scholarly journals Multisite Concordance of DSC-MRI Analysis for Brain Tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project

2018 ◽  
Vol 39 (6) ◽  
pp. 1008-1016 ◽  
Author(s):  
K.M. Schmainda ◽  
M.A. Prah ◽  
S.D. Rand ◽  
Y. Liu ◽  
B. Logan ◽  
...  
Keyword(s):  
Brain Tumors ◽  
National Cancer Institute ◽  
Quantitative Imaging ◽  
Collaborative Project ◽  
Dsc Mri

scholarly journals Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors

Cancers ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 424
Author(s):  
Francesco Sanvito ◽  
Antonella Castellano ◽  
Andrea Falini
Keyword(s):  
Brain Tumors ◽  
Quantitative Imaging ◽  
Special Focus ◽  
Lower Grade ◽  
Primary Brain Tumors ◽  
Molecular Features ◽  
Magnetic Resonance Imaging Mri ◽  
High Field Imaging ◽  
Noninvasive Biomarkers

In recent years, the clinical assessment of primary brain tumors has been increasingly dependent on advanced magnetic resonance imaging (MRI) techniques in order to infer tumor pathophysiological characteristics, such as hemodynamics, metabolism, and microstructure. Quantitative radiomic data extracted from advanced MRI have risen as potential in vivo noninvasive biomarkers for predicting tumor grades and molecular subtypes, opening the era of “molecular imaging” and radiogenomics. This review presents the most relevant advancements in quantitative neuroimaging of advanced MRI techniques, by means of radiomics analysis, applied to primary brain tumors, including lower-grade glioma and glioblastoma, with a special focus on peculiar oncologic entities of current interest. Novel findings from diffusion MRI (dMRI), perfusion-weighted imaging (PWI), and MR spectroscopy (MRS) are hereby sifted in order to evaluate the role of quantitative imaging in neuro-oncology as a tool for predicting molecular profiles, stratifying prognosis, and characterizing tumor tissue microenvironments. Furthermore, innovative technological approaches are briefly addressed, including artificial intelligence contributions and ultra-high-field imaging new techniques. Lastly, after providing an overview of the advancements, we illustrate current clinical applications and future perspectives.

BIOM-44. PRE-SURGICAL ADVANCED MRI IS USEFUL FOR FORECASTING DRUG DISTRIBUTION IN BRAIN TUMORS

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi20-vi21
Author(s):  
Pamela Jackson ◽  
Minjee Kim ◽  
Andrea Hawkins-Daarud ◽  
Kyle Singleton ◽  
Afroz Mohammad ◽  
...  
Keyword(s):  
Brain Tumors ◽  
Diffusion Tensor ◽  
Quantitative Imaging ◽  
Drug Distribution ◽  
Non Invasive ◽  
Promising Tool ◽  
Contrast Enhanced ◽  
Multiple Blood ◽  
Mri Sequences ◽  
Magnetic Resonance Imaging Mri

Abstract Choosing effective chemotherapies for intravenous delivery to brain tumors is challenging, especially given the protective nature of the blood brain barrier (BBB). Connecting drug distribution to non-invasive, pre-surgical magnetic resonance imaging (MRI) could allow for predictive insight into drug distribution. In a previous study, we found that T2Gd images were predictive of a low BBB penetrant drug (Cefazolin), and FLAIR images were predictive of a high BBB penetrant drug (Levetiracetam). While these results are promising, we further seek to explore how advanced MRI sequences might inform image-based models of drug distribution. Prior to surgery, we acquired advanced dynamic contrast enhanced (DCE) and diffusion weighted imaging (DWI) MRI sequences for eight brain tumor patients (7 gliomas and 1 metastatic adenocarcinoma) in addition to the anatomic MRIs. All resulting quantitative maps and acquired images were co-registered. Prior to incision, patients received injections of cefazolin and levetiracetam. Next, multiple blood samples and biopsies were collected during surgery. Biopsies and plasma samples were analyzed for drug concentration using liquid chromatography mass spectrometry (LCMS), and biopsy drug levels were reported as Brain-Plasma Ratio (BPR). Mean image intensity was extracted from a 15x15 voxel window surrounding the biopsy location. We performed linear regression analyses to determine which combination of images were predictive of BPR. We found that considering quantitative imaging improved our initial ability to predict BPR for both drugs. For cefazolin, the third diffusion tensor eigenvalue (L3) map was significantly correlated with BPR (p< 0.001, R2= 0.36). For levetiracetam, the best model consisted of a combination of images and maps with the L3 map and the isotropic diffusion map (P) being the most influential (p= 0.001, R2= 0.63). Advanced MRI-based modeling is a promising tool for forecasting drug distribution in brain tumors and could be of great importance for understanding efficacy and selecting therapeutic strategies.

scholarly journals An Efficient Framework for Accurate Arterial Input Selection in DSC-MRI of Glioma Brain Tumors

2019 ◽  
Vol 16 (Special Issue) ◽  
Author(s):  
Hossein Rahimzadeh ◽  
Salman Rezaie Molood ◽  
Anahita Fathi Kazerooni ◽  
Hamidreza Saligheh Rad
Keyword(s):  
Brain Tumors ◽  
Input Selection ◽  
Dsc Mri

Synthetic generation of DSC‐MRI‐derived relative CBV maps from DCE MRI of brain tumors

2020 ◽  
Vol 85 (1) ◽  
pp. 469-479 ◽  
Author(s):  
Jeremiah W. Sanders ◽  
Henry Szu‐Meng Chen ◽  
Jason M. Johnson ◽  
Donald F. Schomer ◽  
Jorge E. Jimenez ◽  
...  
Keyword(s):  
Brain Tumors ◽  
Dce Mri ◽  
Dsc Mri ◽  
Synthetic Generation

scholarly journals An Efficient Framework for Accurate Arterial Input Selection in DSC-MRI of Glioma Brain Tumors

2018 ◽  
Author(s):  
H Rahimzadeh ◽  
A Fathi Kazerooni ◽  
M R Deevband ◽  
H Saligheh Rad
Keyword(s):  
Brain Tumors ◽  
Arterial Input Function ◽  
Intraclass Correlation ◽  
Dynamic Susceptibility ◽  
Shape Parameters ◽  
Dynamic Susceptibility Contrast ◽  
Input Selection ◽  
Mean Values ◽  
Experienced Radiologist ◽  
Dsc Mri

Introduction: Automatic arterial input function (AIF) selection has an essential role in quantification of cerebral perfusion parameters. The purpose of this study is to develop an optimal automatic method for AIF determination in dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) of glioma brain tumors by using a new preprocessing method.Material and Methods: For this study, DSC-MR images of 43 patients with glioma brain tumors were retrieved retrospectively. Our proposed AIF selection framework consisted an effcient pre-processing step, through which non-arterial curves such as tumorous, tissue, noisy and partial-volume affected curves were excluded, followed by AIF selection through agglomerative hierarchical (AH) clustering method. The performance of automatic AIF clustering was compared with manual AIF selection performed by an experienced radiologist, based on curve shape parameters, i.e. maximum peak (MP), full-width-at-half-maximum (FWHM), M (=MP/ (TTP × FWHM)) and root mean square error (RMSE).Results: Mean values of AIFs shape parameters were compared with those derived from manually selected AIFs by two-tailed paired t-test. The results showed statistically insignificant differences in MP, FWHM, and M parameters and lower RMSE, approving the resemblance of the selected AIF with the gold standard. The intraclass correlation coefficient and coefficients of variation percent showed a better agreement between manual AIF and our proposed AIF selection than previously proposed methods.Conclusion: The results of current work suggest that by using efficient preprocessing steps, the accuracy of automatic AIF selection could be improved and this method appears promising for efficient and accurate clinical applications.

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.

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