Current Imaging Methods Research Articles

Quantitative texture analysis using machine learning for predicting interpretable pulmonary perfusion from non-contrast computed tomography in pulmonary embolism patients

BackgroundPulmonary embolism (PE) is life-threatening and requires timely and accurate diagnosis, yet current imaging methods, like computed tomography pulmonary angiography, present limitations, particularly for patients with contraindications to iodinated contrast agents. We aimed to develop a quantitative texture analysis pipeline using machine learning (ML) based on non-contrast thoracic computed tomography (CT) scans to discover intensity and textural features correlated with regional lung perfusion (Q) physiology and pathology and synthesize voxel-wise Q surrogates to assist in PE diagnosis.MethodsWe retrospectively collected 99mTc-labeled macroaggregated albumin Q-SPECT/CT scans from patients suspected of PE, including an internal dataset of 76 patients (64 for training, 12 for testing) and an external testing dataset of 49 patients. Quantitative CT features were extracted from segmented lung subregions and underwent a two-stage feature selection pipeline. The prior-knowledge-driven preselection stage screened for robust and non-redundant perfusion-correlated features, while the data-driven selection stage further filtered features by fitting ML models for classification. The final classification model, trained with the highest-performing PE-associated feature combination, was evaluated in the testing cohorts based on the Area Under the Curve (AUC) for subregion-level predictability. The voxel-wise Q surrogate was then synthesized using the final selected feature maps (FMs) and model score maps (MSMs) to investigate spatial distributions. The Spearman correlation coefficient (SCC) and Dice similarity coefficient (DSC) were used to assess the spatial consistency between FMs or MSMs and Q-SPECT scans.ResultsThe optimal model performance achieved an AUC of 0.863 during internal testing and 0.828 on the external testing cohort. The model identified a combination containing 14 intensity and textural features that were non-redundant, robust, and capable of distinguishing between high- and low-functional lung regions. Spatial consistency assessment in the internal testing cohort showed moderate-to-high agreement between MSMs and reference Q-SPECT scans, with median SCC of 0.66, median DSCs of 0.86 and 0.64 for high- and low-functional regions, respectively.ConclusionsThis study validated the feasibility of using quantitative texture analysis and a data-driven ML pipeline to generate voxel-wise lung perfusion surrogates, providing a radiation-free, widely accessible alternative to functional lung imaging in managing pulmonary vascular diseases.Clinical trial numberNot applicable.

A geometric view of seismic wavefields: implications for imaging dense clusters of events

SUMMARY Imaging dense clusters of seismicity is crucial to many problems in seismology: to delineate complex systems of faults, provide constraints on the causes of volcanic and cryogenic swarms, and to shed light on possible means to prevent damaging induced seismicity in mining, geothermal, and oil and gas extraction activities. Current imaging methods rely upon high-resolution relative location techniques, commonly requiring arrival-time picks for seismic phases. This paper examines an alternative approach, based upon concepts drawn from differential geometry, that images directly from waveform data. It relies upon the common assumption of spatial continuity of seismic wavefield observations, which implies that a differentiable map exists between the source region to be imaged and waveform observations considered as elements of a vector space. The map creates an image of event clusters on a Riemannian manifold embedded in that vector space. The image can be visualized by projecting the observations into a tangent space of the manifold and is a distorted rendering of cluster geometry. However, the distortion can be predicted and removed if a model for wavefield propagation is available. This visualization approach is applicable to clusters of uniform events with highly similar waveforms, such as are commonly acquired with correlation detectors or other pattern matching techniques. To assess its performance, it is applied to the closely related reciprocal problem of imaging the (known) geometry of an array from observations by the array of several regional events. Differences between the original problem and its reciprocal analogue are noted and controlled for in the analysis. Chief among the differences is the necessity for aligning the waveforms in the original problem, which, to maintain consistency with the original problem, is solved in the reciprocal problem by a generalization of the VanDecar–Crosson algorithm. The VanDecar–Crosson algorithm exhibits a bias, shown through an analysis of the situation when the observed wavefields are adequately modelled as plane waves. In that circumstance, the bias can be predicted and removed. In a test using a portion of a large-N array, this imaging approach is shown to successfully reconstruct the array geometry. The method is applicable directly to infinitesimal array apertures, but is extended to a larger aperture by partitioning the image into local, effectively infinitesimal overlapping subsets. These are inverted, then assembled into a global picture of the array geometry using constraints provided by the overlapped regions. Although demonstrated in a reciprocal array context, the method appears viable for imaging clusters of events with highly similar source mechanisms and time histories.

Using 2D U-Net convolutional neural networks for automatic acetabular and proximal femur segmentation of hip MRI images and morphological quantification: a preliminary study in DDH

BackgroundDevelopmental dysplasia of the hip (DDH) is a common pediatric orthopedic condition characterized by varying degrees of acetabular dysplasia and hip dislocation. Current 2D imaging methods often fail to provide sufficient anatomical detail for effective treatment planning, leading to higher rates of misdiagnosis and missed diagnoses. MRI, with its advantages of being radiation-free, multi-planar, and containing more anatomical information, can provide the crucial morphological and volumetric data needed to evaluate DDH. However, manual techniques for measuring parameters like the center–edge angle (CEA) and acetabular index (AI) are time-consuming. Automating these processes is essential for accurate clinical assessments and personalized treatment strategies.MethodsThis study employed a U-Net-based CNN model to automate the segmentation of hip MRI images in children. The segmentation process was validated using a leave-one-out method during training. Subsequently, the segmented hip joint images were utilized in clinical settings to perform automated measurements of key angles: AI, femoral neck angle (FNA), and CEA. This automated approach aimed to replace manual measurements and provide an objective reference for clinical assessments.ResultsThe U-Net-based network demonstrates high effectiveness in hip segmentation compared to manual radiologist segmentations. In test data, it achieves average DSC values of 0.9109 (acetabulum) and 0.9244 (proximal femur), with a 91.76% segmentation success rate. The average ASD values are 0.3160 mm (acetabulum) and 0.6395 mm (proximal femur) in test data, with Ground Truth (GT) edge points and predicted segmentation maps having a mean distance of less than 1 mm. Using automated segmentation models for clinical hip angle measurements (CEA, AI, FNA) shows no statistical difference compared to manual measurements (p > 0.05).ConclusionUtilizing U-Net-based image segmentation and automated measurement of morphological parameters significantly enhances the accuracy and efficiency of DDH assessment. These methods improve precision in automatic measurements and provide an objective basis for clinical diagnosis and treatment of DDH.

Deep-learning based discrimination of pathologic complete response using MRI in HER2-positive and triple-negative breast cancer

Distinguishing between pathologic complete response and residual cancer after neoadjuvant chemotherapy (NAC) is crucial for treatment decisions, but the current imaging methods face challenges. To address this, we developed deep-learning models using post-NAC dynamic contrast-enhanced MRI and clinical data. A total of 852 women with human epidermal growth factor receptor 2 (HER2)-positive or triple-negative breast cancer were randomly divided into a training set (n = 724) and a validation set (n = 128). A 3D convolutional neural network model was trained on the training set and validated independently. The main models were developed using cropped MRI images, but models using uncropped whole images were also explored. The delayed-phase model demonstrated superior performance compared to the early-phase model (area under the receiver operating characteristic curve [AUC] = 0.74 vs. 0.69, P = 0.013) and the combined model integrating multiple dynamic phases and clinical data (AUC = 0.74 vs. 0.70, P = 0.022). Deep-learning models using uncropped whole images exhibited inferior performance, with AUCs ranging from 0.45 to 0.54. Further refinement and external validation are necessary for enhanced accuracy.

Development of an antigen-based approach to noninvasively image CAR T cells in real time and as a predictive tool.

CAR T cell therapy has revolutionized the treatment of a spectrum of blood-related malignancies. However, treatment responses vary among cancer types and patients. Accurate monitoring of CAR T cell dynamics is crucial for understanding and evaluating treatment efficacy. Positron emission tomography (PET) offers a comprehensive view of CAR T cell homing, especially in critical organs such as lymphoid structures and bone marrow. This information will help assess treatment response and predict relapse risk. Current PET imaging methods for CAR T require genetic modifications, limiting clinical use. To overcome this, we developed an antigen-based imaging approach enabling whole-body CAR T cell imaging. The probe detects CAR T cells in vivo without affecting their function. In an immunocompetent B cell leukemia model, CAR-PET signal in the spleen predicted early mortality risk. The antigen-based CAR-PET approach allows assessment of CAR T therapy responses without altering established clinical protocols. It seamlessly integrates with FDA-approved and future CAR T cell generations, facilitating broader clinical application.

An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis.

Current imaging methods for diagnosing breast cancer (BC) are associated with limited sensitivity and specificity and modest positive predictive power. The recent progress in image analysis using artificial intelligence (AI) has created great promise to improve BC diagnosis and subtype differentiation. In this case, novel quantitative computational methods, such as radiomics, have been developed to enhance the sensitivity and specificity of early BC diagnosis and classification. The potential of radiomics in improving the diagnostic efficacy of imaging studies has been shown in several studies. In this review article, we discuss the radiomics workflow and current handcrafted radiomics methods in the diagnosis and classification of BC based on the most recent studies on different imaging modalities, e.g., MRI, mammography, contrast-enhanced spectral mammography (CESM), ultrasound imaging, and digital breast tumosynthesis (DBT). We also discuss current challenges and potential strategies to improve the specificity and sensitivity of radiomics in breast cancer to help achieve a higher level of BC classification and diagnosis in the clinical setting. The growing field of AI incorporation with imaging information has opened a great opportunity to provide a higher level of care for BC patients.

DNA methylation biomarkers for predicting lymph node metastasis in colorectal cancer.

Colorectal cancer is one of the most common cancers worldwide. Lymph node metastasis is an important marker of colorectal cancer progression and plays a key role in the evaluation of patient prognosis. Accurate preoperative assessment of lymph node metastasis is crucial for devising appropriate treatment plans. However, current clinical imaging methods have limitations in many aspects. Therefore, the discovery of a method for accurately predicting lymph node metastasis is crucial clinical decision-making. DNA methylation is a common epigenetic modification that can regulate gene expression, which also has an important impact on the development of colorectal cancer. It is considered to be a promising biomarker with good specificity and stability and has promising application in predicting lymph node metastasis in patients with colorectal cancer. This article reviews the characteristics and limitations of currently available methods for predicting lymph node metastasis in patients with colorectal cancer and discusses the role of DNA methylation as a biomarker.

Brain-implantable needle-type CMOS imaging device enables multi-layer dissection of seizure calcium dynamics in the hippocampus

Objective: Current neuronal imaging methods mostly use bulky lenses that either impede animal behavior or prohibit multi-depth imaging. To overcome these limitations, we developed a lightweight lensless biophotonic system for neuronal imaging, enabling compact and simultaneous visualization of multiple brain layers. Approach: Our developed ‘CIS-NAIST’ device integrates a micro-CMOS image sensor, thin-film fluorescence filter, micro-LEDs, and a needle-shaped flexible printed circuit. With this device, we monitored neuronal calcium dynamics during seizures across the different layers of the hippocampus and employed machine learning techniques for seizure classification and prediction. Main results: The CIS-NAIST device revealed distinct calcium activity patterns across the CA1, molecular interlayer, and dentate gyrus. Our findings indicated an elevated calcium amplitude activity specifically in the dentate gyrus compared to other layers. Then, leveraging the multi-layer data obtained from the device, we successfully classified seizure calcium activity and predicted seizure behavior using Long Short-Term Memory and Hidden Markov models. Significance: Taken together, our ‘CIS-NAIST’ device offers an effective and minimally invasive method of seizure monitoring that can help elucidate the mechanisms of temporal lobe epilepsy.

Detection of Total Hip Replacement Loosening Based on Structure-Borne Sound: Influence of the Position of the Sensor on the Hip Stem.

Accurate detection of implant loosening is crucial for early intervention in total hip replacements, but current imaging methods lack sensitivity and specificity. Vibration methods, already successful in dentistry, represent a promising approach. In order to detect loosening of the total hip replacement, excitation and measurement should be performed intracorporeally to minimize the influence of soft tissue on damping of the signals. However, only implants with a single sensor intracorporeally integrated into the implant for detecting vibrations have been presented in the literature. Considering different mode shapes, the sensor's position on the implant is assumed to influence the signals. In the work at hand, the influence of the position of the sensor on the recording of the vibrations on the implant was investigated. For this purpose, a simplified test setup was created with a titanium rod implanted in a cylinder of artificial cancellous bone. Mechanical stimulation via an exciter attached to the rod was recorded by three accelerometers at varying positions along the titanium rod. Three states of peri-implant loosening within the bone stock were simulated by extracting the bone material around the titanium rod, and different markers were analyzed to distinguish between these states of loosening. In addition, a modal analysis was performed using the finite element method to analyze the mode shapes. Distinct differences in the signals recorded by the acceleration sensors within defects highlight the influence of sensor position on mode detection and natural frequencies. Thus, using multiple sensors could be advantageous in accurately detecting all modes and determining the implant loosening state more precisely.

On-line correlative imaging of cryo-PALM and soft X-ray tomography for identification of subcellular structures.

Correlative imaging of fluorescence microscopy and soft X-ray microscopy plays a crucial role in exploring the relationship between structure and function in cellular biology. However, the current correlative imaging methods are limited either to off-line or low-resolution fluorescence imaging. In this study, we developed an integrated on-line cryogenic photoactivated localization microscopy (cryo-PALM) system at a soft X-ray microscopy station. This design eliminates some critical issues such as sample damage and complex post-correlation arising from transferring samples between different cryostages. Furthermore, we successfully achieved correlative imaging of cryopreserved near-native cells, with a resolution of about 50 nm of cryo-PALM. Therefore, the developed on-line correlation imaging platform provides a powerful tool for investigating the intricate relationship between structure and function in biological and molecular interactions, as well as in other life science disciplines.

Exploratory Tau PET/CT with [11C]PBB3 in Patients with Suspected Alzheimer's Disease and Frontotemporal Lobar Degeneration: A Pilot Study on Correlation with PET Imaging and Cerebrospinal Fluid Biomarkers.

Accurately diagnosing Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) is challenging due to overlapping symptoms and limitations of current imaging methods. This study investigates the use of [11C]PBB3 PET/CT imaging to visualize tau pathology and improve diagnostic accuracy. Given diagnostic challenges with symptoms and conventional imaging, [11C]PBB3 PET/CT's potential to enhance accuracy was investigated by correlating tau pathology with cerebrospinal fluid (CSF) biomarkers, positron emission tomography (PET), computed tomography (CT), amyloid-beta, and Mini-Mental State Examination (MMSE). We conducted [11C]PBB3 PET/CT imaging on 24 patients with suspected AD or FTLD, alongside [11C]PiB PET/CT (13 patients) and [18F]FDG PET/CT (15 patients). Visual and quantitative assessments of [11C]PBB3 uptake using standardized uptake value ratios (SUV-Rs) and correlation analyses with clinical assessments were performed. The scans revealed distinct tau accumulation patterns; 13 patients had no or faint uptake (PBB3-negative) and 11 had moderate to pronounced uptake (PBB3-positive). Significant inverse correlations were found between [11C]PBB3 SUV-Rs and MMSE scores, but not with CSF-tau or CSF-amyloid-beta levels. Here, we show that [11C]PBB3 PET/CT imaging can reveal distinct tau accumulation patterns and correlate these with cognitive impairment in neurodegenerative diseases. Our study demonstrates the potential of [11C]PBB3-PET imaging for visualizing tau pathology and assessing disease severity, offering a promising tool for enhancing diagnostic accuracy in AD and FTLD. Further research is essential to validate these findings and refine the use of tau-specific PET imaging in clinical practice, ultimately improving patient care and treatment outcomes.

In Vivo PET Detection of Lung Micrometastasis in Mice by Targeting Endothelial VCAM-1 Using a Dual-Contrast PET/MRI Probe.

Current clinical diagnostic imaging methods for lung metastases are sensitive only to large tumours (1-2 mm cross-sectional diameter), and early detection can dramatically improve treatment. We have previously demonstrated that an antibody-targeted MRI contrast agent based on microparticles of iron oxide (MPIO; 1 μm diameter) enables the imaging of endothelial vascular cell adhesion molecule-1 (VCAM-1). Using a mouse model of lung metastasis, upregulation of endothelial VCAM-1 expression was demonstrated in micrometastasis-associated vessels but not in normal lung tissue, and binding of VCAM-MPIO to these vessels was evident histologically. Owing to the lack of proton MRI signals in the lungs, we modified the VCAM-MPIO to include zirconium-89 (89Zr, t1/2 = 78.4 h) in order to allow the in vivo detection of lung metastases by positron emission tomography (PET). Using this new agent (89Zr-DFO-VCAM-MPIO), it was possible to detect the presence of micrometastases within the lung in vivo from ca. 140 μm in diameter. Histological analysis combined with autoradiography confirmed the specific binding of the agent to the VCAM-1 expressing vasculature at the sites of pulmonary micrometastases. By retaining the original VCAM-MPIO as the basis for this new molecular contrast agent, we have created a dual-modality (PET/MRI) agent for the concurrent detection of lung and brain micrometastases.

Assessment of Arterial Involvement in Pancreatic Cancer: Utility of Reconstructed CT Images Perpendicular to Artery.

The purpose of this study was to investigate the utility of reconstructed CT images perpendicular to the artery for assessing arterial involvement from pancreatic cancer and compare the interobserver variability between it and the current diagnostic imaging method. This retrospective study included patients with pancreatic cancer in the pancreatic body or tail who underwent preoperative pancreatic protocol CT and distal pancreatectomy. Five radiologists used axial and coronal CT images (current method) and perpendicular reconstructed CT images (proposed method) to determine if the degree of solid soft-tissue contact with the splenic artery was ≤180° or >180°. The generalized estimating equations were used to compare the diagnostic performance of solid soft-tissue contact >180° between the current and proposed methods. Fleiss' ĸ statistics were used to assess interobserver variability. The sensitivity and negative predictive value for diagnosing solid soft-tissue contact >180° were higher (p < 0.001 for each) and the specificity (p = 0.003) and positive predictive value (p = 0.003) were lower in the proposed method than the current method. Interobserver variability was improved in the proposed method compared with the current method (ĸ = 0.87 vs. 0.67). Reconstructed CT images perpendicular to the artery showed higher sensitivity and negative predictive value for diagnosing solid soft-tissue contact >180° than the current method and demonstrated improved interobserver variability.

Neuroimaging for Early Diagnosis of Alzheimer's Disease: a Review.

Alzheimer's disease (AD) is a progressive neurodegenerative disease that primarily affects people above the age of 60 all around the world. As of now, the cause is unknown and there is no effective cure. The pathological changes of AD have occurred many years before the onset of the disease, and current treatment techniques can only delay the progression of the disease. Because disease-modifying therapies may be most beneficial in the early stages of AD, the clinical significance of an early diagnosis is emphasized. So far, a variety of imaging technologies and related biomarkers have been used to identify and monitor AD, but there are many imaging technologies; finding the most effective imaging technology can assist medical personnel in interpreting the early stages of AD and can also improve patient treatment opportunities. This is, therefore, the main purpose and back-ground of this study. PubMed and other repositories were used in this study to conduct a literature search with various keywords, and relevant articles were reviewed. In this review, different neuroimaging techniques are reviewed which are considered advanced tools to help establish the diagnosis, and in addition, the diagnostic utility, advantages, and limitations of contemporary AD imaging techniques are discussed. The results of the literature review and synthesis show that the prevalence of several in vivo biomarkers helps distinguish affected individuals from healthy controls in the early stages of the disease. Additionally, each current imaging method has its advantages and disadvantages, so no single imaging method is the best diagnostic modality. This article also reviews and draws conclusions on better ways to use the imaging techniques to improve the likelihood of an early diagnosis of AD. It is suggested that future research could focus on expanding the use of imaging technologies and on identifying novel biomarkers manifesting the earliest stages of AD pathology.

Asymmetric Myocardial Involvement as an Early Indicator of Cardiac Dysfunction in Pediatric Dystrophinopathies: A Study on Cardiac Magnetic Resonance (CMR) Parametric Mappings.

Dystrophinopathies, such as Duchenne and Becker muscular dystrophy, frequently lead to cardiomyopathy, being its primary cause of mortality. Detecting cardiac dysfunction early is crucial, but current imaging methods lack insight into microstructural remodeling. This study aims to assess the potential of cardiac magnetic resonance (CMR) parametric mappings for early detection of myocardial involvement in dystrophinopathies and explores whether distinct involvement patterns may indicate impending dysfunction. In this prospective study, 23 dystrophinopathy patients underwent CMR with tissue mappings. To establish a basis for comparison, a control group of 173 subjects was analyzed. CMR protocols included SSFP, T2-weighted and T1-weighted sequences pre and post gadolinium, and tissue mappings for native T1 (nT1), extracellular volume (ECV), and T2 relaxation times. The difference between the left ventricular posterior wall and the interventricular septum was calculated to reveal asymmetric myocardial involvement. Significant differences in LV ejection fraction (LVEF), myocardial mass, and late gadolinium enhancement confirmed abnormalities in patients. Tissue mappings: nT1 (p < 0.001) and ECV (p = 0.002), but not T2, displayed substantial variations, suggesting sensitivity to myocardial involvement. Asymmetric myocardial involvement in nT1 (p = 0.01) and ECV (p = 0.012) between septal and LV posterior wall regions was significant. While higher mapping values didn't correlate with dysfunction, asymmetric involvement in nT1 (ρ=-0.472, p = 0.023) and ECV (ρ=-0.460, p = 0.049) exhibited a significant negative correlation with LVEF. CMR mappings show promise in early myocardial damage detection in dystrophinopathies. Although mapping values may not directly correspond to dysfunction, the negative correlation between asymmetric involvement in nT1 and ECV with LVEF suggests their potential as early biomarkers. Larger, longitudinal studies are needed for a comprehensive understanding and improved risk stratification in dystrophinopathies.

Association of lower extremity peripheral arterial disease with quantitative muscle features from computed tomography angiography

ObjectivesTo explore the association between lower extremity muscle features from CTA and peripheral arterial disease (PAD) severity using digital subtraction angiography (DSA) as reference standard.MethodsInformed consent was waived for this Institutional Review Board approved retrospective study. PAD patients were recruited from July 2016 to September 2020. Two radiologists evaluated PAD severity on DSA and CTA using runoff score. The patients were divided into two groups: mild PAD (DSA score ≤ 7) vs. severe PAD (DSA score > 7). After segmenting lower extremity muscles from CTA, 95 features were extracted for univariable analysis, logistic regression model (LRM) analysis, and sub-dataset analysis (PAD prediction based on only part of the images). AUC of CTA score and LRMs for PAD prediction were calculated. Features were analyzed using Student’s t test and chi-squared test. p < 0.05 was considered statistically significant.ResultsA total of 56 patients (69 ± 11 years; 38 men) with 56 lower legs were enrolled in this study. The lower leg muscles of mild PAD group (36 patients) showed higher CT values (44.6 vs. 39.5, p < 0.001) with smaller dispersion (35.6 vs. 41.0, p < 0.001) than the severe group (20 patients). The AUC of CTA score, LRM-I (constructed with muscle features), and LRM-II (constructed with muscle features and CTA score) for PAD severity prediction were 0.81, 0.84, and 0.89, respectively. The highest predictive performance was observed in the image subset of the middle and inferior segments of lower extremity (LRM-I, 0.83; LRM-II, 0.90).ConclusionsLower extremity muscle features are associated with PAD severity and can be used for PAD prediction.Critical relevance statementQuantitative image features of lower extremity muscles are associated with the degree of lower leg arterial stenosis/occlusion and can be a beneficial supplement to the current imaging methods of vascular stenosis evaluation for the prediction of peripheral arterial disease severity.Key points• Compared with severe PAD, lower leg muscles of mild PAD showed higher CT values (39.5 vs. 44.6, p < 0.001).• Models developed with muscle CT features had AUC = 0.89 for predicting PAD.• PAD severity prediction can be realized through the middle and inferior segment of images (AUC = 0.90).Graphical

Translocator proteinexpression and localization in human endometrium and endometriosis: A potential target for a noninvasive diagnosis?

The limitations of current imaging methods to detect small or superficial endometriotic lesions prompt the search for new molecular targets. TSPO is an 18 KDa protein located in the outer mitochondrial membrane, which can be traced by positron emission tomography (PET) using specific ligands. TSPO is located mostly in neurons and inflammatory sites outside the brain. We hypothesized that it might also be expressed in the human endometrium and endometrial-like tissue, being a target for molecular imaging of endometriosis. This prospective cross-sectional study included 28 women with endometriosis and 11 endometriosis-free controls. Endometriotic lesions (n = 49) and normal peritoneum (n = 13) from endometriosis patients were obtained during laparoscopy, while samples of eutopic endometrium from patients with endometriosis (n = 28) and from control women (n = 11) were collected in the operating room using a flexible device. TSPO mRNA expression was evaluated by quantitative reverse-transcription real-time PCR while protein expression was evaluated by immunohistochemistry with a monoclonal antibody antihuman TSPO. TSPO mRNA expression was detected in an invariable fashion in all tissue types evaluated; however, TSPO protein was found to be more abundant in the glandular epithelium than in the stroma, both in the endometrium and in the endometriotic lesions. Interestingly, hormone therapies did not alter the expression of TSPO, and its presence was mostly negative in tissues adjacent to endometriotic implants. As a proof of concept, the protein expression pattern of TSPO in endometriotic tissue and along the adjacent areas suggests that TSPO-based molecular imaging might be used for noninvasive endometriosis detection.

Transient stimulated Raman scattering spectroscopy and imaging

Stimulated Raman scattering (SRS) has been developed as an essential quantitative contrast for chemical imaging in recent years. However, while spectral lines near the natural linewidth limit can be routinely achieved by state-of-the-art spontaneous Raman microscopes, spectral broadening is inevitable for current mainstream SRS imaging methods. This is because those SRS signals are all measured in the frequency domain. There is a compromise between sensitivity and spectral resolution: as the nonlinear process benefits from pulsed excitations, the fundamental time-energy uncertainty limits the spectral resolution. Besides, the spectral range and acquisition speed are mutually restricted. Here we report transient stimulated Raman scattering (T-SRS), an alternative time-domain strategy that bypasses all these fundamental conjugations. T-SRS is achieved by quantum coherence manipulation: we encode the vibrational oscillations in the stimulated Raman loss (SRL) signal by femtosecond pulse-pair sequence excited vibrational wave packet interference. The Raman spectrum was then achieved by Fourier transform of the time-domain SRL signal. Since all Raman modes are impulsively and simultaneously excited, T-SRS features the natural-linewidth-limit spectral line shapes, laser-bandwidth-determined spectral range, and improved sensitivity. With ~150-fs laser pulses, we boost the sensitivity of typical Raman modes to the sub-mM level. With all-plane-mirror high-speed time-delay scanning, we further demonstrated hyperspectral SRS imaging of live-cell metabolism and high-density multiplexed imaging with the natural-linewidth-limit spectral resolution. T-SRS shall find valuable applications for advanced Raman imaging.

Cohesive framework for non-line-of-sight imaging based on Dirac notation.

The non-line-of-sight (NLOS) imaging field encompasses both experimental and computational frameworks that focus on imaging elements that are out of the direct line-of-sight, for example, imaging elements that are around a corner. Current NLOS imaging methods offer a compromise between accuracy and reconstruction time as experimental setups have become more reliable, faster, and more accurate. However, all these imaging methods implement different assumptions and light transport models that are only valid under particular circumstances. This paper lays down the foundation for a cohesive theoretical framework which provides insights about the limitations and virtues of existing approaches in a rigorous mathematical manner. In particular, we adopt Dirac notation and concepts borrowed from quantum mechanics to define a set of simple equations that enable: i) the derivation of other NLOS imaging methods from such single equation (we provide examples of the three most used frameworks in NLOS imaging: back-propagation, phasor fields, and f-k migration); ii) the demonstration that the Rayleigh-Sommerfeld diffraction operator is the propagation operator for wave-based imaging methods; and iii) the demonstration that back-propagation and wave-based imaging formulations are equivalent since, as we show, propagation operators are unitary. We expect that our proposed framework will deepen our understanding of the NLOS field and expand its utility in practical cases by providing a cohesive intuition on how to image complex NLOS scenes independently of the underlying reconstruction method.

The repeatability of periodontal imaging with intraoral ultrasound scanning.

Ultrasound is a non-invasive and low-cost diagnostic tool widely used in medicine. Recent studies have demonstrated that ultrasound imaging might have the potential to be used intraorally to assess the periodontium by comparing it to current imaging methods. This study aims to characterize the repeatability of intraoral periodontal ultrasound imaging. Two hundred and twenty-three teeth were scanned from fourteen volunteers participating in this study. One operator conducted all the scans in each tooth thrice with a 20MHz intraoral ultrasound. The repeatability of three measurements, alveolar bone crest to the cementoenamel junction (ABC-CEJ), gingival thickness (GT), and alveolar bone thickness (ABT), was calculated with intercorrelation coefficient (ICC). Measurements were also compared with mean absolute deviation (MAD), repeatability coefficient (RC), and descriptive statistics. ICC scores for intra-rater repeatability were 0.917(0.897,0.933), 0.849(0.816,0.878), and 0.790(0.746,0.898), MAD results were 0.610mm (± 0.508), 0.224 (± 0.200), and 0.067 (± 0.060), and RC results were 0.648, 0.327, and 0.121 for ABC-CEJ, GT, and ABT measurements, respectively. Results of the present study pointed towards good or excellent repeatability of ultrasound as a measurement tool for periodontal structures. Clinicians could benefit from the introduction of a novel chairside diagnostic tool. Ultrasound is a non-invasive imaging assessment tool for the periodontium with promising results in the literature. Further validation, establishment of scanning protocols, and commercialization are still needed before ultrasound imaging is available for clinicians.