective, this method is prone to generating artifacts near the native image plane (NIP) and requires powerful computers for processing dense 3D images. In addition to the deconvolution algorithm, the phase space retrieval method11 has been developed to computationally recover a high-resolution light-field image. Comparing with the deconvolution method, this method shows decreasing fidelity away from the NIP. In terms of optical methods, insertion of phase masks,12 optimization of optical distances,13 adoption of an orthogonal detection arm,14 and selective plane illumination15 have been demonstrated to improve the spatial and angular resolution of LFM. However, the increased complexity in optical design often prevents the easy application of computational methods, e.g., deconvolution algorithms. In this work, we present a new hybrid LFM via selective optical illumination, which reconstructs a high-resolution 4D light-field image from a simultaneously recorded 2D image and low-resolution 4D light-field image. Briefly, multi-layer 2D images within the depth of the focus (DOF) are generated via deconvolution. Next, the multi-layer images are used as constraints for the iterated Fourier transform of the interpolated 4D light-field image to reconstruct a high-resolution 4D light-field image. Accordingly, the hybrid LFM achieves an imaging speed of 100-s volumes per second (vps) with a 3D field of view of 250×250×80 μm3 in the x,y, and z axes, respectively. The measured axial and lateral resolutions, i.e., full width at half maximum (FWHM), are 9.36 and 1.85 μm, respectively.

Significance: Photoacoustic imaging (PAI) has been greatly developed in a broad range of diagnostic applications. The efficiency of light to sound conversion in PAI is limited by the ubiquitous noise arising from the tissue background, leading to a low signal-to-noise ratio (SNR), and thus a poor quality of images. Frame averaging has been widely used to reduce the noise; however, it compromises the temporal resolution of PAI. Aim: We propose an approach for photoacoustic (PA) signal denoising based on a combination of low-pass filtering and sparse coding (LPFSC). Approach: LPFSC method is based on the fact that PA signal can be modeled as the sum of low frequency and sparse components, which allows for the reduction of noise levels using a hybrid alternating direction method of multipliers in an optimization process. Results: LPFSC method was evaluated using in-silico and experimental phantoms. The results show a 26% improvement in the peak SNR of PA signal compared to the averaging method for in-silico data. On average, LPFSC method offers a 63% improvement in the image contrast-to-noise ratio and a 33% improvement in the structural similarity index compared to the averaging method for objects located at three different depths, ranging from 10 to 20 mm, in a porcine tissue phantom. Conclusions: The proposed method is an effective tool for PA signal denoising, whereas it ultimately improves the quality of reconstructed images, especially at higher depths, without limiting the image acquisition speed.

Significance: Practical implementation of acousto-optic imaging (AOI) encounters difficulties that prevent it from rapid adoption in clinical use. In many practical medical applications, the region of interest may be accessed only from one side, and using a water tank for coupling is not feasible. The solution might be to use reflection-mode imaging with an electronically scanned ultrasound (US) focus. Such an approach, however, entails considerable challenges. Aim: The possibilities of detecting and localizing light-absorbing inclusions inside turbid media by combining reflection-mode AOI conducted using a one-dimensional US array with electronic scanning of the US focus are investigated experimentally and signal processing algorithms that could be used for this purpose are introduced. Approach: We determine the speckle contrast decrease due to the acousto-optic effect as a function of the US focal point coordinates. Different signal postprocessing techniques are investigated. Results: A significant decrease in the determined speckle contrast difference values is observed due to the presence of light-absorbing inclusions. However, local minima occur in the plots only under specific conditions. Subtracting individual distributions and determining symmetry deviations allow for localizing the inclusions. Conclusions: Detection and localization of optically distinct regions are possible using the introduced approach. Signal postprocessing is required in a general case.

Significance: Photoacoustic-based visual servoing is a promising technique for surgical tool tip tracking and automated visualization of photoacoustic targets during interventional procedures. However, one outstanding challenge has been the reliability of obtaining segmentations using low-energy light sources that operate within existing laser safety limits. Aim: We developed the first known graphical processing unit (GPU)-based real-time implementation of short-lag spatial coherence (SLSC) beamforming for photoacoustic imaging and applied this real-time algorithm to improve signal segmentation during photoacoustic-based visual servoing with low-energy lasers. Approach: A 1-mm-core-diameter optical fiber was inserted into ex vivo bovine tissue. Photoacoustic-based visual servoing was implemented as the fiber was manually displaced by a translation stage, which provided ground truth measurements of the fiber displacement. GPU-SLSC results were compared with a central processing unit (CPU)-SLSC approach and an amplitude-based delay-and-sum (DAS) beamforming approach. Performance was additionally evaluated with in vivo cardiac data. Results: The GPU-SLSC implementation achieved frame rates up to 41.2 Hz, representing a factor of 348 speedup when compared with offline CPU-SLSC. In addition, GPU-SLSC successfully recovered low-energy signals (i.e., ≤268 μJ) with mean ± standard deviation of signal-to-noise ratios of 11.2±2.4 (compared with 3.5±0.8 with conventional DAS beamforming). When energies were lower than the safety limit for skin (i.e., 394.6 μJ for 900-nm wavelength laser light), the median and interquartile range (IQR) of visual servoing tracking errors obtained with GPU-SLSC were 0.64 and 0.52 mm, respectively (which were lower than the median and IQR obtained with DAS by 1.39 and 8.45 mm, respectively). GPU-SLSC additionally reduced the percentage of failed segmentations when applied to in vivo cardiac data. Conclusions: Results are promising for the use of low-energy, miniaturized lasers to perform GPU-SLSC photoacoustic-based visual servoing in the operating room with laser pulse repetition frequencies as high as 41.2 Hz.

Significance: We introduce an application of machine learning trained on optical phase features of epithelial and mesenchymal cells to grade cancer cells’ morphologies, relevant to evaluation of cancer phenotype in screening assays and clinical biopsies. Aim: Our objective was to determine quantitative epithelial and mesenchymal qualities of breast cancer cells through an unbiased, generalizable, and linear score covering the range of observed morphologies. Approach: Digital holographic microscopy was used to generate phase height maps of noncancerous epithelial (Gie-No3B11) and fibroblast (human gingival) cell lines, as well as MDA-MB-231 and MCF-7 breast cancer cell lines. Several machine learning algorithms were evaluated as binary classifiers of the noncancerous cells that graded the cancer cells by transfer learning. Results: Epithelial and mesenchymal cells were classified with 96% to 100% accuracy. Breast cancer cells had scores in between the noncancer scores, indicating both epithelial and mesenchymal morphological qualities. The MCF-7 cells skewed toward epithelial scores, while MDA-MB-231 cells skewed toward mesenchymal scores. Linear support vector machines (SVMs) produced the most distinct score distributions for each cell line. Conclusions: The proposed epithelial–mesenchymal score, derived from linear SVM learning, is a sensitive and quantitative approach for detecting epithelial and mesenchymal characteristics of unknown cells based on well-characterized cell lines. We establish a framework for rapid and accurate morphological evaluation of single cells and subtle phenotypic shifts in imaged cell populations.

An MR-Linac can provide motion information of tumour and organs-at-risk before, during, and after beam delivery. However, MR imaging cannot provide real-time high-quality volumetric images which capture breath-to-breath variability of respiratory motion. Surrogate-driven motion models relate the motion of the internal anatomy to surrogate signals, thus can estimate the 3D internal motion from these signals. Internal surrogate signals based on patient anatomy can be extracted from 2D cine-MR images, which can be acquired on an MR-Linac during treatment, to build and drive motion models. In this paper we investigate different MRI-derived surrogate signals, including signals generated by applying principal component analysis to the image intensities, or control point displacements derived from deformable registration of the 2D cine-MR images. We assessed the suitability of the signals to build models that can estimate the motion of the internal anatomy, including sliding motion and breath-to-breath variability. We quantitatively evaluated the models by estimating the 2D motion in sagittal and coronal slices of 8 lung cancer patients, and comparing them to motion measurements obtained from image registration. For sagittal slices, using the first and second principal components on the control point displacements as surrogate signals resulted in the highest model accuracy, with a mean error over patients around 0.80 mm which was lower than the in-plane resolution. For coronal slices, all investigated signals except the skin signal produced mean errors over patients around 1 mm. These results demonstrate that surrogate signals derived from 2D cine-MR images, including those generated by applying principal component analysis to the image intensities or control point displacements, can accurately model the motion of the internal anatomy within a single sagittal or coronal slice. This implies the signals should also be suitable for modelling the 3D respiratory motion of the internal anatomy.

Significance: Machine learning is increasingly being applied to the classification of microscopic data. In order to detect some complex and dynamic cellular processes, time-resolved live-cell imaging might be necessary. Incorporating the temporal information into the classification process may allow for a better and more specific classification. Aim: We propose a methodology for cell classification based on the time-lapse quantitative phase images (QPIs) gained by digital holographic microscopy (DHM) with the goal of increasing performance of classification of dynamic cellular processes. Approach: The methodology was demonstrated by studying epithelial-mesenchymal transition (EMT) which entails major and distinct time-dependent morphological changes. The time-lapse QPIs of EMT were obtained over a 48-h period and specific novel features representing the dynamic cell behavior were extracted. The two distinct end-state phenotypes were classified by several supervised machine learning algorithms and the results were compared with the classification performed on single-time-point images. Results: In comparison to the single-time-point approach, our data suggest the incorporation of temporal information into the classification of cell phenotypes during EMT improves performance by nearly 9% in terms of accuracy, and further indicate the potential of DHM to monitor cellular morphological changes. Conclusions: Proposed approach based on the time-lapse images gained by DHM could improve the monitoring of live cell behavior in an automated fashion and could be further developed into a tool for high-throughput automated analysis of unique cell behavior. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

When analyzing multidiameter single-fiber reflectance (MDSFR) spectra, the inhomogeneous distribution of melanin pigments in skin tissue is usually not accounted for. Especially in heavily pigmented skins, this can result in bad fits and biased estimation of tissue optical properties. A model is introduced to account for the inhomogeneous distribution of melanin pigments in skin tissue. In vivo visible MDSFR measurements were performed on heavily pigmented skin of type IV to VI. Skin tissue optical properties and related physiological properties were extracted from the measured spectra using the introduced model. The absorption of melanin pigments described by the introduced model demonstrates a good correlation with the co-localized measurement of the well-known melanin index. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

The mesh-based Monte Carlo (MMC) algorithm is increasingly used as the gold-standard for developing new biophotonics modeling techniques in 3-D complex tissues, including both diffusion-based and various Monte Carlo (MC)-based methods. Compared to multilayered and voxel-based MCs, MMC can utilize tetrahedral meshes to gain improved anatomical accuracy but also results in higher computational and memory demands. Previous attempts of accelerating MMC using graphics processing units (GPUs) have yielded limited performance improvement and are not publicly available. We report a highly efficient MMC—MMCL—using the OpenCL heterogeneous computing framework and demonstrate a speedup ratio up to 420× compared to state-of-the-art single-threaded CPU simulations. The MMCL simulator supports almost all advanced features found in our widely disseminated MMC software, such as support for a dozen of complex source forms, wide-field detectors, boundary reflection, photon replay, and storing a rich set of detected photon information. Furthermore, this tool supports a wide range of GPUs/CPUs across vendors and is freely available with full source codes and benchmark suites at http://mcx.space/#mmc.