Computer-Aided Detection of Metastatic Brain Tumors Using Magnetic Resonance Black-Blood Imaging

Yang, Sanghwa; Nam, Yoonho; Kim, Min Oh; Kim, Eung Yeop; Park, Jaeseok; Kim, Dong Hyun

doi:10.1097/rli.0b013e318277f078

Cited by 43 publications

(33 citation statements)

References 27 publications

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“…Recent studies compared 3D SE and 3D GRE images with 1-mmthick slices for detecting brain metastases [11][12][13][14][15]. However, there are currently no definitive guidelines state which image should be used to detect brain metastasis, and few the full text (one case report [23], one study with insufficient data [24], and seven studies that were not in the field of interest) [25][26][27][28][29][30][31]. Finally, eight eligible studies, which included a total sample size of 252 patients and 1413 brain metastases, were included in our meta-analysis [8][9][10][11][12][13][14][15].…”

Section: Discussionmentioning

confidence: 99%

The detectability of brain metastases using contrast-enhanced spin-echo or gradient-echo images: a systematic review and meta-analysis

et al. 2016

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This study aimed to compare the detectability of brain metastases using contrast-enhanced spin-echo (SE) and gradient-echo (GRE) T1-weighted images. The Ovid-MEDLINE and EMBASE databases were searched for studies on the detectability of brain metastases using contrast-enhanced SE or GRE images. The pooled proportions for the detectability of brain metastases were assessed using random-effects modeling. Heterogeneity among studies was determined using χ (2) statistics for the pooled estimates and the inconsistency index, I (2) . To overcome heterogeneity, subgroup analyses according to slice thickness and lesion size were performed. A total of eight eligible studies, which included a sample size of 252 patients and 1413 brain metastases, were included. The detectability of brain metastases using SE images (89.2 %) was higher than using GRE images (81.6 %; adjusted 84.0 %), but this difference was not statistically significant (p = 0.2385). In subgroup analysis of studies with 1-mm-thick slices and small metastases (<5 mm in diameter), 3-dimensional (3D) SE images demonstrated a higher detectability in comparison to 3D GRE images (93.7 % vs 73.1 % in 1-mm-thick slices; 89.5 % vs 59.4 % for small metastases) (p < 0.0001). Although both SE or GRE images are acceptable for detecting brain metastases, contrast-enhanced 3D SE images using 1-mm-thick slices are preferred for detecting brain metastases, especially small lesions (<5 mm in diameter).

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Section: Discussionmentioning

confidence: 99%

The detectability of brain metastases using contrast-enhanced spin-echo or gradient-echo images: a systematic review and meta-analysis

et al. 2016

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“…The addition of these algorithms to human interpretation could potentially lead to greater sensitivity and improved accuracy of intracranial metastases characterization, based both on standard postcontrast exams as well as advanced physiologic imaging such as perfusion-related acquisitions (Ambrosini et al, 2010; Yang et al, 2013; Szwarc et al, 2015). …”

Section: Enhancing Detection Of Small Metastasesmentioning

confidence: 99%

Brain metastases: neuroimaging

Pope

2018

Handbook of Clinical Neurology

154

130

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Magnetic resonance imaging (MRI) is the cornerstone for evaluating patients with brain masses such as primary and metastatic tumors. Important challenges in effectively detecting and diagnosing brain metastases and in accurately characterizing their subsequent response to treatment remain. These difficulties include discriminating metastases from potential mimics such as primary brain tumors and infection, detecting small metastases, and differentiating treatment response from tumor recurrence and progression. Optimal patient management could be benefited by improved and well-validated prognostic and predictive imaging markers, as well as early response markers to identify successful treatment prior to changes in tumor size. To address these fundamental needs, newer MRI techniques including diffusion and perfusion imaging, MR spectroscopy, and positron emission tomography (PET) tracers beyond traditionally used 18-fluorodeoxyglucose are the subject of extensive ongoing investigations, with several promising avenues of added value already identified. These newer techniques provide a wealth of physiologic and metabolic information that may supplement standard MR evaluation, by providing the ability to monitor and characterize cellularity, angiogenesis, perfusion, pH, hypoxia, metabolite concentrations, and other critical features of malignancy. This chapter reviews standard and advanced imaging of brain metastases provided by computed tomography, MRI, and amino acid PET, focusing on potential biomarkers that can serve as problem-solving tools in the clinical management of patients with brain metastases.

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“…Later, Farjam et al [18] used 3D post-Gd-DTPA T1 volumes to localize hyperintense metastatic lesions with a 3D sphericalshell template. In a different study, Yang et al [19] considered contrast-enhanced MRI black-blood pulse sequence for metastatic lesion detection using 3D template. Wang et al [20] performed another study with an adaptive template using NCCC as similarity measure for tumor detection using T1 MR images.…”

Section: Introductionmentioning

confidence: 99%

Radius-Optimized Efficient Template Matching for Lesion Detection from Brain Images

Koley

Dutta

Aganj

2020

Preprint

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Computer-aided detection of brain lesions from volumetric magnetic resonance imaging (MRI) is in demand for fast and automatic diagnosis of neural diseases. The templatematching technique can provide satisfactory outcome for automatic localization of brain lesions; however, finding the optimal template size that maximizes similarity of the template and the lesion remains challenging. This increases the complexity of the algorithm and the requirement for computational resources, while processing large MRI volumes with three-dimensional (3D) templates. Hence, reducing the computational complexity of template matching is needed. In this paper, we first propose a mathematical framework for computing the normalized crosscorrelation coefficient (NCCC) as the similarity measure between the MRI volume and approximated 3D Gaussian template with linear time complexity, , as opposed to the conventional fast Fourier transform (FFT) based approach with the complexity , where is the number of voxels in the image and is the number of tried template radii. We then propose a mathematical formulation to analytically estimate the optimal template radius for each voxel in the image and compute the NCCC with the location-dependent optimal radius, reducing the complexity to . We test our methods on one synthetic and two real multiple-sclerosis databases, and compare its performance in lesion detection with FFT and a state-of-the-art lesion prediction algorithm. We demonstrate through our experiments the efficiency of the proposed method for brain lesion detection and its comparable performance with existing techniques.Index Terms-Brain lesion detection, computational complexity, FFT, MRI, NCCC, template matching.

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Computer-Aided Detection of Metastatic Brain Tumors Using Magnetic Resonance Black-Blood Imaging

Abstract: The results demonstrate that accurate automated detection of metastatic brain tumors using contrast-enhanced black-blood imaging sequence is possible compared with using conventional contrast-enhanced MP-RAGE sequence.

Cited by 43 publications

References 27 publications

The detectability of brain metastases using contrast-enhanced spin-echo or gradient-echo images: a systematic review and meta-analysis

The detectability of brain metastases using contrast-enhanced spin-echo or gradient-echo images: a systematic review and meta-analysis

Brain metastases: neuroimaging

Radius-Optimized Efficient Template Matching for Lesion Detection from Brain Images

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