Toward Quantitative Imaging of Soot in an Explosively Generated Fireball

2022 ◽  
Author(s):  
Ashley J. Saltzman ◽  
Daniel R. Guildenbecher ◽  
Sean P. Kearney ◽  
Julien Manin ◽  
Lyle Pickett
Keyword(s):  
Quantitative Imaging

Fluorescent potentiometric dyes for membrane-potential imaging

1994 ◽  
Vol 52 ◽  
pp. 166-167
Author(s):  
Leslie M. Loew
Keyword(s):  
Membrane Potential ◽  
Single Cells ◽  
Quantitative Imaging ◽  
Optical Recording ◽  
Biological Processes ◽  
Major Focus ◽  
The Past ◽  
Excitable Systems ◽  
Major Application ◽  
The Impact

A major application of potentiometric dyes has been the multisite optical recording of electrical activity in excitable systems. After being championed by L.B. Cohen and his colleagues for the past 20 years, the impact of this technology is rapidly being felt and is spreading to an increasing number of neuroscience laboratories. A second class of experiments involves using dyes to image membrane potential distributions in single cells by digital imaging microscopy - a major focus of this lab. These studies usually do not require the temporal resolution of multisite optical recording, being primarily focussed on slow cell biological processes, and therefore can achieve much higher spatial resolution. We have developed 2 methods for quantitative imaging of membrane potential. One method uses dual wavelength imaging of membrane-staining dyes and the other uses quantitative 3D imaging of a fluorescent lipophilic cation; the dyes used in each case were synthesized for this purpose in this laboratory.

Do we need Quantitative Imaging?

2020 ◽  
Vol 59 (03) ◽  
pp. 225-226 ◽  
Author(s):  
Janet F. Eary ◽  
Winfried Brenner
Keyword(s):  
Quantitative Imaging

Semiconductor Quantum Dots for Multicolor Fluorescence Imaging and Spectroscopy of Single Cancer Cells

2003 ◽  
Vol 773 ◽  
Author(s):  
Xiaohu Gao ◽  
Shuming Nie ◽  
Wallace H. Coulter
Keyword(s):  
Quantum Dots ◽  
Cancer Cells ◽  
Fluorescence Imaging ◽  
Organic Dyes ◽  
Fluorescent Proteins ◽  
Single Cells ◽  
Quantitative Imaging ◽  
Semiconductor Quantum Dots ◽  
Single Cancer ◽  
Imaging And Spectroscopy

AbstractLuminescent quantum dots (QDs) are emerging as a new class of biological labels with unique properties and applications that are not available from traditional organic dyes and fluorescent proteins. Here we report new developments in using semiconductor quantum dots for quantitative imaging and spectroscopy of single cancer cells. We show that both live and fixed cells can be labeled with multicolor QDs, and that single cells can be analyzed by fluorescence imaging and wavelength-resolved spectroscopy. These results raise new possibilities in cancer imaging, molecular profiling, and disease staging.

scholarly journals Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically driven quantitative biomarkers

2021 ◽  
Author(s):  
Laure Fournier ◽  
Lena Costaridou ◽  
Luc Bidaut ◽  
Nicolas Michoux ◽  
Frederic E. Lecouvet ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Quantitative Imaging ◽  
A Priori ◽  
External Validation ◽  
Data Driven ◽  
Imaging Biomarkers ◽  
Clinical Validation ◽  
Biological Processes ◽  
Imaging Data ◽  
Selection Of

Abstract Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Key Points • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.

scholarly journals Quantifying the Response of Grass Carp Larvae to Acoustic Stimuli Using Particle-Tracking Velocimetry

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 603
Author(s):  
Hojung You ◽  
Rafael O. Tinoco
Keyword(s):  
Particle Tracking ◽  
Grass Carp ◽  
Particle Tracking Velocimetry ◽  
Quantitative Imaging ◽  
Functional Relation ◽  
Acoustic Signals ◽  
Ecological Impacts ◽  
Ctenopharyngodon Idella ◽  
Response Ratio ◽  
Larval Behavior

Acoustic deterrents are recognized as a promising method to prevent the spread of invasive grass carp, Ctenopharyngodon idella (Valenciennes, 1844) and the negative ecological impacts caused by them. As the efficacy of sound barriers depends on the hearing capabilities of carp, it is important to identify whether carps can recognize acoustic signals and alter their swimming behavior. Our study focuses on quantifying the response of grass carp larvae when exposed to out-of-water acoustic signals within the range of 100–1000 Hz, by capturing their movement using particle-tracking velocimetry (PTV), a quantitative imaging tool often used for hydrodynamic studies. The number of responsive larvae is counted to compute response ratio at each frequency, to quantify the influence of sound on larval behavior. While the highest response occurred at 700 Hz, we did not observe any clear functional relation between frequency of sound and response ratio. Overall, 20–30% of larvae were consistently reacting to sound stimuli regardless of the frequency. In this study, we emphasize that larval behaviors when exposed to acoustic signals vary by individual, and thus a sufficient number of larvae should be surveyed at the same time under identical conditions, to better quantify their sensitivity to sound rather than repeating the experiment with individual specimens. Since bulk quantification, such as mean or quantile velocities of multiple specimens, can misrepresent larval behavior, our study finds that including the response ratio can more effectively reflect the larval response.

scholarly journals The Primacy of High B-Value 3T-DWI Radiomics in the Prediction of Clinically Significant Prostate Cancer

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 739
Author(s):  
Alessandro Bevilacqua ◽  
Margherita Mottola ◽  
Fabio Ferroni ◽  
Alice Rossi ◽  
Giampaolo Gavelli ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Quantitative Imaging ◽  
B Value ◽  
Support Vector ◽  
Svm Classifier ◽  
Primary Role ◽  
High B Value ◽  
Wilcoxon Rank Sum Test ◽  
Apparent Diffusion ◽  
Clinically Significant

Predicting clinically significant prostate cancer (csPCa) is crucial in PCa management. 3T-magnetic resonance (MR) systems may have a novel role in quantitative imaging and early csPCa prediction, accordingly. In this study, we develop a radiomic model for predicting csPCa based solely on native b2000 diffusion weighted imaging (DWIb2000) and debate the effectiveness of apparent diffusion coefficient (ADC) in the same task. In total, 105 patients were retrospectively enrolled between January–November 2020, with confirmed csPCa or ncsPCa based on biopsy. DWIb2000 and ADC images acquired with a 3T-MRI were analyzed by computing 84 local first-order radiomic features (RFs). Two predictive models were built based on DWIb2000 and ADC, separately. Relevant RFs were selected through LASSO, a support vector machine (SVM) classifier was trained using repeated 3-fold cross validation (CV) and validated on a holdout set. The SVM models rely on a single couple of uncorrelated RFs (ρ < 0.15) selected through Wilcoxon rank-sum test (p ≤ 0.05) with Holm–Bonferroni correction. On the holdout set, while the ADC model yielded AUC = 0.76 (95% CI, 0.63–0.96), the DWIb2000 model reached AUC = 0.84 (95% CI, 0.63–0.90), with specificity = 75%, sensitivity = 90%, and informedness = 0.65. This study establishes the primary role of 3T-DWIb2000 in PCa quantitative analyses, whilst ADC can remain the leading sequence for detection.

scholarly journals Optimization of SPECT/CT imaging protocols for quantitative and qualitative 99mTc SPECT

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Dennis Kupitz ◽  
Heiko Wissel ◽  
Jan Wuestemann ◽  
Stephanie Bluemel ◽  
Maciej Pech ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Quantitative Imaging ◽  
Scatter Correction ◽  
Reconstruction Algorithm ◽  
Weighting Factor ◽  
Quantitative Spect ◽  
Pet Ct ◽  
Spect Acquisition ◽  
The Mean ◽  
A Minor

Abstract Background The introduction of hybrid SPECT/CT devices enables quantitative imaging in SPECT, providing a methodological setup for quantitation using SPECT tracers comparable to PET/CT. We evaluated a specific quantitative reconstruction algorithm for SPECT data using a 99mTc-filled NEMA phantom. Quantitative and qualitative image parameters were evaluated for different parametrizations of the acquisition and reconstruction protocol to identify an optimized quantitative protocol. Results The reconstructed activity concentration (ACrec) and the signal-to-noise ratio (SNR) of all examined protocols (n = 16) were significantly affected by the parametrization of the weighting factor k used in scatter correction, the total number of iterations and the sphere volume (all, p < 0.0001). The two examined SPECT acquisition protocols (with 60 or 120 projections) had a minor impact on the ACrec and no significant impact on the SNR. In comparison to the known AC, the use of default scatter correction (k = 0.47) or object-specific scatter correction (k = 0.18) resulted in an underestimation of ACrec in the largest sphere volume (26.5 ml) by − 13.9 kBq/ml (− 16.3%) and − 7.1 kBq/ml (− 8.4%), respectively. An increase in total iterations leads to an increase in estimated AC and a decrease in SNR. The mean difference between ACrec and known AC decreased with an increasing number of total iterations (e.g., for 20 iterations (2 iterations/10 subsets) = − 14.6 kBq/ml (− 17.1%), 240 iterations (24i/10s) = − 8.0 kBq/ml (− 9.4%), p < 0.0001). In parallel, the mean SNR decreased significantly from 2i/10s to 24i/10s by 76% (p < 0.0001). Conclusion Quantitative SPECT imaging is feasible with the used reconstruction algorithm and hybrid SPECT/CT, and its consistent implementation in diagnostics may provide perspectives for quantification in routine clinical practice (e.g., assessment of bone metabolism). When combining quantitative analysis and diagnostic imaging, we recommend using two different reconstruction protocols with task-specific optimized setups (quantitative vs. qualitative reconstruction). Furthermore, individual scatter correction significantly improves both quantitative and qualitative results.

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