Journal of Biomedical Optics最新文献

Significance: Imaging flow cytometry allows highly informative multi-point cell analysis for biological assays and medical diagnosis. Rapid processing of the imaged cells during flow allows real-time classification and sorting of the cells. Off-axis holography enables imaging flow cytometry without chemical cell staining but requires digital processing to the optical path delay profile for each frame before the cells can be classified, which slows down the overall processing throughput. We present a method for real-time cell classification via label-free quantitative imaging flow cytometry using digital holography, offering a comprehensive representation of cellular structures, without the need for digital processing before automatic cell classification.

Aim: We aim to develop an automatic cell classification scheme based directly on the off-axis holographic projections of the cells during flow and test it for stain-free imaging flow cytometry of white blood cells.

Approach: After building a dedicated off-axis holographic microscopy system for acquiring white blood cells during flow, we apply deep-learning classification directly in the off-axis hologram space, rather than in the quantitative phase profile space. This way, we simplify computational processes and allow a significant increase in the cell classification throughput. In addition, by utilizing multiple-viewpoint holographic projections of the cells rotated during flow, instead of using a single projection, we obtain better classification results due to the additional cellular information gained.

Results: Our technique demonstrates increasing accuracy with additional viewpoint holographic projections from the optical system, achieving a 7.69% improvement when processing ten interferometric projections compared with a single interferometric projection (regular off-axis hologram). Our technique also outperforms using multiple optical path delay profile projections, requiring off-axis holographic digital preprocessing, by 17.95%, because the holographic projections are analyzed directly without preprocessing and includes the amplitude information as well.

Conclusions: Our cell classification approach has great potential for high-throughput, high-content, label-free imaging flow cytometry for classification of large-scale cellular datasets and real-time cell classification during flow in clinical settings.

Significance: Existing photoacoustic phantoms are unable to mimic complex microvascular structures with varying sizes and distributions. A suitable material with structures that mimic intricate microvascular networks is needed.

Aim: Our aim is to introduce loofah as a natural phantom material with complex fiber networks ranging from 50 to $300\mu m$ , enabling the fabrication of phantoms with controlled optical properties comparable to those of human microvasculature.

Approach: By introducing a controllable chromophore into the loofah material, we controlled its absorption properties. The loofah's vasculature-mimetic capabilities and stability in photoacoustic signal generation were evaluated using co-registered ultrasound, acoustic-resolution photoacoustic microscopy (ARPAM), and optical-resolution photoacoustic microscopy (ORPAM).

Results: ORPAM results confirmed the loofah's ability to control chromophore distribution, leading to consistent and regulated photoacoustic signals. ARPAM results demonstrated that the loofah phantom effectively replicates vascular structures, exhibiting superior performance in mimicking microvascular networks compared with commonly used tissue-mimetic phantoms. The dominant diameter range of the phantom's microvasculature was between 100 and $250\mu m$ , aligning well with the targeted range and facilitating meaningful comparisons with human vascular structures.

Conclusions: The loofah material provides a low-cost and effective method for creating submillimeter microvascular phantoms for photoacoustic imaging. Its exceptional morphology and customizability allow it to be shaped into various vascular network configurations, enhancing the fidelity of phantom imaging and assisting in system calibration and validation. In addition, data obtained from this realistic microvascular phantom can offer greater opportunities for training machine learning models.

Significance: In the last years, time-resolved near-infrared spectroscopy (TD-NIRS) has gained increasing interest as a tool for studying tissue spectroscopy with commercial devices. Although it provides much more information than its continuous wave counterpart, accurate models interpreting the measured raw data in real time are still lacking.

Aim: We introduce an analytical model that can be integrated and used in TD-NIRS data processing software and toolkits in real time. This is based on the so-called sensitivity factors (SFs) of the distributions of time of flight (DTOFs) of photons measured in optically turbid and semi-infinite multilayered media, such as the human head.

Approach: We derived analytical expressions for the SFs that link changes in the absorption coefficient of each layer to changes in the statistical moments of DTOFs acquired in a reflectance configuration. This was later validated with results from Monte Carlo (MC) simulations, which stand as the gold standard in terms of photon migration in biological tissue. Next, we designed a couple of simulated experiments depicting how the analytical SFs can be used to retrieve absorption changes in the particular case of a five-layered medium.

Results: Comparison between theory and simulations in 2-, 5-, and 10-layered media showed very good agreement (in most cases with weighted mean absolute percentage errors below 10%). Moreover, our derivations could be run in a few milliseconds (except for the extreme case of the variance SF in the 10-layered medium), which means a speedup of up to 10,000× with respect to MC simulations, with a much better spatial resolution and without their typically associated stochastic noise.

Conclusions: In summary, our method achieves performances similar to those given by MC simulations, but orders of magnitude faster, which makes it very suitable for its implementation in real-time applications.

Significance: Radiofrequency ablation to treat atrial fibrillation (AF) involves isolating the pulmonary vein from the left atria to prevent AF from occurring. However, creating ablation lesions within the pulmonary veins can cause adverse complications.

Aim: We propose automated classification algorithms to classify optical coherence tomography (OCT) volumes of human venoatrial junctions.

Approach: A dataset of comprehensive OCT volumes of 26 venoatrial junctions was used for this study. Texture, statistical, and optical features were extracted from OCT patches. Patches were classified as a left atrium or pulmonary vein using random forest (RF), logistic regression (LR), and convolutional neural networks (CNNs). The features were inputs into the RF and LR classifiers. The inputs to the CNNs included: (1) patches and (2) an ensemble of patches and patch-derived features.

Results: Utilizing a sevenfold cross-validation, the patch-only CNN balances sensitivity and specificity best, with an area under the receiver operating characteristic (AUROC) curve of $0.84\pm 0.109$ across the test sets. RF is more sensitive than LR, with an AUROC curve of $0.78\pm 0.102$ .

Conclusions: Cardiac tissues can be identified in benchtop OCT images by automated analysis. Extending this analysis to data obtained in vivo is required to tune automated analysis further. Performing this classification in vivo could aid doctors in identifying substrates of interest and treating AF.

Significance: Accurate values of skin optical properties are essential for developing reliable computational models and optimizing optical imaging systems. However, published values show a large variability due to a variety of factors, including differences in sample collection, preparation, experimental methodology, and analysis.

Aim: We aim to explore the influence of storage conditions on the optical properties of the excised skin from 400 to 1100 nm.

Approach: We utilize a double integrating sphere system and inverse adding-doubling approach to determine absorption, ${\mu }_a$ , and reduced scattering, ${\mu }_s^{\text{'}}$ , coefficients of the porcine dermis and subcutaneous fat before and after refrigeration, freezing, or flash freezing.

Results: Our findings indicate a small average change of $-0.005$ , $-0.003$ , and $0.002{\mathrm{mm}}^{-1}$ in ${\mu }_a$ for the dermis and 0.001, $-0.003$ , and $-0.008{\mathrm{mm}}^{-1}$ for the subcutaneous tissue after refrigeration, freezing, and flash freezing, respectively, with the most notable differences observed in the hemoglobin absorption region. The value of ${\mu }_s^{\text{'}}$ shows a negligible average change of $-0.05$ , $-0.001$ , and $-0.02\hspace{0.17em}{\mathrm{mm}}^{-1}$ for the dermis, and 0.06, $-0.1$ , and $0.03{\mathrm{mm}}^{-1}$ change for the subcutaneous tissue for refrigerated, frozen, and flash-frozen samples, respectively.

Conclusions: The results provide additional context for the variability of published values of optical parameters and enable informed selection of sample storage conditions for future measurements. In addition, the results discussed here can be used to improve study planning, particularly with regard to maximizing the use of finite samples that have been collected.

Significance: Standardization of fluorescence molecular imaging (FMI) is critical for ensuring quality control in guiding surgical procedures. To accurately evaluate system performance, two metrics, the signal-to-noise ratio (SNR) and contrast, are widely employed. However, there is currently no consensus on how these metrics can be computed.

Aim: We aim to examine the impact of SNR and contrast definitions on the performance assessment of FMI systems.

Approach: We quantified the SNR and contrast of six near-infrared FMI systems by imaging a multi-parametric phantom. Based on approaches commonly used in the literature, we quantified seven SNRs and four contrast values considering different background regions and/or formulas. Then, we calculated benchmarking (BM) scores and respective rank values for each system.

Results: We show that the performance assessment of an FMI system changes depending on the background locations and the applied quantification method. For a single system, the different metrics can vary up to $\sim 35\mathrm{dB}$ (SNR), $\sim 8.65a.u$ . (contrast), and $\sim 0.67a.u$ . (BM score).

Conclusions: The definition of precise guidelines for FMI performance assessment is imperative to ensure successful clinical translation of the technology. Such guidelines can also enable quality control for the already clinically approved indocyanine green-based fluorescence image-guided surgery.

JBO Editor in Chief Muyinatu Bell highlights gratitude and forthcoming initiatives for the new year.

Significance: The precise identification and preservation of functional brain areas during neurosurgery are crucial for optimizing surgical outcomes and minimizing postoperative deficits. Intraoperative imaging plays a vital role in this context, offering insights that guide surgeons in protecting critical cortical regions.

Aim: We aim to evaluate and compare the efficacy of intraoperative thermal imaging (ITI) and intraoperative optical imaging (IOI) in detecting the primary somatosensory cortex, providing a detailed assessment of their potential integration into surgical practice.

Approach: Data from nine patients undergoing tumor resection in the region of the somatosensory cortex were analyzed. Both IOI and ITI were employed simultaneously, with a specific focus on the areas identified as the primary somatosensory cortex (S1 region). The methodologies included a combination of imaging techniques during distinct phases of rest and stimulation, confirmed by electrophysiological monitoring of somatosensory evoked potentials to verify the functional areas identified by both imaging methods. The data were analyzed using a Fourier-based analytical framework to distinguish physiological signals from background noise.

Results: Both ITI and IOI successfully generated reliable activity maps following median nerve stimulation. IOI showed greater consistency across various clinical scenarios, including those involving cortical tumors. Quantitative analysis revealed that IOI could more effectively differentiate genuine neuronal activity from artifacts compared with ITI, which was occasionally prone to false positives in the presence of cortical abnormalities.

Conclusions: ITI and IOI produce comparable functional maps with moderate agreement in Cohen's kappa values. Their distinct physiological mechanisms suggest complementary use in specific clinical scenarios, such as cortical tumors or impaired neurovascular coupling. IOI excels in spatial resolution and mapping reliability, whereas ITI provides additional insights into metabolic changes and tissue properties, especially in pathological areas. Combined, these modalities could enhance the understanding and analysis of functional and pathological processes in complex neurosurgical cases.

Significance: Intraoperative molecular imaging (IMI) enables the detection and visualization of cancer tissue using targeted radioactive or fluorescent tracers. While IMI research has rapidly expanded, including the recent Food and Drug Administration approval of a targeted fluorophore, the limits of detection have not been well-defined.

Aim: The ability of widely available handheld intraoperative tools (Neoprobe and SPY-PHI) to measure gamma decay and fluorescence intensity from IMI tracers was assessed while varying characteristics of both the signal source and the intervening tissue or gelatin phantoms.

Approach: Gamma decay signal and fluorescence from tracer-bearing tumors (TBTs) and modifiable tumor-like inclusions (TLIs) were measured through increasing thicknesses of porcine tissue and gelatin in custom 3D-printed molds. TBTs buried beneath porcine tissue were used to simulate IMI-guided tumor resection.

Results: Gamma decay from TBTs and TLIs was detected through significantly thicker tissue and gelatin than fluorescence, with at least 5% of the maximum signal observed through up to 5 and 0.5 cm, respectively, depending on the overlying tissue type or gelatin.

Conclusions: We developed novel systems that can be fine-tuned to simulate variable tumor characteristics and tissue environments. These were used to evaluate the detection of fluorescent and gamma signals from IMI tracers and simulate IMI surgery.