scholarly journals Influence of segmented vessel size due to limited imaging resolution on coronary hyperemic flow prediction from arterial crown volume

2016 ◽  
Vol 310 (7) ◽  
pp. H839-H846 ◽  
Author(s):  
P. van Horssen ◽  
M. G. J. T. B. van Lier ◽  
J. P. H. M. van den Wijngaard ◽  
E. VanBavel ◽  
I. E. Hoefer ◽  
...  
Keyword(s):  
Imaging Modality ◽  
Scaling Law ◽  
Image Resolution ◽  
Reference Level ◽  
Imaging Data ◽  
Cast Material ◽  
Truncation Level ◽  
Arterial Network ◽  
Crown Volume ◽  
Imaging Resolution

Computational predictions of the functional stenosis severity from coronary imaging data use an allometric scaling law to derive hyperemic blood flow (Q) from coronary arterial volume (V), Q = αVβ. Reliable estimates of α and β are essential for meaningful flow estimations. We hypothesize that the relation between Q and V depends on imaging resolution. In five canine hearts, fluorescent microspheres were injected into the left anterior descending coronary artery during maximal hyperemia. The coronary arteries of the excised heart were filled with fluorescent cast material, frozen, and processed with an imaging cryomicrotome to yield a three-dimensional representation of the coronary arterial network. The effect of limited image resolution was simulated by assessing scaling law parameters from the virtual arterial network at 11 truncation levels ranging from 50 to 1,000 μm segment radius. Mapped microsphere locations were used to derive the corresponding relative Q using a reference truncation level of 200 μm. The scaling law factor α did not change with truncation level, despite considerable intersubject variability. In contrast, the scaling law exponent β decreased from 0.79 to 0.55 with increasing truncation radius and was significantly lower for truncation radii above 500 μm vs. 50 μm ( P < 0.05). Hyperemic Q was underestimated for vessel truncation above the reference level. In conclusion, flow-crown volume relations confirmed overall power law behavior; however, this relation depends on the terminal vessel radius that can be visualized. The scaling law exponent β should therefore be adapted to the resolution of the imaging modality.

scholarly journals Tracking Transplanted Stem Cells Using Magnetic Resonance Imaging and the Nanoparticle Labeling Method in Urology

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jae Heon Kim ◽  
Hong J. Lee ◽  
Yun Seob Song
Keyword(s):  
Magnetic Resonance Imaging ◽  
Stem Cells ◽  
Molecular Imaging ◽  
Magnetic Resonance ◽  
Imaging Modality ◽  
Imaging Techniques ◽  
Image Resolution ◽  
Imaging Method ◽  
Resonance Imaging

A reliablein vivoimaging method to localize transplanted cells and monitor their viability would enable a systematic investigation of cell therapy. Most stem cell transplantation studies have used immunohistological staining, which does not provide information about the migration of transplanted cellsin vivoin the same host. Molecular imaging visualizes targeted cells in a living host, which enables determining the biological processes occurring in transplanted stem cells. Molecular imaging with labeled nanoparticles provides the opportunity to monitor transplanted cells noninvasively without sacrifice and to repeatedly evaluate them. Among several molecular imaging techniques, magnetic resonance imaging (MRI) provides high resolution and sensitivity of transplanted cells. MRI is a powerful noninvasive imaging modality with excellent image resolution for studying cellular dynamics. Several types of nanoparticles including superparamagnetic iron oxide nanoparticles and magnetic nanoparticles have been used to magnetically label stem cells and monitor viability by MRI in the urologic field. This review focuses on the current role and limitations of MRI with labeled nanoparticles for tracking transplanted stem cells in urology.

scholarly journals Integrating Transcriptomics, Genomics, and Imaging in Alzheimer's Disease: A Federated Model

2021 ◽  
Author(s):  
Jianfeng Wu ◽  
Yanxi Chen ◽  
Panwen Wang ◽  
Richard J Caselli ◽  
Paul M Thompson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Imaging Modality ◽  
Association Studies ◽  
Imaging Genetics ◽  
Health Concern ◽  
Genome Wide Association Studies ◽  
Imaging Data ◽  
Stable Performance

Alzheimer's disease (AD) affects more than 1 in 9 people age 65 and older and becomes an urgent public health concern as the global population ages. In clinical practice, structural magnetic resonance imaging (sMRI) is the most accessible and widely used diagnostic imaging modality. Additionally, genome-wide association studies (GWAS) and transcriptomic, the study of gene expression, also play an important role in understanding AD etiology and progression. Sophisticated imaging genetics systems have been developed to discover genetic factors that consistently affect brain function and structure. However, most studies to date focused on the relationships between brain sMRI and GWAS or brain sMRI and transcriptomics. To our knowledge, few methods have been developed to discover and infer multimodal relationships among sMRI, GWAS, and transcriptomics. To address this, we propose a novel federated model, Genotype-Expression-Imaging Data Integration (GEIDI), to identify genetic and transcriptomic influences on brain sMRI measures. The relationships between brain imaging measures and gene expression are allowed to depend on a person's genotype at the single-nucleotide polymorphism (SNP) level, making the inferences adaptive and personalized. We performed extensive experiments on publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Experimental results demonstrated our proposed method outperformed state-of-the-art expression quantitative trait loci (eQTL) methods for detecting genetic and transcriptomic factors related to AD and has stable performance when data are integrated from multiple sites. Our GEIDI approach may offer novel insights into the relationship among image biomarkers, genotypes, and gene expression and help discover novel genetic targets for potential AD drug treatments.

scholarly journals Inter-observer agreement on the evaluation of automated breast volume scanner for breast masses

2022 ◽  
pp. 1-11
Author(s):  
Yingchun Liu ◽  
Lin Chen ◽  
Jia Zhan ◽  
Xuehong Diao ◽  
Yun Pang ◽  
...  
Keyword(s):  
Imaging Modality ◽  
Observer Agreement ◽  
Breast Volume ◽  
Imaging Data ◽  
Predictive Values ◽  
Breast Masses ◽  
Cutoff Value ◽  
Automated Breast Volume Scanner ◽  
The Masses ◽  
Sensitivity Specificity

Objective: To explore inter-observer agreement on the evaluation of automated breast volume scanner (ABVS) for breast masses. Methods: A total of 846 breast masses in 630 patients underwent ABVS examinations. The imaging data were independently interpreted by senior and junior radiologists regarding the mass size ([Formula: see text][Formula: see text]cm, [Formula: see text][Formula: see text]cm and total). We assessed inter-observer agreement of BI-RADS lexicons, unique descriptors of ABVS coronal planes. Using BI-RADS 3 or 4a as a cutoff value, the diagnostic performances for 331 masses with pathological results in 253 patients were assessed. Results: The overall agreements were substantial for BI-RADS lexicons ([Formula: see text]–0.779) and the characteristics on the coronal plane of ABVS ([Formula: see text]), except for associated features ([Formula: see text]). However, the overall agreement was moderate for orientation ([Formula: see text]) for the masses [Formula: see text][Formula: see text]cm. The agreements were substantial to be perfect for categories 2, 3, 4, 5 and overall ([Formula: see text]–0.918). However, the agreements were moderate to substantial for categories 4a ([Formula: see text]), 4b ([Formula: see text]), and 4c ([Formula: see text]), except for category 4b of the masses [Formula: see text][Formula: see text]cm ([Formula: see text]). Moreover, for radiologists 1 and 2, there were no significant differences in sensitivity, specificity, accuracy, positive and negative predictive values with BI-RADS 3 or 4a as a cutoff value ([Formula: see text] for all). Conclusion: ABVS is a reliable imaging modality for the assessment of breast masses with good inter-observer agreement.

Molecular Scale Imaging With a Mutlilayer Superlens

2005 ◽  
Author(s):  
P. Chaturvedi ◽  
N. Fang
Keyword(s):  
Parallel Imaging ◽  
Imaging Techniques ◽  
Tolerance Analysis ◽  
Evanescent Waves ◽  
Image Resolution ◽  
Recent Theory ◽  
Optimized Design ◽  
Molecular Scale ◽  
Imaging Resolution ◽  
Matrix Scheme

Recent theory [1] suggested a thin negative index film should function as a “superlens”, providing image detail with resolution beyond the diffraction limit—a limitation to which all positive index optics are subject. The superlens allows the recovery of evanescent waves in the image via the excitation of surface plasmons. It has been demonstrated experimentally [2] that a silver superlens allows to resolve features well below the working wavelength. Resolution as high as 60 nanometer (λ/6) half-pitch has been achieved. This unique class of superlens will enable parallel imaging and nanofabrication in a single snapshot, a feat that are not yet available with other nanoscale imaging techniques such as atomic force microscope or scanning electron microscope. In this paper, we explore the possibility of further refining the image resolution using a multilayer superlens [3]. Using a stable transfer matrix scheme, our numerical calculations show an ultimate imaging resolution of λ/24. This is made possible using alternating stacks of alumina (Al2O3) and silver (Ag) layers to enhance a broad spectrum of evanescent waves via surface plasmon modes. Furthermore, we present the effect of alterations in number of layers and thickness to the image transfer function. With optimized design of multilayer superlens (working wavelength of 387.5nm), our study indicates the feasibility of resolving features of 16nm and below. Moreover, our tolerance analysis indicates that a 380 nm commercial light source would degrade slightly the imaging resolution to about 20nm. Preliminary experiments are ongoing to demonstrate the molecular scale imaging resolution. The development of potential low-loss and high resolution superlens opens the door to exciting applications in nanoscale optical metrology and nanomanufacturing.

scholarly journals Automated Classification of Breast Cancer Cells Using High-Throughput Holographic Cytometry

2021 ◽  
Vol 9 ◽  
Author(s):  
Cindy X. Chen ◽  
Han Sang Park ◽  
Hillel Price ◽  
Adam Wax
Keyword(s):  
Single Cell ◽  
Cancer Cells ◽  
High Throughput ◽  
Single Cell Analysis ◽  
Imaging Modality ◽  
Cell Types ◽  
Needle Aspiration ◽  
Microfluidic Channels ◽  
Phase Imaging ◽  
Imaging Data

Holographic cytometry is an ultra-high throughput quantitative phase imaging modality that is capable of extracting subcellular information from millions of cells flowing through parallel microfluidic channels. In this study, we present our findings on the application of holographic cytometry to distinguishing carcinogen-exposed cells from normal cells and cancer cells. This has potential application for environmental monitoring and cancer detection by analysis of cytology samples acquired via brushing or fine needle aspiration. By leveraging the vast amount of cell imaging data, we are able to build single-cell-analysis-based biophysical phenotype profiles on the examined cell lines. Multiple physical characteristics of these cells show observable distinct traits between the three cell types. Logistic regression analysis provides insight on which traits are more useful for classification. Additionally, we demonstrate that deep learning is a powerful tool that can potentially identify phenotypic differences from reconstructed single-cell images. The high classification accuracy levels show the platform’s potential in being developed into a diagnostic tool for abnormal cell screening.

scholarly journals Study on Hyper Spectral Imaging

2021 ◽  
Vol 9 (VI) ◽  
pp. 2017-2021
Author(s):  
Bathula Namratha
Keyword(s):  
Data Acquisition ◽  
Situation Awareness ◽  
Spectral Imaging ◽  
Image Resolution ◽  
Imaging Data ◽  
Spectral Signatures ◽  
Spectral Bands ◽  
Hyper Spectral ◽  
Hyper Spectral Imaging ◽  
Imaging Sensors

Spectroscopy deals with how light behave in the target and recognize materials bases on their different spectral signatures. Spectrum describes the amount and range of radiation that is emitted, reflected or transmitted from the target. Hyper spectral data acquisition and exploitation by providing imaging sensors and software solutions covering hundreds of spectral bands from UV-VIS to SWIS is used to observe Earth, atmospheric science, space situation awareness etc. The work focuses primarily on hyper spectral imaging, data acquisition methods, Image resolution improvement strategies.

scholarly journals Combining A Second-Generation Multivariate Index Assay with Ovarian Imaging Improves the Preoperative Assessment of An Adnexal Mass

2019 ◽  
pp. 1-9
Author(s):  
Rowan G. Bullock ◽  
Alan Smith ◽  
Donald G. Munroe ◽  
Frederick R. Ueland ◽  
Scott T. Goodrich ◽  
...  
Keyword(s):  
Adnexal Mass ◽  
Clinical Performance ◽  
Imaging Modality ◽  
Preoperative Assessment ◽  
Lower Sensitivity ◽  
Imaging Features ◽  
Imaging Data ◽  
Serum Samples ◽  
Pelvic Imaging ◽  
Post Menopausal

Background: To understand the relationship between imaging and the next generation multivariate index assay (MIA2G) in the preoperative assessment of an adnexal mass. Methods: Serum samples and imaging data from two previously published studies are reanalyzed using the MIA2G test. We calculated the clinical performance of MIA2G and discrete imaging features associated with malignant risk. Results: 878 women were eligible for this analysis, 48.3% post-menopausal and 51.7% pre-menopausal. The prevalence of having a malignant pathology was 18%. Ultrasound was the most frequently used imaging modality. The combination of MIA2G “or” ultrasound resulted in higher sensitivity than either test alone, 93.5% compared to 87.6% for MIA2G and 74.2% for ultrasound. The negative predictive value was high: 94.6% for ultrasound, 98.1% for MIA2G “or” ultrasound. MIA2G “and” ultrasound had higher specificity but lower sensitivity than MIA2G or ultrasound alone. Similar results were seen for CT scan when evaluated with MIA2G. Conclusion: MIA2G and pelvic imaging are complementary tests and interpreting them together can provide important information about the malignant risk of an ovarian tumor. For physicians making decisions about a referral to a specialist, the combination of MIA2G “or” ultrasound has the highest sensitivity in predicting ovarian malignancy.

scholarly journals BARcode DEmixing through Non-negative Spatial Regression (BarDensr)

2020 ◽  
Author(s):  
Shuonan Chen ◽  
Jackson Loper ◽  
Xiaoyin Chen ◽  
Tony Zador ◽  
Liam Paninski
Keyword(s):  
Convex Optimization ◽  
Spatial Resolution ◽  
Spatial Regression ◽  
Real Data ◽  
Computational Method ◽  
Spatial Density ◽  
Signal Recovery ◽  
Optimization Approach ◽  
Imaging Data ◽  
Imaging Resolution

AbstractModern spatial transcriptomics methods can target thousands of different types of RNA transcripts in a single slice of tissue. Many biological applications demand a high spatial density of transcripts relative to the imaging resolution, leading to partial mixing of transcript rolonies in many pixels; unfortunately, current analysis methods do not perform robustly in this highly-mixed setting. Here we develop a new analysis approach, BARcode DEmixing through Non-negative Spatial Regression (BarDensr): we start with a generative model of the physical process that leads to the observed image data and then apply sparse convex optimization methods to estimate the underlying (demixed) rolony densities. We apply Bar-Densr to simulated and real data and find that it achieves state of the art signal recovery, particularly in densely-labeled regions or data with low spatial resolution. Finally, BarDensr is fast and parallelizable. We provide open-source code as well as an implementation for the ‘NeuroCAAS’ cloud platform.Author SummarySpatial transcriptomics technologies allow us to simultaneously detect multiple molecular targets in the context of intact tissues. These experiments yield images that answer two questions: which kinds of molecules are present, and where are they located in the tissue? In many experiments (e.g., mapping RNA expression in fine neuronal processes), it is desirable to increase the signal density relative to the imaging resolution. This may lead to mixing of signals from multiple RNA molecules into single imaging pixels; thus we need to demix the signals from these images. Here we introduce BarDensr, a new computational method to perform this demixing. The method is based on a forward model of the imaging process, followed by a convex optimization approach to approximately ‘invert’ mixing induced during imaging. This new approach leads to significantly improved performance in demixing imaging data with dense expression and/or low spatial resolution.

Algorithmic Analysis of Clinical, Neuropsychological, and Imaging Data in Localization-Related Epilepsy

2019 ◽  
pp. 46-79
Author(s):  
Masoud Latifinavid ◽  
Kost Elisevich ◽  
Hamid Soltanian-Zadeh
Keyword(s):  
Imaging Modality ◽  
Brain Structures ◽  
Anatomical Landmarks ◽  
Imaging Features ◽  
Imaging Data ◽  
Neuropsychological Profile ◽  
Algorithmic Analysis ◽  
Algorithmic Approaches ◽  
Physical Findings ◽  
Selection Of Patients

The current study examines algorithmic approaches for analysis of multimodal attributes in localization-related epilepsy (LRE), specifically, their impact on the selection of patients for surgical consideration. Invasive electrographic data is excluded here to concentrate upon the localized anatomical landmarks and identified/initialized brain structures in volumetric MR images as well as initial clinical presentation and the varied elements of the seizure history, ictal semiology, risk and seizure-precipitating factors and physical findings in addition to several features of the neuropsychological profile including various parameters of cognition and both speech and memory lateralization. First, the imaging modality data is excluded and just clinical, electrographic and neuropsychological data are investigated. Afterward, the imaging data are investigated and a comparison between the prediction results of the two types of data is done. In the case of using non-imaging multimodal data, 56% and using imaging features, about 71% of correct outcome prediction was obtained.

scholarly journals The interleaved partial active shape model search (IPASM) algorithm – Preliminary results of a novel approach towards 3D ultrasound-based bone surface reconstruction

10.29007/ckw2 ◽  
2018 ◽  
Author(s):  
Christoph Hänisch ◽  
Benjamin Hohlmann ◽  
Klaus Radermacher
Keyword(s):  
Search Algorithm ◽  
Imaging Modality ◽  
Statistical Shape Model ◽  
Training Data ◽  
Active Shape Model ◽  
Imaging Data ◽  
Shape Model ◽  
Underlying Distribution ◽  
Model Search ◽  
Active Shape

In applications such as biomechanical simulations or implant planning, bone surfaces of the knee are most often reconstructed from computed tomography or magnetic resonance imaging data. Here, ultrasound (US) might serve as an alternative imaging modality. However, established methods cannot directly be transferred to US due to differences in imaging quality and underlying physics.In this paper, we present a generalisation of the well-known active shape model search algorithm (ASM) that allows for segmenting various structures in US volume images that are too large to be captured with a single recording. The multi-view segmentation approach uses a-priori knowledge in the form of a statistical shape model (SSM) as is the case with the classical ASM. This allows to extrapolate missing information and to generate shapes that comply with the underlying distribution of some training data. The main differences are, however, that the SSM is not only adapted to a single image but to multiple images and that the adaption process is interleaved. As a result, within each iteration the surface information of all sub-volumes is propagated and used in all subsequent steps.In-silico tests were conducted to investigate how this algorithm would perform in real tracked US data. US volume images were split in slightly overlapping sub-volumes, noise was added, and the alignment was distorted. We could show that the algorithm is capable of reconstructing shapes in the lower millimetre range and for some cases even with submillimetric accuracy. The algorithm is hardly affected by orientation errors below 5 degrees and displacement errors below 5 mm; above these limits, the average absolute SDE as well as its associated variance increases.

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