Abstract
Objectives: The objective of this study was to develop a computer-aided detection system for automated brain metastases detection using magnetic resonance black-blood imaging and compare its applicability with conventional magnetization-prepared rapid gradient echo (MP-RAGE) imaging. Materials and Methods: Twenty-six patients with brain metastases were imaged with a contrast-enhanced, 3-dimensional, whole-brain magnetic resonance black-blood pulse sequence. Approval from the institutional review board and informed consent from the patients were obtained. Preprocessing steps included B1 inhomogeneity correction and brain extraction. The computer-aided detection system used 3-dimensional template matching, which measured normalized cross-correlation coefficient to generate possible metastases candidates. An artificial neural network was used for classification after various volume features were extracted. The same detection procedure was tested with contrast-enhanced MP-RAGE, which was also acquired from the same patients. Results: The performance of the proposed detection method was measured by the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity values. In the black-blood case, detection process displayed an AUROC of 0.9355, a sensitivity value of 81.1%, and a specificity value of 98.2%. Magnetization-prepared rapid gradient echo data showed an AUROC of 0.6508, a sensitivity value of 30.2%, and a specificity value of 99.97%. Conclusions: 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.
| Original language | English |
|---|---|
| Pages (from-to) | 113-119 |
| Number of pages | 7 |
| Journal | Investigative Radiology |
| Volume | 48 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2013 Feb 1 |
Fingerprint
Keywords
- artificial neural network
- black-blood imaging
- brain metastases
- computer-aided detection
- MP-RAGE
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
Cite this
Computer-aided detection of metastatic brain tumors using magnetic resonance black-blood imaging. / Yang, Seungwook; Nam, Yoonho; Kim, Min Oh; Kim, Eung Yeop; Park, Jaeseok; Kim, Dong Hyun.
In: Investigative Radiology, Vol. 48, No. 2, 01.02.2013, p. 113-119.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Computer-aided detection of metastatic brain tumors using magnetic resonance black-blood imaging
AU - Yang, Seungwook
AU - Nam, Yoonho
AU - Kim, Min Oh
AU - Kim, Eung Yeop
AU - Park, Jaeseok
AU - Kim, Dong Hyun
PY - 2013/2/1
Y1 - 2013/2/1
N2 - Objectives: The objective of this study was to develop a computer-aided detection system for automated brain metastases detection using magnetic resonance black-blood imaging and compare its applicability with conventional magnetization-prepared rapid gradient echo (MP-RAGE) imaging. Materials and Methods: Twenty-six patients with brain metastases were imaged with a contrast-enhanced, 3-dimensional, whole-brain magnetic resonance black-blood pulse sequence. Approval from the institutional review board and informed consent from the patients were obtained. Preprocessing steps included B1 inhomogeneity correction and brain extraction. The computer-aided detection system used 3-dimensional template matching, which measured normalized cross-correlation coefficient to generate possible metastases candidates. An artificial neural network was used for classification after various volume features were extracted. The same detection procedure was tested with contrast-enhanced MP-RAGE, which was also acquired from the same patients. Results: The performance of the proposed detection method was measured by the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity values. In the black-blood case, detection process displayed an AUROC of 0.9355, a sensitivity value of 81.1%, and a specificity value of 98.2%. Magnetization-prepared rapid gradient echo data showed an AUROC of 0.6508, a sensitivity value of 30.2%, and a specificity value of 99.97%. Conclusions: 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.
AB - Objectives: The objective of this study was to develop a computer-aided detection system for automated brain metastases detection using magnetic resonance black-blood imaging and compare its applicability with conventional magnetization-prepared rapid gradient echo (MP-RAGE) imaging. Materials and Methods: Twenty-six patients with brain metastases were imaged with a contrast-enhanced, 3-dimensional, whole-brain magnetic resonance black-blood pulse sequence. Approval from the institutional review board and informed consent from the patients were obtained. Preprocessing steps included B1 inhomogeneity correction and brain extraction. The computer-aided detection system used 3-dimensional template matching, which measured normalized cross-correlation coefficient to generate possible metastases candidates. An artificial neural network was used for classification after various volume features were extracted. The same detection procedure was tested with contrast-enhanced MP-RAGE, which was also acquired from the same patients. Results: The performance of the proposed detection method was measured by the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity values. In the black-blood case, detection process displayed an AUROC of 0.9355, a sensitivity value of 81.1%, and a specificity value of 98.2%. Magnetization-prepared rapid gradient echo data showed an AUROC of 0.6508, a sensitivity value of 30.2%, and a specificity value of 99.97%. Conclusions: 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.
KW - artificial neural network
KW - black-blood imaging
KW - brain metastases
KW - computer-aided detection
KW - MP-RAGE
UR - http://www.scopus.com/inward/record.url?scp=84872361694&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872361694&partnerID=8YFLogxK
U2 - 10.1097/RLI.0b013e318277f078
DO - 10.1097/RLI.0b013e318277f078
M3 - Article
VL - 48
SP - 113
EP - 119
JO - Investigative Radiology
JF - Investigative Radiology
SN - 0020-9996
IS - 2
ER -