High-resolution hybrid micro-CT imaging pipeline for mouse brain region segmentation and volumetric morphometry.

BACKGROUND: Brain region segmentation and morphometry in humanized apolipoprotein E (APOE) mouse models with a human NOS2 background (HN) contribute to Alzheimer's disease (AD) research by demonstrating how various risk factors affect the brain. Photon-counting detector (PCD) micro-CT provides faster scan times than MRI, with superior contrast and spatial resolution to energy-integrating detector (EID) micro-CT. This paper presents a pipeline for mouse brain imaging, segmentation, and morphometry from PCD micro-CT. METHODS: We used brains of 26 mice from 3 genotypes (APOE22HN, APOE33HN, APOE44HN). The pipeline included PCD and EID micro-CT scanning, hybrid (PCD and EID) iterative reconstruction, and brain region segmentation using the Small Animal Multivariate Brain Analysis (SAMBA) tool. We applied SAMBA to transfer brain region labels from our new PCD CT atlas to individual PCD brains via diffeomorphic registration. Region-based and voxel-based analyses were used for comparisons by genotype and sex. RESULTS: Together, PCD and EID scanning take ~5 hours to produce images with a voxel size of 22 µm, which is faster than MRI protocols for mouse brain morphometry with voxel size above 40 µm. Hybrid iterative reconstruction generates PCD images with minimal artifacts and higher spatial resolution and contrast than EID images. Our PCD atlas is qualitatively and quantitatively similar to the prior MRI atlas and successfully transfers labels to PCD brains in SAMBA. Male and female mice had significant volume differences in 26 regions, including parts of the entorhinal cortex and cingulate cortex. APOE22HN brains were larger than APOE44HN brains in clusters from the hippocampus, a region where atrophy is associated with AD. CONCLUSIONS: This work establishes a pipeline for mouse brain analysis using PCD CT, from staining to imaging and labeling brain images. Our results validate the effectiveness of the approach, setting a foundation for research on AD mouse models while reducing scanning durations.

A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images.

Photon-counting CT (PCCT) is powerful for spectral imaging and material decomposition but produces noisy weighted filtered backprojection (wFBP) reconstructions. Although iterative reconstruction effectively denoises these images, it requires extensive computation time. To overcome this limitation, we propose a deep learning (DL) model, UnetU, which quickly estimates iterative reconstruction from wFBP. Utilizing a 2D U-net convolutional neural network (CNN) with a custom loss function and transformation of wFBP, UnetU promotes accurate material decomposition across various photon-counting detector (PCD) energy threshold settings. UnetU outperformed multi-energy non-local means (ME NLM) and a conventional denoising CNN called UnetwFBP in terms of root mean square error (RMSE) in test set reconstructions and their respective matrix inversion material decompositions. Qualitative results in reconstruction and material decomposition domains revealed that UnetU is the best approximation of iterative reconstruction. In reconstructions with varying undersampling factors from a high dose ex vivo scan, UnetU consistently gave higher structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) to the fully sampled iterative reconstruction than ME NLM and UnetwFBP. This research demonstrates UnetU's potential as a fast (i.e., 15 times faster than iterative reconstruction) and generalizable approach for PCCT denoising, holding promise for advancing preclinical PCCT research.

Material decomposition from photon-counting CT using a convolutional neural network and energy-integrating CT training labels.

Objective.Photon-counting CT (PCCT) has better dose efficiency and spectral resolution than energy-integrating CT, which is advantageous for material decomposition. Unfortunately, the accuracy of PCCT-based material decomposition is limited due to spectral distortions in the photon-counting detector (PCD).Approach.In this work, we demonstrate a deep learning (DL) approach that compensates for spectral distortions in the PCD and improves accuracy in material decomposition by using decomposition maps provided by high-dose multi-energy-integrating detector (EID) data as training labels. We use a 3D U-net architecture and compare networks with PCD filtered back projection (FBP) reconstruction (FBP2Decomp), PCD iterative reconstruction (Iter2Decomp), and PCD decomposition (Decomp2Decomp) as the input.Main results.We found that our Iter2Decomp approach performs best, but DL outperforms matrix inversion decomposition regardless of the input. Compared to PCD matrix inversion decomposition, Iter2Decomp gives 27.50% lower root mean squared error (RMSE) in the iodine (I) map and 59.87% lower RMSE in the photoelectric effect (PE) map. In addition, it increases the structural similarity (SSIM) by 1.92%, 6.05%, and 9.33% in the I, Compton scattering (CS), and PE maps, respectively. When taking measurements from iodine and calcium vials, Iter2Decomp provides excellent agreement with multi-EID decomposition. One limitation is some blurring caused by our DL approach, with a decrease from 1.98 line pairs/mm at 50% modulation transfer function (MTF) with PCD matrix inversion decomposition to 1.75 line pairs/mm at 50% MTF when using Iter2Decomp.Significance.Overall, this work demonstrates that our DL approach with high-dose multi-EID derived decomposition labels is effective at generating more accurate material maps from PCD data. More accurate preclinical spectral PCCT imaging such as this could serve for developing nanoparticles that show promise in the field of theranostics (therapy and diagnostics).

Determining Thresholds for Three Indices of Autoregulation to Identify the Lower Limit of Autoregulation During Cardiac Surgery.

OBJECTIVES: Monitoring cerebral autoregulation may help identify the lower limit of autoregulation in individual patients. Mean arterial blood pressure below lower limit of autoregulation appears to be a risk factor for postoperative acute kidney injury. Cerebral autoregulation can be monitored in real time using correlation approaches. However, the precise thresholds for different cerebral autoregulation indexes that identify the lower limit of autoregulation are unknown. We identified thresholds for intact autoregulation in patients during cardiopulmonary bypass surgery and examined the relevance of these thresholds to postoperative acute kidney injury. DESIGN: A single-center retrospective analysis. SETTING: Tertiary academic medical center. PATIENTS: Data from 59 patients was used to determine precise cerebral autoregulation thresholds for identification of the lower limit of autoregulation. These thresholds were validated in a larger cohort of 226 patients. METHODS AND MAIN RESULTS: Invasive mean arterial blood pressure, cerebral blood flow velocities, regional cortical oxygen saturation, and total hemoglobin were recorded simultaneously. Three cerebral autoregulation indices were calculated, including mean flow index, cerebral oximetry index, and hemoglobin volume index. Cerebral autoregulation curves for the three indices were plotted, and thresholds for each index were used to generate threshold- and index-specific lower limit of autoregulations. A reference lower limit of autoregulation could be identified in 59 patients by plotting cerebral blood flow velocity against mean arterial blood pressure to generate gold-standard Lassen curves. The lower limit of autoregulations defined at each threshold were compared with the gold-standard lower limit of autoregulation determined from Lassen curves. The results identified the following thresholds: mean flow index (0.45), cerebral oximetry index (0.35), and hemoglobin volume index (0.3). We then calculated the product of magnitude and duration of mean arterial blood pressure less than lower limit of autoregulation in a larger cohort of 226 patients. When using the lower limit of autoregulations identified by the optimal thresholds above, mean arterial blood pressure less than lower limit of autoregulation was greater in patients with acute kidney injury than in those without acute kidney injury. CONCLUSIONS: This study identified thresholds of intact and impaired cerebral autoregulation for three indices and showed that mean arterial blood pressure below lower limit of autoregulation is a risk factor for acute kidney injury after cardiac surgery.

Motility-based label-free detection of parasites in bodily fluids using holographic speckle analysis and deep learning.

Parasitic infections constitute a major global public health issue. Existing screening methods that are based on manual microscopic examination often struggle to provide sufficient volumetric throughput and sensitivity to facilitate early diagnosis. Here, we demonstrate a motility-based label-free computational imaging platform to rapidly detect motile parasites in optically dense bodily fluids by utilizing the locomotion of the parasites as a specific biomarker and endogenous contrast mechanism. Based on this principle, a cost-effective and mobile instrument, which rapidly screens ~3.2 mL of fluid sample in three dimensions, was built to automatically detect and count motile microorganisms using their holographic time-lapse speckle patterns. We demonstrate the capabilities of our platform by detecting trypanosomes, which are motile protozoan parasites, with various species that cause deadly diseases affecting millions of people worldwide. Using a holographic speckle analysis algorithm combined with deep learning-based classification, we demonstrate sensitive and label-free detection of trypanosomes within spiked whole blood and artificial cerebrospinal fluid (CSF) samples, achieving a limit of detection of ten trypanosomes per mL of whole blood (~five-fold better than the current state-of-the-art parasitological method) and three trypanosomes per mL of CSF. We further demonstrate that this platform can be applied to detect other motile parasites by imaging Trichomonas vaginalis, the causative agent of trichomoniasis, which affects 275 million people worldwide. With its cost-effective, portable design and rapid screening time, this unique platform has the potential to be applied for sensitive and timely diagnosis of neglected tropical diseases caused by motile parasites and other parasitic infections in resource-limited regions.

Stem Cells in Pulmonary Disease and Regeneration.

The epithelial cells lining the mammalian lung are subjected to constant interaction with the external environment, necessitating robust regeneration strategies to deal with cell loss due to natural turnover or damage arising from inhaled agents or disease. Since lung epithelial function extends beyond respiratory gas exchange to include roles such as immune defense and mucociliary clearance, a diverse complement of epithelial cell types exists that are regionally distributed along the respiratory tree and extensive surface area of the alveolar interface. Although steady-state turnover of the epithelium appears to be relatively low in ideal situations, the vital role of the lung requires stem and progenitor cell populations that can promptly respond to the loss or damage of epithelial tissues. The identity and role of stem cell populations that carry out repair and replacement in the lung has begun to be clarified in recent years, led by cell lineage tracking experiments in the mouse lung, which have revealed a complex interplay of differentiation, transdifferentiation, and dedifferentiation between lung stem cells and functional respiratory cell populations. In this review article, we present the current understanding of the stem cell populations within the pulmonary epithelium and describe ongoing efforts to use these stem cell populations to generate models for exploring lung function and disease.

Functional Enterospheres Derived In Vitro from Human Pluripotent Stem Cells.

Intestinal organoids derived from human pluripotent stem cells (hPSCs) are valuable in vitro research models that enable simplified access to human gastrointestinal tissues. Here, we report the in vitro generation of enterospheres (hEnS) from hPSC-derived gastrointestinal epithelial precursors. hEnS are cystic spheroids with a simple uniform structure composed entirely of intestinal epithelium. hEnS express markers of mature brush border cells and share a transcriptome profile similar to that of more mature intestinal organoids. Modulation of signaling cues enables control of hEnS growth and differentiation, including long-term propagation. We show that hEnS can be exploited for functional studies: hEnS display an innate immune response when treated with enteric pathogens, and transgenic modification of hEnS with a fluorescence cell-cycle reporter enables hEnS-forming stem cell enrichment. Our work establishes hEnS as an accessible and tractable in vitro modeling system for studying human gastrointestinal biology.

Organoids as a model system for studying human lung development and disease.

The lung is a complex organ comprising multiple cell types that perform a variety of vital processes, including immune defense and gas exchange. Diseases of the lung, such as chronic obstructive pulmonary disease, asthma and lung cancer, together represent one of the largest causes of patient suffering and mortality. Logistical barriers that hamper access to embryonic, normal adult or diseased lung tissue currently hinder the study of lung disease. In vitro lung modeling represents an attractive and accessible avenue for investigating lung development, function and disease pathology, but accurately modeling the lung in vitro requires a system that recapitulates the structural features of the native lung. Organoids are stem cell-derived three-dimensional structures that are supported by an extracellular matrix and contain multiple cell types whose spatial arrangement and interactions mimic those of the native organ. Recently, organoids representative of the respiratory system have been generated from adult lung stem cells and human pluripotent stem cells. Ongoing studies are showing that organoids may be used to model human lung development, and can serve as a platform for interrogating the function of lung-related genes and signalling pathways. In a therapeutic context, organoids may be used for modeling lung diseases, and as a platform for screening for drugs that alleviate respiratory disease. Here, we summarize the organoid-forming capacity of respiratory cells, current lung organoid technologies and their potential use in future therapeutic applications.

A comparison of methods to measure the magnetic moment of magnetotactic bacteria through analysis of their trajectories in external magnetic fields.

Magnetotactic bacteria possess organelles called magnetosomes that confer a magnetic moment on the cells, resulting in their partial alignment with external magnetic fields. Here we show that analysis of the trajectories of cells exposed to an external magnetic field can be used to measure the average magnetic dipole moment of a cell population in at least five different ways. We apply this analysis to movies of Magnetospirillum magneticum AMB-1 cells, and compare the values of the magnetic moment obtained in this way to that obtained by direct measurements of magnetosome dimension from electron micrographs. We find that methods relying on the viscous relaxation of the cell orientation give results comparable to that obtained by magnetosome measurements, whereas methods relying on statistical mechanics assumptions give systematically lower values of the magnetic moment. Since the observed distribution of magnetic moments in the population is not sufficient to explain this discrepancy, our results suggest that non-thermal random noise is present in the system, implying that a magnetotactic bacterial population should not be considered as similar to a paramagnetic material.