Diffuse Optical Tomography Research Articles

Deep learning-based temporal deconvolution for photon time-of-flight distribution retrieval

The acquisition of the time of flight (ToF) of photons has found numerous applications in the biomedical field. Over the last decades, a few strategies have been proposed to deconvolve the temporal instrument response function (IRF) that distorts the experimental time-resolved data. However, these methods require burdensome computational strategies and regularization terms to mitigate noise contributions. Herein, we propose a deep learning model specifically to perform the deconvolution task in fluorescence lifetime imaging (FLI). The model is trained and validated with representative simulated FLI data with the goal of retrieving the true photon ToF distribution. Its performance and robustness are validated with well-controlled in vitro experiments using three time-resolved imaging modalities with markedly different temporal IRFs. The model aptitude is further established with in vivo preclinical investigation. Overall, these in vitro and in vivo validations demonstrate the flexibility and accuracy of deep learning model-based deconvolution in time-resolved FLI and diffuse optical imaging.

Fundamental effects of array density and modulation frequency on image quality of diffuse optical tomography.

Diffuse optical tomography (DOT) provides three-dimensional image reconstruction of chromophore perturbations within a turbid volume. Two leading strategies to optimize DOT image quality include, (i) arrays of regular, interlacing, high-density (HD) grids of sources and detectors with closest spacing less than 15mm, or (ii) source modulated light of order ∼100MHz. However, the general principles for how these crucial design parameters of array density and modulation frequency may interact to provide an optimal system design have yet to be elucidated. Herein, we systematically evaluated how these design parameters effect image quality via multiple key metrics. Specifically, we simulated 32 system designs with realistic measurement noise and quantified localization error, spatial resolution, signal-to-noise, and localization depth of field for each of ∼85000 point spread functions in each model. We found that array density had a far stronger effect on image quality metrics than modulation frequency. Additionally, model fits for image quality metrics revealed that potential improvements diminish with regular arrays denser than 9mm closest spacing. Further, for a given array density, 300MHz source modulation provided the deepest reliable imaging compared to other frequencies. Our results indicate that both array density and modulation frequency affect the spatial sampling of tissue, which asymptotically saturates due to photon diffusivity within a turbid volume. In summary, our results provide comprehensive perspectives for optimizing future DOT system designs in applications from wearable functional brain imaging to breast tumor detection.

Optimal Image Reconstruction and Anomaly Detection in Diffuse Optical Tomography with Hybrid CNN-LSTM

Optimal Image Reconstruction and Anomaly Detection in Diffuse Optical Tomography with Hybrid CNN-LSTM

Whole-head high-density diffuse optical tomography to map infant audio-visual responses to social and non-social stimuli

Abstract Infancy is a time of rapid brain development. High-density diffuse optical tomography (HD-DOT) is an optical neuroimaging method that maps changes in cortical haemoglobin concentration, a marker of functional brain activation. Recent years have seen a huge advance in wearable hardware for HD-DOT, however previous headgear has only been capable of sampling specific areas of the cortex. In this work, we aimed to develop headgear capable of sampling across the whole infant scalp surface and to conduct a proof-of-concept demonstration of whole-head HD-DOT in infants aged 5 to 7 months. We developed a whole-head infant implementation of the high-density LUMO design developed by Gowerlabs Ltd. (UK). HD-DOT data were collected from a cohort of infants (N = 16) during the presentation of a screen-based paradigm assessing social processing. Using whole-head HD-DOT, we mapped activity across the entirety of the optically-accessible cortex which far exceeds coverage achieved by previous infant optical neuroimaging methods. We found activity in temporal regions which corroborates previous research. Further, we mapped activity in regions outside those typically sampled in infant research using social processing paradigms, finding activation in regions across the occipital, parietal, and frontal cortices as well as an apparent inverted response in sensorimotor regions. Following this proof-of-concept, we envisage that whole-head HD-DOT will be applied to map the interaction between different regions of the brain, opening new avenues to map activity in the awake infant brain to better understand the trajectory of typical and atypical neurodevelopment.

Scientists' perspectives on ethical issues in research with emerging portable neuroimaging technology: The need for guidance on ethical, legal, and societal implications (ELSI).

Deployment of new, more portable, and less costly neuroimaging technologies such as portable magnetoencephalography, electroencephalography, positron emission tomography, functional near-infrared spectroscopy, high-density diffuse optical tomography, and magnetic resonance imaging is advancing rapidly. Given this trajectory toward increasing use of neuroimaging outside the hospital, we sought to identify ethical, legal, and societal implications (ELSI) of these new technologies by understanding the perspectives of those scientists and engineers developing and implementing portable neuroimaging technologies in the United States, Europe, and Asia. Based on a literature review, we identified and contacted 19 potential interviewees and then conducted 11 semi-structured interviews in English by Zoom. Analysis of the interviews revealed key themes and ELSI issues. Developers reported that without proper ELSI guidance, portable and accessible neuroimaging technology could be misused, fail to comply with applicable regulation and policy, and ultimately fall short in its mission to provide neuroimaging for the world. Our interviews suggested that ELSI guidance should address differences between imaging modalities because they vary in capability, limitations, and likelihood of generating incidental findings.

Medium-adaptive compressive diffuse optical tomography.

The low spatial resolution of diffuse optical tomography (DOT) has motivated the development of high-density DOT systems utilizing spatially-encoded illumination and detection strategies. Data compression methods, through the application of Fourier or Hadamard patterns, have been commonly explored for both illumination and detection but were largely limited to pre-determined patterns regardless of imaging targets. Here, we show that target-optimized detection patterns can yield significantly improved DOT reconstructions in both in silico and experimental tests. Applying reciprocity, we can further iteratively optimize both illumination and detection patterns and show that these simultaneously optimized source/detection patterns outperform predetermined patterns in simulation settings. In addition, we show media-adaptive measurement data compression methods enable wide-field DOT systems to recover highly complex inclusions inside optically-thick media with reduced background artifacts. Furthermore, using truncated optimized patterns shows an improvement of 2-4× in increased speed of data acquisition and reconstruction without significantly losing image quality. The proposed method can be readily extended for additional data dimensions such as spectrum and time.

Neurovascular mechanisms of cognitive aging: Sex-related differences in the average progression of arteriosclerosis, white matter atrophy, and cognitive decline

Arterial stiffness (arteriosclerosis) has been linked to heightened risks for cognitive decline, and ultimately for Alzheimer's disease and other forms of dementia. Importantly, neurovascular outcomes generally vary according to one's biological sex. Here, capitalizing on a large sample of participants with neuroimaging and behavioral data (N = 203, age range = 18–87 years), we aimed to provide support for a hierarchical model of neurocognitive aging, which links age-related declines in cerebrovascular health to the rate of cognitive decline via a series of intervening variables, such as white matter integrity. By applying a novel piecewise regression approach to our cross-sectional sample to support Granger-like temporal inferences, we show that, on average, a precipitous decline in cerebral arterial elasticity (measured with diffuse optical imaging of the cerebral arterial pulse; pulse-DOT) precedes an acceleration in the development of white matter lesions by nearly a decade, with women protected from these deleterious effects until approximately age 50, the average onset of menopause. By employing multiple-mediator path analyses while controlling for sex, we show that age may impair cognition via the sequential indirect effects of arteriosclerosis and white matter atrophy on fluid, but not crystallized, abilities. Importantly, we replicate these results using pulse pressure, an independent index of arterial health, thereby providing converging evidence for the central role of arteriosclerosis as an accelerating factor in normal and pathological aging and identifying robust sex-related differences in the progression of cerebral arteriosclerosis and white matter degradation.

Prediction of disease-free survival using strain elastography and diffuse optical tomography in patients with T1 breast cancer: a 10-year follow-up study

BackgroundEarly-stage breast cancer (BC) presents a certain risk of recurrence, leading to variable prognoses and complicating individualized management. Yet, preoperative noninvasive tools for accurate prediction of disease-free survival (DFS) are lacking. This study assessed the potential of strain elastography (SE) and diffuse optical tomography (DOT) for non-invasive preoperative prediction of recurrence in T1 BC and developed a prediction model for estimating the probability of DFS.MethodsA total of 565 eligible patients with T1 invasive BC were enrolled prospectively and followed to investigate the recurrence. The associations between imaging features and DFS were evaluated and a best-prediction model for DFS was developed and validated.ResultsDuring the median follow-up period of 10.8 years, 77 patients (13.6%) developed recurrences. The fully adjusted Cox proportional hazards model showed a significant trend between an increasing strain ratio (SR) (P < 0.001 for trend) and the total hemoglobin concentration (TTHC) (P = 0.001 for trend) and DFS. In the subgroup analysis, an intensified association between SR and DFS was observed among women who were progesterone receptor (PR)-positive, lower Ki-67 expression, HER2 negative, and without adjuvant chemotherapy and without Herceptin treatment (all P < 0.05 for interaction). Significant interactions between TTHC status and the lymphovascular invasion, estrogen receptor (ER) status, PR status, HER2 status, and Herceptin treatment were found for DFS(P < 0.05).The imaging–clinical combined model (TTHC + SR + clinicopathological variables) proved to be the best prediction model (AUC = 0.829, 95% CI = 0.786–0.872) and was identified as a potential risk stratification tool to discriminate the risk probability of recurrence.ConclusionThe combined imaging-clinical model we developed outperformed traditional clinical prognostic indicators, providing a non-invasive, reliable tool for preoperative DFS risk stratification and personalized therapeutic strategies in T1 BC. These findings underscore the importance of integrating advanced imaging techniques into clinical practice and offer support for future research to validate and expand on these predictive methodologies.

NinjaCap: a fully customizable and 3D printable headgear for functional near-infrared spectroscopy and electroencephalography brain imaging.

Accurate sensor placement is vital for non-invasive brain imaging, particularly for functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT), which lack standardized layouts such as those in electroencephalography (EEG). Custom, manually prepared probe layouts on textile caps are often imprecise and labor intensive. We introduce a method for creating personalized, 3D-printed headgear, enabling the accurate translation of 3D brain coordinates to 2D printable panels for custom fNIRS and EEG sensor layouts while reducing costs and manual labor. Our approach uses atlas-based or subject-specific head models and a spring-relaxation algorithm for flattening 3D coordinates onto 2D panels, using 10-5 EEG coordinates for reference. This process ensures geometrical fidelity, crucial for accurate probe placement. Probe geometries and holder types are customizable and printed directly on the cap, making the approach agnostic to instrument manufacturers and probe types. Our ninjaCap method offers probe placement accuracy. Over the last five years, we have developed and validated this approach with over 50 cap models and 500 participants. A cloud-based ninjaCap generation pipeline along with detailed instructions is now available at openfnirs.org. The ninjaCap marks a significant advancement in creating individualized neuroimaging caps, reducing costs and labor while improving probe placement accuracy, thereby reducing variability in research.

Mapping neural correlates of biological motion perception in autistic children using high-density diffuse optical tomography

BackgroundAutism spectrum disorder (ASD), a neurodevelopmental disorder defined by social communication deficits plus repetitive behaviors and restricted interests, currently affects 1/36 children in the general population. Recent advances in functional brain imaging show promise to provide useful biomarkers of ASD diagnostic likelihood, behavioral trait severity, and even response to therapeutic intervention. However, current gold-standard neuroimaging methods (e.g., functional magnetic resonance imaging, fMRI) are limited in naturalistic studies of brain function underlying ASD-associated behaviors due to the constrained imaging environment. Compared to fMRI, high-density diffuse optical tomography (HD-DOT), a non-invasive and minimally constraining optical neuroimaging modality, can overcome these limitations. Herein, we aimed to establish HD-DOT to evaluate brain function in autistic and non-autistic school-age children as they performed a biological motion perception task previously shown to yield results related to both ASD diagnosis and behavioral traits.MethodsWe used HD-DOT to image brain function in 46 ASD school-age participants and 49 non-autistic individuals (NAI) as they viewed dynamic point-light displays of coherent biological and scrambled motion. We assessed group-level cortical brain function with statistical parametric mapping. Additionally, we tested for brain-behavior associations with dimensional metrics of autism traits, as measured with the Social Responsiveness Scale-2, with hierarchical regression models.ResultsWe found that NAI participants presented stronger brain activity contrast (coherent > scrambled) than ASD children in cortical regions related to visual, motor, and social processing. Additionally, regression models revealed multiple cortical regions in autistic participants where brain function is significantly associated with dimensional measures of ASD traits.LimitationsOptical imaging methods are limited in depth sensitivity and so cannot measure brain activity within deep subcortical regions. However, the field of view of this HD-DOT system includes multiple brain regions previously implicated in both task-based and task-free studies on autism.ConclusionsThis study demonstrates that HD-DOT is sensitive to brain function that both differentiates between NAI and ASD groups and correlates with dimensional measures of ASD traits. These findings establish HD-DOT as an effective tool for investigating brain function in autistic and non-autistic children. Moreover, this study established neural correlates related to biological motion perception and its association with dimensional measures of ASD traits.

Effects of Aging, Fitness, and Cerebrovascular Status on White Matter Microstructural Health.

White matter (WM) microstructural health declines with increasing age, with evidence suggesting that improved cardiorespiratory fitness (CRF) may mitigate this decline. Specifically, higher fit older adults tend to show preserved WM microstructural integrity compared to their lower fit counterparts. However, the extent to which fitness and aging independently impact WM integrity across the adult lifespan is still an open question, as is the extent to which cerebrovascular health mediates these relationships. In a large sample (N = 125, aged 25-72), we assessed the impact of age and fitness on fractional anisotropy (FA, derived using diffusion weighted imaging, DWI) and probed the mediating role of cerebrovascular health (derived using diffuse optical tomography of the cerebral arterial pulse, pulse-DOT) in these relationships. After orthogonalizing age and fitness and computing a PCA on whole brain WM regions, we found several WM regions impacted by age that were independent from the regions impacted by fitness (hindbrain areas, including brainstem and cerebellar tracts), whereas other areas showed interactive effects of age and fitness (midline areas, including fornix and corpus callosum). Critically, cerebrovascular health mediated both relationships suggesting that vascular health plays a linking role between age, fitness, and brain health. Secondarily, we assessed potential sex differences in these relationships and found that, although females and males generally showed the same age-related FA declines, males exhibited somewhat steeper declines than females. Together, these results suggest that age and fitness impact specific WM regions and highlight the mediating role of cerebrovascular health in maintaining WM health across adulthood.

Fluorescence diffuse optical monitoring of bioreactors: a hybrid deep learning and model-based approach for tomography.

Biosynthesis in bioreactors plays a vital role in many applications, but tools for accurate in situ monitoring of the cells are still lacking. By engineering the cells such that their conditions are reported through fluorescence, it is possible to fill in the gap using fluorescence diffuse optical tomography (fDOT). However, the spatial accuracy of the reconstruction can still be limited, due to e.g. undersampling and inaccurate estimation of the optical properties. Utilizing controlled phantom studies, we use a two-step hybrid approach, where a preliminary fDOT result is first obtained using the classic model-based optimization, and then enhanced using a neural network. We show in this paper using both simulated and phantom experiments that the proposed method can lead to a 8-fold improvement (Intersection over Union) of fluorescence inclusion reconstruction in noisy conditions, at the same speed of conventional neural network-based methods. This is an important step towards our ultimate goal of fDOT monitoring of bioreactors.

Improving diffuse optical tomography imaging quality using APU-Net: an attention-based physical U-Net model.

Traditional diffuse optical tomography (DOT) reconstructions are hampered by image artifacts arising from factors such as DOT sources being closer to shallow lesions, poor optode-tissue coupling, tissue heterogeneity, and large high-contrast lesions lacking information in deeper regions (known as shadowing effect). Addressing these challenges is crucial for improving the quality of DOT images and obtaining robust lesion diagnosis. We address the limitations of current DOT imaging reconstruction by introducing an attention-based U-Net (APU-Net) model to enhance the image quality of DOT reconstruction, ultimately improving lesion diagnostic accuracy. We designed an APU-Net model incorporating a contextual transformer attention module to enhance DOT reconstruction. The model was trained on simulation and phantom data, focusing on challenges such as artifact-induced distortions and lesion-shadowing effects. The model was then evaluated by the clinical data. Transitioning from simulation and phantom data to clinical patients' data, our APU-Net model effectively reduced artifacts with an average artifact contrast decrease of 26.83% and improved image quality. In addition, statistical analyses revealed significant contrast improvements in depth profile with an average contrast increase of 20.28% and 45.31% for the second and third target layers, respectively. These results highlighted the efficacy of our approach in breast cancer diagnosis. The APU-Net model improves the image quality of DOT reconstruction by reducing DOT image artifacts and improving the target depth profile.

Optimizing spatial accuracy in electroencephalography reconstruction through diffuse optical tomography priors in the auditory cortex.

Diffuse optical tomography (DOT) enhances the localization accuracy of neural activity measured with electroencephalography (EEG) while preserving EEG's high temporal resolution. However, the spatial resolution of reconstructed activity diminishes for deeper neural sources. In this study, we analyzed DOT-enhanced EEG localization of neural sources modeled at depths ranging from 11-25 mm in simulations. Our findings reveal systematic biases in reconstructed depth related to DOT channel length. To address this, we developed a data-informed method for selecting DOT channels to improve the spatial accuracy of DOT-enhanced EEG reconstruction. Using our method, the average absolute reconstruction depth errors of DOT reconstruction across all depths are 0.9 ± 0.6 mm, 1.2 ± 0.9 mm, and 1.2 ± 1.1 mm under noiseless, low-level noise, and high-level noise conditions, respectively. In comparison, using fixed channel lengths resulted in errors of 2.6 ± 1.5 mm, 5.0 ± 2.6 mm, and 7.3 ± 4.5 mm under the same conditions. Consequently, our method improved the depth accuracy of DOT reconstructions and facilitated the use of more accurate spatial priors for EEG reconstructions, enhancing the overall precision of the technique.

Correction: Randomized recursive techniques for image reconstruction in diffuse optical tomography

Correction: Randomized recursive techniques for image reconstruction in diffuse optical tomography

Ultrasound and diffuse optical tomography-transformer model for assessing pathological complete response to neoadjuvant chemotherapy in breast cancer.

We evaluate the efficiency of integrating ultrasound (US) and diffuse optical tomography (DOT) images for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients. The ultrasound-diffuse optical tomography (USDOT)-Transformer model represents a significant step toward accurate prediction of pCR, which is critical for personalized treatment planning. We aim to develop and assess the performance of the USDOT-Transformer model, which combines US and DOT images with tumor receptor biomarkers to predict the pCR of breast cancer patients under NAC. We developed the USDOT-Transformer model using a dual-input transformer to process co-registered US and DOT images along with tumor receptor biomarkers. Our dataset comprised imaging data from 60 patients at multiple time points during their chemotherapy treatment. We used fivefold cross-validation to assess the model's performance, comparing its results against a single modality of US or DOT. The USDOT-Transformer model demonstrated excellent predictive performance, with a mean area under the receiving characteristic curve of 0.96 (95%CI: 0.93 to 0.99) across the fivefold cross-validation. The integration of US and DOT images significantly enhanced the model's ability to predict pCR, outperforming models that relied on a single imaging modality (0.87 for US and 0.82 for DOT). This performance indicates the potential of advanced deep learning techniques and multimodal imaging data for improving the accuracy (ACC) of pCR prediction. The USDOT-Transformer model offers a promising non-invasive approach for predicting pCR to NAC in breast cancer patients. By leveraging the structural and functional information from US and DOT images, the model offers a faster and more reliable tool for personalized treatment planning. Future work will focus on expanding the dataset and refining the model to further improve its accuracy and generalizability.

A Semi-smooth Newton Method for Diffuse Optical Tomography with L1-Norm and Total Variation Regularization

This study introduces a novel multi-measurement Diffuse Optical Tomography reconstruction method that employs both L1-norm and Total Variation regularization, solved by the semi-smooth Newton method. By integrating L1-norm regularization to address sparsity and TV regularization to preserve edges and structural details, our approach effectively combats the ill-posed nature of DOT reconstructions. The results demonstrate that our approach reduces the required iterations while simultaneously maintaining or enhancing reconstruction accuracy and robustness to noise.

Label-free assessment of the transformation zone using multispectral diffuse optical imaging toward early detection of cervical cancer.

The assessment of the transformation zone is a critical step toward diagnosis of cervical cancer. This work involves the development of a portable, label-free transvaginal multispectral diffuse optical imaging (MDOI) imaging probe to estimate the transformation zone. The images were acquired from N = 5 (N = 1 normal, N = 2 premalignant, and N = 2 malignant) patients. Key parameters such as spectral contrast ratio (ρ) at 545 and 450 nm were higher in premalignant (0.29, 0.25 for 450 nm and 0.30, 0.17 for 545 nm) as compared to the normal patients (0.13 and 0.14 for 450 and 545 nm, respectively). The threshold for the spectral intensity ratio R610/R450 and R610/R545 can also be used as a marker to correlate with the new and original squamous columnar junction (SCJ), respectively. The pilot study highlights the use of new markers such as spectral contrast ratio (ρ) and spectral intensity ratio (R610/R450 and R610/R545) images.

Diffuse optical tomography of the brain: effects of inaccurate baseline optical parameters and refinements using learned post-processing.

Diffuse optical tomography (DOT) uses near-infrared light to image spatially varying optical parameters in biological tissues. In functional brain imaging, DOT uses a perturbation model to estimate the changes in optical parameters, corresponding to changes in measured data due to brain activity. The perturbation model typically uses approximate baseline optical parameters of the different brain compartments, since the actual baseline optical parameters are unknown. We simulated the effects of these approximate baseline optical parameters using parameter variations earlier reported in literature, and brain atlases from four adult subjects. We report the errors in estimated activation contrast, localization, and area when incorrect baseline values were used. Further, we developed a post-processing technique based on deep learning methods that can reduce the effects due to inaccurate baseline optical parameters. The method improved imaging of brain activation changes in the presence of such errors.

Randomized recursive techniques for image reconstruction in diffuse optical tomography

Randomized recursive techniques for image reconstruction in diffuse optical tomography