Multimodal cross-examination of progressive apraxia of speech by diffusion tensor imaging-based tractography and Tau-PET scans.

Progressive apraxia of speech (PAOS) is a 4R tauopathy characterized by difficulties with motor speech planning. Neurodegeneration in PAOS targets the premotor cortex, particularly the supplementary motor area (SMA), with degeneration of white matter (WM) tracts connecting premotor and motor cortices and Broca's area observed on diffusion tensor imaging (DTI). We aimed to assess flortaucipir uptake across speech-language-related WM tracts identified using DTI tractography in PAOS. Twenty-two patients with PAOS and 26 matched healthy controls were recruited by the Neurodegenerative Research Group (NRG) and underwent MRI and flortaucipir-PET. The patient population included patients with primary progressive apraxia of speech (PPAOS) and non-fluent variant/agrammatic primary progressive aphasia (agPPA). Flortaucipir PET scans and DTI were coregistered using rigid registration with a mutual information cost function in subject space. Alignments between DTI and flortaucipir PET were inspected in all cases. Whole-brain tractography was calculated using deterministic algorithms by a tractography reconstruction tool (DSI-studio) and specific tracts were identified using an automatic fiber tracking atlas-based method. Fractional anisotropy (FA) and flortaucipir standardized uptake value ratios (SUVRs) were averaged across the frontal aslant tract, arcuate fasciculi, inferior frontal-occipital fasciculus, inferior and middle longitudinal fasciculi, as well as the SMA commissural fibers. Reduced FA (p < .0001) and elevated flortaucipir SUVR (p = .0012) were observed in PAOS cases compared to controls across all combined WM tracts. For flortaucipir SUVR, the greatest differentiation of PAOS from controls was achieved with the SMA commissural fibers (area under the receiver operator characteristic curve [AUROC] = 0.83), followed by the left arcuate fasciculus (AUROC = 0.75) and left frontal aslant tract (AUROC = 0.71). Our findings demonstrate that flortaucipir uptake is increased across WM tracts related to speech/language difficulties in PAOS.

Multimodal imaging observation of primary vitreous cysts.

BACKGROUND: Primary vitreous cyst is a clinical variant delineated by the existence of a vesicle within the vitreous cavity from birth. This particular disease tends to be uncommon, and the underlying mechanisms contributing to its pathogenesis remain obscure. CASE PRESENTATION: A 37-year-old male patient manifested blurry vision and floaters in his right eye, a symptomology first noticed three months prior. Upon slit-lamp examination, a pigmented, round, 1 papilla diameter-sized mass was discerned floating in the vitreous. A meticulous examination of the floaters was conducted using an array of multimodal imaging techniques. Other potential conditions, including cysticercosis, toxoplasmosis, and tumors, were conclusively excluded through comprehensive diagnostic tests such as blood examinations, liver ultrasound, and cranial magnetic resonance imaging (MRI), resulting in the diagnosis of a primary vitreous cyst. The patient did not report any other discomforts and did not receive any subsequent interventions or treatments. CONCLUSION: We furnish an exhaustive case report of a patient diagnosed with a primary vitreous cyst. The incorporation of multimodal images in the characterization of the disease anticipates facilitating an enriched comprehension by medical practitioners.

Semi-supervised multi-modal medical image segmentation with unified translation.

The two major challenges to deep-learning-based medical image segmentation are multi-modality and a lack of expert annotations. Existing semi-supervised segmentation models can mitigate the problem of insufficient annotations by utilizing a small amount of labeled data. However, most of these models are limited to single-modal data and cannot exploit the complementary information from multi-modal medical images. A few semi-supervised multi-modal models have been proposed recently, but they have rigid structures and require additional training steps for each modality. In this work, we propose a novel flexible method, semi-supervised multi-modal medical image segmentation with unified translation (SMSUT), and a unique semi-supervised procedure that can leverage multi-modal information to improve the semi-supervised segmentation performance. Our architecture capitalizes on unified translation to extract complementary information from multi-modal data which compels the network to focus on the disparities and salient features among each modality. Furthermore, we impose constraints on the model at both pixel and feature levels, to cope with the lack of annotation information and the diverse representations within semi-supervised multi-modal data. We introduce a novel training procedure tailored for semi-supervised multi-modal medical image analysis, by integrating the concept of conditional translation. Our method has a remarkable ability for seamless adaptation to varying numbers of distinct modalities in the training data. Experiments show that our model exceeds the semi-supervised segmentation counterparts in the public datasets which proves our network's high-performance capabilities and the transferability of our proposed method. The code of our method will be openly available at https://github.com/Sue1347/SMSUT-MedicalImgSegmentation.

Advancing post-traumatic seizure classification and biomarker identification: Information decomposition based multimodal fusion and explainable machine learning with missing neuroimaging data.

A late post-traumatic seizure (LPTS), a consequence of traumatic brain injury (TBI), can potentially evolve into a lifelong condition known as post-traumatic epilepsy (PTE). Presently, the mechanism that triggers epileptogenesis in TBI patients remains elusive, inspiring the epilepsy community to devise ways to predict which TBI patients will develop PTE and to identify potential biomarkers. In response to this need, our study collected comprehensive, longitudinal multimodal data from 48 TBI patients across multiple participating institutions. A supervised binary classification task was created, contrasting data from LPTS patients with those without LPTS. To accommodate missing modalities in some subjects, we took a two-pronged approach. Firstly, we extended a graphical model-based Bayesian estimator to directly classify subjects with incomplete modality. Secondly, we explored conventional imputation techniques. The imputed multimodal information was then combined, following several fusion and dimensionality reduction techniques found in the literature, and subsequently fitted to a kernel- or a tree-based classifier. For this fusion, we proposed two new algorithms: recursive elimination of correlated components (RECC) that filters information based on the correlation between the already selected features, and information decomposition and selective fusion (IDSF), which effectively recombines information from decomposed multimodal features. Our cross-validation findings showed that the proposed IDSF algorithm delivers superior performance based on the area under the curve (AUC) score. Ultimately, after rigorous statistical comparisons and interpretable machine learning examination using Shapley values of the most frequently selected features, we recommend the two following magnetic resonance imaging (MRI) abnormalities as potential biomarkers: the left anterior limb of internal capsule in diffusion MRI (dMRI), and the right middle temporal gyrus in functional MRI (fMRI).

Multimodal data fusion reveals functional and neurochemical correlates of Parkinson's disease.

BACKGROUND: Neurotransmitter deficits and spatial associations among neurotransmitter distribution, brain activity, and clinical features in Parkinson's disease (PD) remain unclear. Better understanding of neurotransmitter impairments in PD may provide potential therapeutic targets. Therefore, we aimed to investigate the spatial relationship between PD-related patterns and neurotransmitter deficits. METHODS: We included 59 patients with PD and 41 age- and sex-matched healthy controls (HCs). The voxel-wise mean amplitude of the low-frequency fluctuation (mALFF) was calculated and compared between the two groups. The JuSpace toolbox was used to test whether spatial patterns of mALFF alterations in patients with PD were associated with specific neurotransmitter receptor/transporter densities. RESULTS: Compared to HCs, patients with PD showed reduced mALFF in the sensorimotor- and visual-related regions. In addition, mALFF alteration patterns were significantly associated with the spatial distribution of the serotonergic, dopaminergic, noradrenergic, glutamatergic, cannabinoid, and acetylcholinergic neurotransmitter systems (p < 0.05, false discovery rate-corrected). CONCLUSIONS: Our results revealed abnormal brain activity patterns and specific neurotransmitter deficits in patients with PD, which may provide new insights into the mechanisms and potential targets for pharmacotherapy.

[Multimodal topographically oriented approach to the study of full-thickness macular holes]. / Mul'timodal'nyi topograficheski orientirovannyi podkhod k izucheniyu skvoznykh makulyarnykh razryvov.

PURPOSE: This article studies the relationship between structural changes according to the findings of optical coherence tomography (OCT) and OCT angiography (OCTA), microperimetry (MP), multifocal electroretinography (mfERG) parameters in topographically corresponding areas of the macular region in idiopathic full-thickness macular holes (FTMH). MATERIAL AND METHODS: OCT, OCTA, MP and mfERG were performed in 14 eyes with FTMH stages I-IV according to Gass. In 13 points at a distance of 0-2.5°, 2.5-5.0°, and 5.0-10.0° from the fixation point, the light sensitivity (LS), amplitude and latency of the P1 component were compared with the size of the hole, the area of cystic changes (CC) at the level of the inner nuclear layer (INL) and the outer plexiform layer and Henle fiber layer complex (OPL+HFL), vessel density in the superficial and deep capillary plexus (SCP and DCP). RESULTS: LS and P1 component amplitude were significantly reduced at a distance of up to 5.0° from the fixation point. LS correlates with the apical and basal diameter of the hole (R> -0.53), the area of CC in the INL (R> -0.62) and the OPL+HFL complex (R> -0.55), the density of vessels in the SCP at a distance of up to 2.5° from the fixation point (R>0.51) and in the DCP at a distance of up to 5° from the fixation point (R>0.49). The P1 amplitude correlates with the basal diameter of the hole (R= -0.38), the area of CC in the INL and the OPL+HFL complex (R> -0.33) and vessel density in the SCP (R=0.37) at a distance of up to 2.5° from the fixation point, as well as vessel density in the DCP at a distance of up to 5° from the fixation point (R=0.47). Vessel density in the DCP is significantly lower in the presence of CC in the retina (p<0.001). CONCLUSION: In FTMH, there is a relationship between bioelectrical activity and LS, and structural disorders, capillary perfusion in different layers of the retina. A multimodal topographically oriented approach allows studying the relationship between structural and functional parameters in individual points of the retina and can be used in monitoring of FTMH after surgical treatment.

Multimodal neuroimaging network associated with executive function in adolescent major depressive disorder patients via cognition-guided magnetic resonance imaging fusion.

Adolescents are high-risk population for major depressive disorder. Executive dysfunction emerges as a common feature of depression and exerts a significant influence on the social functionality of adolescents. This study aimed to identify the multimodal co-varying brain network related to executive function in adolescent with major depressive disorder. A total of 24 adolescent major depressive disorder patients and 43 healthy controls were included and completed the Intra-Extra Dimensional Set Shift Task. Multimodal neuroimaging data, including the amplitude of low-frequency fluctuations from resting-state functional magnetic resonance imaging and gray matter volume from structural magnetic resonance imaging, were combined with executive function using a supervised fusion method named multimodal canonical correlation analysis with reference plus joint independent component analysis. The major depressive disorder showed more total errors than the healthy controls in the Intra-Extra Dimensional Set Shift task. Their performance on the Intra-Extra Dimensional Set Shift Task was negatively related to the 14-item Hamilton Rating Scale for Anxiety score. We discovered an executive function-related multimodal fronto-occipito-temporal network with lower amplitude of low-frequency fluctuation and gray matter volume loadings in major depressive disorder. The gray matter component of the identified network was negatively related to errors made in Intra-Extra Dimensional Set Shift while positively related to stages completed. These findings may help to deepen our understanding of the pathophysiological mechanisms of cognitive dysfunction in adolescent depression.

Multimodal Structural and Functional Characterization of Retinal Vasculopathy with Cerebral Leukoencephalopathy.

OBJECTIVE: To describe and quantify the structural and functional consequences of retinal vasculopathy with cerebral leukoencephalopathy (RVCL) on the neurosensory retina. DESIGN: Cross sectional descriptive study from December 2021 to December 2022. PARTICIPANTS: Retinal vasculopathy with cerebral leukoencephalopathy patients (n = 9, 18 eyes) recruited from the RVCL Research Center at Washington University in St. Louis. METHODS: Retinal vasculopathy with cerebral leukoencephalopathy patients underwent comprehensive ophthalmological evaluation including OCT, OCT angiography (OCTA), ultrawidefield fundus imaging, retinal autofluorescence, dark adaptation, electroretinography (ERG), Goldmann kinetic perimetry, and fluorescein angiography (FA). MAIN OUTCOME MEASURES: Comprehensive characterization from various modalities including best-corrected visual acuity, central subfield thickness (µm) from OCT, foveal avascular zone (mm2) from OCTA, dark adaptation rod intercept (seconds), cone response in ERG, and presence or absence of vascular abnormalities, leakage, neovascularization, and nonperfusion on FA. RESULTS: A total of 18 eyes from 9 individuals were included in this study. The best-corrected visual acuity ranged from 20/15 to 20/70. The mean central subfield thickness from OCT was 275.8 µm (range, 217-488 µm). The mean foveal avascular zone (FAZ) from OCTA was 0.65 (range, 0.18-1.76) mm2. On dark adaptometry, the mean time was 5.02 (range, 2.9-6.5) minutes, and 1 individual had impaired dark adaptation. Electroretinography demonstrated mild cone response impairment in 4 eyes. On FA, there was evidence of macular and peripheral capillary nonperfusion in 16 of 18 eyes and notable areas of vascular leakage and retinal edema in 5 of the 18 eyes. CONCLUSIONS: This study illustrates the phenotypic spectrum of disease and may be clinically valuable for aiding diagnosis, monitoring disease progression, and further elucidating the pathophysiology of RVCL to aid in the development of therapies. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Unilateral Toxoplasma retinochoroiditis with frosted branch angiitis: a multimodal imaging study.

A woman in her late 30s presented with sudden diminution of vision, redness and pain in the right eye (OD) of 10 days' duration. Best corrected visual acuity (BCVA) was 20/160 in OD and 20/20 in the left eye (OS). Anterior segment of OD showed keratic precipitates, flare 3+, cells 2+ and a festooned pupil. Vitreous haze and cells were seen in OD. Frosted branch angiitis (FBA) was seen in all quadrants in OD and old Toxoplasma scar was seen in both eyes. Serum toxoplasma immunoglobulin G (IgG) was positive and IgM negative, and PCR of an aqueous humour sample was negative for Toxoplasma She was diagnosed with toxoplasa retinochoroiditis in OD and treated with intravitreal clindamycin injections, oral anti-Toxoplasma antibiotics and steroids. Three months later, her BCVA in OD was 20/40 with resolving inflammation. She presented 2 months later with a new focus of retinochoroiditis without FBA and an old Toxoplasma scar.

Preparation of Hematoporphyrin-Poly(Lactic Acid) Nanoparticles Encapsulated Perfluoropentane/Salicylic Acid for Enhanced US/CEST MR Bimodal Imaging.

Background: Medical imaging modalities, such as magnetic resonance imaging (MRI), ultrasound, and fluorescence imaging, have gained widespread acceptance in clinical practice for tumor diagnosis. Each imaging modality has its own unique principles, advantages, and limitations, thus necessitating a multimodal approach for a comprehensive disease understanding of the disease process. To enhance diagnostic precision, physicians frequently integrate data from multiple imaging modalities, driving research advancements in multimodal imaging technology research. Methods: In this study, hematoporphyrin-poly (lactic acid) (HP-PLLA) polymer was prepared via ring-opening polymerization and thoroughly characterized using FT-IR, 1H-NMR, XRD, and TGA. HP-PLLA based nanoparticles encapsulating perfluoropentane (PFP) and salicylic acid were prepared via emulsion-solvent evaporation. Zeta potential and mean diameter were assessed using DLS and TEM. Biocompatibility was evaluated via cell migration, hemolysis, and cytotoxicity assays. Ultrasonic imaging was performed with a dedicated apparatus, while CEST MRI was conducted using a 7.0 T animal scanner. Results: We designed and prepared a novel dual-mode nanoimaging probe SA/PFP@HP-PLLA NPs. PFP enhanced US imaging, while salicylic acid bolstered CEST imaging. With an average size of 74.43 ± 1.12 nm, a polydispersity index of 0.175 ± 0.015, and a surface zeta potential of -64.1 ± 2.11 mV. These NPs exhibit excellent biocompatibility and stability. Both in vitro and in vivo experiments confirmed the SA/PFP@HP-PLLA NP's ability to improve tumor characterization and diagnostic precision. Conclusion: The SA/PFP@HP-PLLA NPs demonstrate promising dual-modality imaging capabilities, indicating their potential for preclinical and clinical use as a contrast agent.

The diagnostic value of multimodal imaging based on MR combined with ultrasound in benign and malignant breast diseases.

We aimed to construct and validate a multimodality MRI combined with ultrasound based on radiomics for the evaluation of benign and malignant breast diseases. The preoperative enhanced MRI and ultrasound images of 131 patients with breast diseases confirmed by pathology in Aerospace Center Hospital from January 2021 to August 2023 were retrospectively analyzed, including 73 benign diseases and 58 malignant diseases. Ultrasound and 3.0 T multiparameter MRI scans were performed in all patients. Then, all the data were divided into training set and validation set in a 7:3 ratio. Regions of interest were drawn layer by layer based on ultrasound and MR enhanced sequences to extract radiomics features. The optimal radiomic features were selected by the best feature screening method. Logistic Regression classifier was used to establish models according to the best features, including ultrasound model, MRI model, ultrasound combined with MRI model. The model efficacy was evaluated by the area under the curve (AUC) of the receiver operating characteristic, sensitivity, specificity, and accuracy. The F-test based on ANOVA was used to screen out 20 best ultrasonic features, 11 best MR Features, and 14 best features from the combined model. Among them, texture features accounted for the largest proportion, accounting for 79%.The ultrasound combined with MR Image fusion model based on logistic regression classifier had the best diagnostic performance. The AUC of the training group and the validation group were 0.92 and 091, the sensitivity was 0.80 and 0.67, the specificity was 0.90 and 0.94, and the accuracy was 0.84 and 0.79, respectively. It was better than the simple ultrasound model (AUC of validation set was 0.82) or the simple MR model (AUC of validation set was 0.85). Compared with the traditional ultrasound or magnetic resonance diagnosis of breast diseases, the multimodal model of MRI combined with ultrasound based on radiomics can more accurately predict the benign and malignant breast diseases, thus providing a better basis for clinical diagnosis and treatment.

ReMIND: The Brain Resection Multimodal Imaging Database.

The standard of care for brain tumors is maximal safe surgical resection. Neuronavigation augments the surgeon's ability to achieve this but loses validity as surgery progresses due to brain shift. Moreover, gliomas are often indistinguishable from surrounding healthy brain tissue. Intraoperative magnetic resonance imaging (iMRI) and ultrasound (iUS) help visualize the tumor and brain shift. iUS is faster and easier to incorporate into surgical workflows but offers a lower contrast between tumorous and healthy tissues than iMRI. With the success of data-hungry Artificial Intelligence algorithms in medical image analysis, the benefits of sharing well-curated data cannot be overstated. To this end, we provide the largest publicly available MRI and iUS database of surgically treated brain tumors, including gliomas (n = 92), metastases (n = 11), and others (n = 11). This collection contains 369 preoperative MRI series, 320 3D iUS series, 301 iMRI series, and 356 segmentations collected from 114 consecutive patients at a single institution. This database is expected to help brain shift and image analysis research and neurosurgical training in interpreting iUS and iMRI.

Multimodal MALDI imaging mass spectrometry for improved diagnosis of melanoma.

Imaging mass spectrometry (IMS) provides promising avenues to augment histopathological investigation with rich spatio-molecular information. We have previously developed a classification model to differentiate melanoma from nevi lesions based on IMS protein data, a task that is challenging solely by histopathologic evaluation. Most IMS-focused studies collect microscopy in tandem with IMS data, but this microscopy data is generally omitted in downstream data analysis. Microscopy, nevertheless, forms the basis for traditional histopathology and thus contains invaluable morphological information. In this work, we developed a multimodal classification pipeline that uses deep learning, in the form of a pre-trained artificial neural network, to extract the meaningful morphological features from histopathological images, and combine it with the IMS data. To test whether this deep learning-based classification strategy can improve on our previous results in classification of melanocytic neoplasia, we utilized MALDI IMS data with collected serial H&E stained sections for 331 patients, and compared this multimodal classification pipeline to classifiers using either exclusively microscopy or IMS data. The multimodal pipeline achieved the best performance, with ROC-AUCs of 0.968 vs. 0.938 vs. 0.931 for the multimodal, unimodal microscopy and unimodal IMS pipelines respectively. Due to the use of a pre-trained network to perform the morphological feature extraction, this pipeline does not require any training on large amounts of microscopy data. As such, this framework can be readily applied to improve classification performance in other experimental settings where microscopy data is acquired in tandem with IMS experiments.

PET/MRI multimodality imaging to evaluate changes in glymphatic system function and biomarkers of Alzheimer's disease.

The glymphatic system is considered to play a pivotal role in the clearance of disease-causing proteins in neurodegenerative diseases. This study employed MR diffusion tensor imaging (DTI) to evaluate glymphatic system function and its correlation with brain amyloid accumulation levels measured using [11C]Pittsburgh compound-B (PiB) PET/MRI. Fifty-six patients with mild cognitive impairment and early Alzheimer's disease (AD: 70 ± 11 y) underwent [11C]PiB PET/MRI to assess amyloid deposition and were compared with 27 age-matched cognitively normal volunteers (CN: 69 ± 10y). All participants were evaluated for cognitive function using the Mini Mental State Examination (MMSE) before [11C]PiB PET/MRI. DTI images were acquired during the PET/MRI scan with several other MR sequences. The DTI analysis along the perivascular space index (DTI-ALPS index) was calculated to estimate the functional activity of the glymphatic system. Centiloid scale was applied to quantify amyloid deposition levels from [11C]PiB PET images. All patients in the AD group showed positive [11C]PiB accumulation, whereas all CN participants were negative. ALPS-index for all subjects linearly correlated with PiB centiloid, MMSE scores, and hippocampal volume. The correlation between the ALPS-index and PiB accumulation was more pronounced than with any other biomarkers. These findings suggest that glymphatic system dysfunction is a significant factor in the early stages of Alzheimer's disease.

Multimodal preoperative imaging for transcatheter mitral valve replacement in the domestic sheep model.

Preclinical in vivo evaluation is an essential step in the progression of new cardiac devices into patient use, with studies predominantly performed in the domestic sheep model. A growing area of interest in cardiac device development is transcatheter mitral valve replacement (TMVR). Clinically, multimodal imaging, or computed tomography (CT) and echocardiography (echo) are used extensively to preoperatively determine mitral valve morphology prior to an intervention, but there is no description on how these modalities can be implemented to support preclinical studies. The purpose of this study is to apply clinically relevant CT and echo acquisition and assessment techniques to a large group of naive research sheep in order to analyze and report modality-related effects on mitral valve dimensional reference intervals in the sheep model. To this end, fifty-five adult domestic sheep underwent preoperative CT and echo exams and resultant images were analyzed using a landmark-based multiplanar measurement protocol and compiled into a master dataset for statistical analysis. We found moderate agreement between CT and echo-derived measurements of the mitral valve in sheep and propose the first clinically-relevant dimensional indices for the sheep's naive mitral valve which can be used to guide future studies evaluating novel TMVR devices. This study is the first of its kind in proposing a reproducible method for detailed examination of the mitral valve in the sheep model using clinically-relevant multimodal imaging. As in patients, CT and echo can reveal accurate native mitral valve dimensions in the sheep prior to preclinical TMVR studies.

Development and Validation of a Multimodality Model Based on Whole-Slide Imaging and Biparametric MRI for Predicting Postoperative Biochemical Recurrence in Prostate Cancer.

Purpose To develop and validate a machine learning multimodality model based on preoperative MRI, surgical whole-slide imaging (WSI), and clinical variables for predicting prostate cancer (PCa) biochemical recurrence (BCR) following radical prostatectomy (RP). Materials and Methods In this retrospective study (September 2015 to April 2021), 363 male patients with PCa who underwent RP were divided into training (n = 254; median age, 69 years [IQR, 64-74 years]) and testing (n = 109; median age, 70 years [IQR, 65-75 years]) sets at a ratio of 7:3. The primary end point was biochemical recurrence-free survival. The least absolute shrinkage and selection operator Cox algorithm was applied to select independent clinical variables and construct the clinical signature. The radiomics signature and pathomics signature were constructed using preoperative MRI and surgical WSI data, respectively. A multimodality model was constructed by combining the radiomics signature, pathomics signature, and clinical signature. Using Harrell concordance index (C index), the predictive performance of the multimodality model for BCR was assessed and compared with all single-modality models, including the radiomics signature, pathomics signature, and clinical signature. Results Both radiomics and pathomics signatures achieved good performance for BCR prediction (C index: 0.742 and 0.730, respectively) on the testing cohort. The multimodality model exhibited the best predictive performance, with a C index of 0.860 on the testing set, which was significantly higher than all single-modality models (all P ≤ .01). Conclusion The multimodality model effectively predicted BCR following RP in patients with PCa and may therefore provide an emerging and accurate tool to assist postoperative individualized treatment. Keywords: MR Imaging, Urinary, Pelvis, Comparative Studies Supplemental material is available for this article. © RSNA, 2024.

Multiscale and multimodal imaging for three-dimensional vascular and histomorphological organ structure analysis of the pancreas.

Exocrine and endocrine pancreas are interconnected anatomically and functionally, with vasculature facilitating bidirectional communication. Our understanding of this network remains limited, largely due to two-dimensional histology and missing combination with three-dimensional imaging. In this study, a multiscale 3D-imaging process was used to analyze a porcine pancreas. Clinical computed tomography, digital volume tomography, micro-computed tomography and Synchrotron-based propagation-based imaging were applied consecutively. Fields of view correlated inversely with attainable resolution from a whole organism level down to capillary structures with a voxel edge length of 2.0 µm. Segmented vascular networks from 3D-imaging data were correlated with tissue sections stained by immunohistochemistry and revealed highly vascularized regions to be intra-islet capillaries of islets of Langerhans. Generated 3D-datasets allowed for three-dimensional qualitative and quantitative organ and vessel structure analysis. Beyond this study, the method shows potential for application across a wide range of patho-morphology analyses and might possibly provide microstructural blueprints for biotissue engineering.