Standardization of relative cerebral blood volume (rCBV) image maps for ease of both inter‐ and intrapatient comparisons

Bedekar, D.; Jensen, Todd R.; Schmainda, Kathleen M.

doi:10.1002/mrm.22445

Cited by 68 publications

(76 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Parameters used included the following: TE ranging from 23 to 50 ms, TR ranging from 1250 to 1400 ms, flip angles ranging from 30°to 35°, 40 -90 repetitions (temporal time points), section thickness ranging from 4 to 7 mm with intersection gap ranging from 0 to 1.5 mm, number of sections ranging from 6 to 20, and matrix size ranging from 80 ϫ 96 to 128 ϫ 128. Postprocessing data analysis was performed to determine standardized rCBV by using previously published methods 17 and commercially available software (IB Neuro v2.0; Imaging Biometrics, Elm Grove, Wisconsin). 18 F-FDOPA PET images were obtained for 7 patients as previously described.…”

Section: Mr Diffusion Image Acquisition and Postprocessing Image Analmentioning

confidence: 99%

Persistent Diffusion-Restricted Lesions in Bevacizumab-Treated Malignant Gliomas Are Associated with Improved Survival Compared with Matched Controls

Mong¹,

Ellingson²,

Nghiemphu³

et al. 2012

AJNR Am J Neuroradiol

View full text Add to dashboard Cite

show abstract

Section: Mr Diffusion Image Acquisition and Postprocessing Image Analmentioning

confidence: 99%

Persistent Diffusion-Restricted Lesions in Bevacizumab-Treated Malignant Gliomas Are Associated with Improved Survival Compared with Matched Controls

Mong¹,

Ellingson²,

Nghiemphu³

et al. 2012

AJNR Am J Neuroradiol

View full text Add to dashboard Cite

show abstract

“…[1,13,17,22]. Most existing perfusion MRI studies considered only the CBV of the perfusion parameters for tumor grading.…”

Section: Related Workmentioning

confidence: 99%

“…Many studies have extracted single features of the structural tumor images like location, volume, size, shape, etc. in order to find relevant features for tumor grading [1,13,17,22]. Some techniques have been proposed trying to classify tumors by considering spatial information of three dimensional tumor Regions Of Interest (ROI) [21,26].…”

Section: Introductionmentioning

confidence: 99%

“…Several research groups have demonstrated that perfusion MRI can be used to distinguish between different tumor grades [1,5,15,17,27] mainly differentiating between grade II and a combined group of grade III and IV brain tumors. It has been shown that Cerebral Blood Volume reliably correlates with tumor grade and histological findings of increased tumor vascularity [1,5,25,32,34].…”

Section: Introductionmentioning

confidence: 99%

“…It has been shown that Cerebral Blood Volume reliably correlates with tumor grade and histological findings of increased tumor vascularity [1,5,25,32,34]. Nevertheless, until now the non-invasive grading of low-grade versus anaplastic glioma has remained very difficult.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A novel tumor grading technique using functional magnetic resonance imaging

Haegler¹,

Böhm²

2011

Proceedings of the 2011 Workshop on Data Mining for Medicine and Healthcare

View full text Add to dashboard Cite

Adequate therapy planning of gliomas needs histological determination of invasive biopsy due to the fact that both treatment and prognosis of glial neoplasms vary strongly depending on their histological grading. Magnetic resonance imaging based glioma grading is currently mainly based on contrast-enhanced T1-weighted images. To additionally gain information on tumor physiology for glioma grading functional MRI techniques like perfusion MR have also been considered. Here, we present a novel technique for glioma grading, which is based on a similarity search for uncertain data. In order to perform the search as accurate as possible we used four different features of the tumors as input for the similarity search: the Cerebral Blood Volume, the Cerebral Blood Flow, the Mean Transit Time, and the post-contrast T1-weighted image. For each patient a tumor feature vector was defined by a four-dimensional Probability Density Function, more precisely a Gaussian Mixture Model (GMM). In contrast to existing similarity searches we also considered correlations between different features in the similarity search. Here, we present the entire workflow from image acquisition to tumor grade prediction providing a probabilitybased classifier for gliomas. We applied our approach to MRI data sets of glioma patients, which were preprocessed and converted to four-dimensional GMMs, and achieved an accuracy, sensitivity, and specificity of 83.8 %, 78.6 %, and 87.0 % while grading based solely on contrast-enhancement could only achieve an accuracy, sensitivity, and specificity of 64.9 %, 52.4 %, and 81.3 %, respectively. Hence, our proposed similarity search based grading technique seems to be of great value for supporting non-invasive tumor grading since it integrates the information of different MRI sequences including perfusion maps in one semi-automatic analysis.

show abstract

Comparison between intensity normalization techniques for dynamic susceptibility contrast (DSC)‐MRI estimates of cerebral blood volume (CBV) in human gliomas

Ellingson

Zaw

Cloughesy

et al. 2012

Magnetic Resonance Imaging

View full text Add to dashboard Cite

Purpose: To compare ''standardization,'' ''Gaussian normalization,'' and ''Z-score normalization'' intensity transformation techniques in dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) estimates of cerebral blood volume (CBV) in human gliomas. DSC-MRI is a well-established biomarker for CBV in brain tumors; however, DSC-MRI estimates of CBV are semiquantitative. The use of image intensity transformation algorithms provides a mechanism for obtaining quantitatively similar CBV maps with the same intensity scaling. Materials and Methods:The coefficient of variance (CV) in normal-appearing white matter and relative contrast between tumor regions and normal tissue was compared between the three CBV transformations across five different MR scanners in 96 patients with gliomas. Results:The results suggest all normalization techniques improved variability and relative tumor contrast of CBV measurements compared with nonnormalized CBV maps. The results suggest Gaussian normalization of CBV maps provided slightly lower CV in normal white matter and provided slightly higher tumor contrast for glioblastomas (WHO grade IV) compared with other techniques. Conclusion:The results suggest Gaussian normalization of leakage-corrected CBV maps may be the best choice for image intensity correction for use in large-scale, multicenter clinical trials where MR scanners and protocols vary widely due to ease of implementation, lowest variability, and highest tumor to normal tissue contrast.

show abstract

Standardization of relative cerebral blood volume (rCBV) image maps for ease of both inter‐ and intrapatient comparisons

Cited by 68 publications

References 16 publications

Persistent Diffusion-Restricted Lesions in Bevacizumab-Treated Malignant Gliomas Are Associated with Improved Survival Compared with Matched Controls

Persistent Diffusion-Restricted Lesions in Bevacizumab-Treated Malignant Gliomas Are Associated with Improved Survival Compared with Matched Controls

A novel tumor grading technique using functional magnetic resonance imaging

Comparison between intensity normalization techniques for dynamic susceptibility contrast (DSC)‐MRI estimates of cerebral blood volume (CBV) in human gliomas

Contact Info

Product

Resources

About