Dynamic Susceptibility of a Magnetic Impurity

1984 ◽  
pp. 297-303
Author(s):  
Pedro Schlottmann
Keyword(s):  
Magnetic Impurity ◽  
Dynamic Susceptibility

scholarly journals Differentiating Glioblastomas from Solitary Brain Metastases: An Update on the Current Literature of Advanced Imaging Modalities

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2960
Author(s):  
Austin-John Fordham ◽  
Caitlin-Craft Hacherl ◽  
Neal Patel ◽  
Keri Jones ◽  
Brandon Myers ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Brain Metastases ◽  
Clinical Assessment ◽  
Volume Fraction ◽  
Imaging Modalities ◽  
Current Evidence ◽  
Dynamic Susceptibility ◽  
Advanced Imaging ◽  
Imaging Protocol ◽  
Solitary Brain Metastases

Differentiating between glioblastomas and solitary brain metastases proves to be a challenging diagnosis for neuroradiologists, as both present with imaging patterns consisting of peritumoral hyperintensities with similar intratumoral texture on traditional magnetic resonance imaging sequences. Early diagnosis is paramount, as each pathology has completely different methods of clinical assessment. In the past decade, recent developments in advanced imaging modalities enabled providers to acquire a more accurate diagnosis earlier in the patient’s clinical assessment, thus optimizing clinical outcome. Dynamic susceptibility contrast has been optimized for detecting relative cerebral blood flow and relative cerebral blood volume. Diffusion tensor imaging can be used to detect changes in mean diffusivity. Neurite orientation dispersion and density imaging is an innovative modality detecting changes in intracellular volume fraction, isotropic volume fraction, and extracellular volume fraction. Magnetic resonance spectroscopy is able to assist by providing a metabolic descriptor while detecting variable ratios of choline/N-acetylaspartate, choline/creatine, and N-acetylaspartate/creatine. Finally, radiomics and machine learning algorithms have been devised to assist in improving diagnostic accuracy while often utilizing more than one advanced imaging protocol per patient. In this review, we provide an update on all the current evidence regarding the identification and differentiation of glioblastomas from solitary brain metastases.

scholarly journals Correction to: Association of dynamic susceptibility magnetic resonance imaging at initial tumor diagnosis with the prognosis of different molecular glioma subtypes

2021 ◽  
Author(s):  
Cornelia Brendle ◽  
Uwe Klose ◽  
Johann-Martin Hempel ◽  
Jens Schittenhelm ◽  
Marco Skardelly ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Tumor Diagnosis ◽  
Dynamic Susceptibility ◽  
Resonance Imaging ◽  
Link Type ◽  
Initial Tumor

A Correction to this paper has been published: https://doi.org/10.1007/s10072-021-05352-6

scholarly journals Combined Perfusion and Permeability Imaging Reveals Different Pathophysiologic Tissue Responses After Successful Thrombectomy

2021 ◽  
Author(s):  
Arne Potreck ◽  
Matthias A. Mutke ◽  
Charlotte S. Weyland ◽  
Johannes A. R. Pfaff ◽  
Peter A. Ringleb ◽  
...  
Keyword(s):  
Clinical Outcome ◽  
Blood Brain Barrier ◽  
Hemorrhagic Transformation ◽  
Brain Barrier ◽  
Dynamic Susceptibility ◽  
Blood Brain Barrier Disruption ◽  
Flow Maps ◽  
Blood Brain ◽  
Patients At Risk ◽  
Brain Barrier Disruption

AbstractDespite successful recanalization of large-vessel occlusions in acute ischemic stroke, individual patients profit to a varying degree. Dynamic susceptibility-weighted perfusion and dynamic T1-weighted contrast-enhanced blood-brain barrier permeability imaging may help to determine secondary stroke injury and predict clinical outcome. We prospectively performed perfusion and permeability imaging in 38 patients within 24 h after successful mechanical thrombectomy of an occlusion of the middle cerebral artery M1 segment. Perfusion alterations were evaluated on cerebral blood flow maps, blood-brain barrier disruption (BBBD) visually and quantitatively on ktrans maps and hemorrhagic transformation on susceptibility-weighted images. Visual BBBD within the DWI lesion corresponded to a median ktrans elevation (IQR) of 0.77 (0.41–1.4) min−1 and was found in all 7 cases of hypoperfusion (100%), in 10 of 16 cases of hyperperfusion (63%), and in only three of 13 cases with unaffected perfusion (23%). BBBD was significantly associated with hemorrhagic transformation (p < 0.001). While BBBD alone was not a predictor of clinical outcome at 3 months (positive predictive value (PPV) = 0.8 [0.56–0.94]), hypoperfusion occurred more often in patients with unfavorable clinical outcome (PPV = 0.43 [0.10–0.82]) compared to hyperperfusion (PPV = 0.93 [0.68–1.0]) or unaffected perfusion (PPV = 1.0 [0.75–1.0]). We show that combined perfusion and permeability imaging reveals distinct infarct signatures after recanalization, indicating the severity of prior ischemic damage. It assists in predicting clinical outcome and may identify patients at risk of stroke progression.

scholarly journals Fully automated analysis combining [18F]-FET-PET and multiparametric MRI including DSC perfusion and APTw imaging: a promising tool for objective evaluation of glioma progression

2021 ◽  
Author(s):  
K. J. Paprottka ◽  
S. Kleiner ◽  
C. Preibisch ◽  
F. Kofler ◽  
F. Schmidt-Graf ◽  
...  
Keyword(s):  
Random Forest ◽  
Tumor Progression ◽  
Automated Analysis ◽  
Random Forest Classifier ◽  
Imaging Modalities ◽  
Amide Proton ◽  
Tumor Board ◽  
Dynamic Susceptibility ◽  
Fet Pet ◽  
Promising Tool

Abstract Purpose To evaluate diagnostic accuracy of fully automated analysis of multimodal imaging data using [18F]-FET-PET and MRI (including amide proton transfer-weighted (APTw) imaging and dynamic-susceptibility-contrast (DSC) perfusion) in differentiation of tumor progression from treatment-related changes in patients with glioma. Material and methods At suspected tumor progression, MRI and [18F]-FET-PET data as part of a retrospective analysis of an observational cohort of 66 patients/74 scans (51 glioblastoma and 23 lower-grade-glioma, 8 patients included at two different time points) were automatically segmented into necrosis, FLAIR-hyperintense, and contrast-enhancing areas using an ensemble of deep learning algorithms. In parallel, previous MR exam was processed in a similar way to subtract preexisting tumor areas and focus on progressive tumor only. Within these progressive areas, intensity statistics were automatically extracted from [18F]-FET-PET, APTw, and DSC-derived cerebral-blood-volume (CBV) maps and used to train a Random Forest classifier with threefold cross-validation. To evaluate contribution of the imaging modalities to the classifier’s performance, impurity-based importance measures were collected. Classifier performance was compared with radiology reports and interdisciplinary tumor board assessments. Results In 57/74 cases (77%), tumor progression was confirmed histopathologically (39 cases) or via follow-up imaging (18 cases), while remaining 17 cases were diagnosed as treatment-related changes. The classification accuracy of the Random Forest classifier was 0.86, 95% CI 0.77–0.93 (sensitivity 0.91, 95% CI 0.81–0.97; specificity 0.71, 95% CI 0.44–0.9), significantly above the no-information rate of 0.77 (p = 0.03), and higher compared to an accuracy of 0.82 for MRI (95% CI 0.72–0.9), 0.81 for [18F]-FET-PET (95% CI 0.7–0.89), and 0.81 for expert consensus (95% CI 0.7–0.89), although these differences were not statistically significant (p > 0.1 for all comparisons, McNemar test). [18F]-FET-PET hot-spot volume was single-most important variable, with relevant contribution from all imaging modalities. Conclusion Automated, joint image analysis of [18F]-FET-PET and advanced MR imaging techniques APTw and DSC perfusion is a promising tool for objective response assessment in gliomas.

scholarly journals Fast whole brain MR imaging of dynamic susceptibility contrast changes in the cerebrospinal fluid (cDSC MRI)

2020 ◽  
Vol 84 (6) ◽  
pp. 3256-3270 ◽  
Author(s):  
Di Cao ◽  
Ningdong Kang ◽  
Jay J. Pillai ◽  
Xinyuan Miao ◽  
Adrian Paez ◽  
...  
Keyword(s):  
Cerebrospinal Fluid ◽  
Mr Imaging ◽  
Dynamic Susceptibility ◽  
Dynamic Susceptibility Contrast ◽  
Whole Brain ◽  
Susceptibility Contrast
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