Benjamin S Strnad1, Hilary L P Orlowski2, Matthew S Parsons2, Amber Salter3, Sonika Dahiya4, Aseem Sharma2. 1. Mallinckrodt Institute of Radiology, Washington University School of Medicine, Campus Box 8131, 510 S Kingshighway Blvd., St. Louis, MO, 63110, USA. strnadb@wustl.edu. 2. Mallinckrodt Institute of Radiology, Washington University School of Medicine, Campus Box 8131, 510 S Kingshighway Blvd., St. Louis, MO, 63110, USA. 3. Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA. 4. Division of Neuropathology,Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA.
Abstract
BACKGROUND: Mesial temporal sclerosis (MTS) is an important cause of intractable epilepsy. Early and accurate diagnosis of MTS is essential to providing curative and life-changing therapy but can be challenging in children in whom the impact of diagnosis is particularly high. Magnetic resonance imaging (MRI) plays an important role in the diagnosis of MTS, and image processing of MRI is a recently studied strategy to improve its accuracy. OBJECTIVE: In a retrospective case-control study, we assessed the performance of an image processing algorithm (Correlative Image Enhancement [CIE]) for detecting MTS-related hippocampal signal abnormality in children. MATERIALS AND METHODS: Twenty-seven pediatric MTS cases (9 males, 18 females; mean age: 16±standard deviation [SD] 6.7 years) were identified from a pathology database of amygdylo-hippocampectomies performed in children with epilepsy. Twenty-seven children with no seizure history (9 males, 18 females; mean age: 13.8±SD 2.8 years), and with normal brain MRI, were identified for the control group. Blinded investigators processed the MRI coronal FLAIR (fluid-attenuated inversion recovery) images with CIE, saved the processed images as a separate series, and made equivalent region of interest measurements on the processed and unprocessed series to calculate contrast-to-noise ratio. Six blinded reviewers then rated the randomized series for hippocampal signal abnormality and MTS disease status. RESULTS: CIE increased signal intensity and contrast-to-noise ratio in 26/27 hippocampi with pathologically confirmed MTS (96.3%) with the mean (SD) contrast-to-noise ratio of cases increasing from 14.9 (11.1) to 77.7 (58.7) after processing (P<0.001). Contrast-to-noise ratio increased in 1/54 normal control hippocampi (1.9%), with no significant change in the mean contrast-to-noise ratio of the control group after processing (P=0.57). Mean (SD) reader sensitivity for detecting abnormal signal intensity increased from 83.3% (14.2) to 94.8% (3.3) after processing. Mean specificity for abnormal signal intensity increased from 94.4% (7.3) to 96.3% (0). While sensitivity improved after processing for detection of MTS disease status in 4/6 readers, the mean reader sensitivity and specificity for MTS detection increased only minimally after processing, from 79.6% to 80.7% and from 95.7% to 96.3%, respectively. CONCLUSION: The CIE image processing algorithm selectively increased the contrast-to-noise ratio of hippocampi affected by MTS, improved reader performance in detecting MTS-related hippocampal signal abnormality and could have high impact on pediatric patients undergoing work-up for seizures.
BACKGROUND:Mesial temporal sclerosis (MTS) is an important cause of intractable epilepsy. Early and accurate diagnosis of MTS is essential to providing curative and life-changing therapy but can be challenging in children in whom the impact of diagnosis is particularly high. Magnetic resonance imaging (MRI) plays an important role in the diagnosis of MTS, and image processing of MRI is a recently studied strategy to improve its accuracy. OBJECTIVE: In a retrospective case-control study, we assessed the performance of an image processing algorithm (Correlative Image Enhancement [CIE]) for detecting MTS-related hippocampal signal abnormality in children. MATERIALS AND METHODS: Twenty-seven pediatric MTS cases (9 males, 18 females; mean age: 16±standard deviation [SD] 6.7 years) were identified from a pathology database of amygdylo-hippocampectomies performed in children with epilepsy. Twenty-seven children with no seizure history (9 males, 18 females; mean age: 13.8±SD 2.8 years), and with normal brain MRI, were identified for the control group. Blinded investigators processed the MRI coronal FLAIR (fluid-attenuated inversion recovery) images with CIE, saved the processed images as a separate series, and made equivalent region of interest measurements on the processed and unprocessed series to calculate contrast-to-noise ratio. Six blinded reviewers then rated the randomized series for hippocampal signal abnormality and MTS disease status. RESULTS: CIE increased signal intensity and contrast-to-noise ratio in 26/27 hippocampi with pathologically confirmed MTS (96.3%) with the mean (SD) contrast-to-noise ratio of cases increasing from 14.9 (11.1) to 77.7 (58.7) after processing (P<0.001). Contrast-to-noise ratio increased in 1/54 normal control hippocampi (1.9%), with no significant change in the mean contrast-to-noise ratio of the control group after processing (P=0.57). Mean (SD) reader sensitivity for detecting abnormal signal intensity increased from 83.3% (14.2) to 94.8% (3.3) after processing. Mean specificity for abnormal signal intensity increased from 94.4% (7.3) to 96.3% (0). While sensitivity improved after processing for detection of MTS disease status in 4/6 readers, the mean reader sensitivity and specificity for MTS detection increased only minimally after processing, from 79.6% to 80.7% and from 95.7% to 96.3%, respectively. CONCLUSION: The CIE image processing algorithm selectively increased the contrast-to-noise ratio of hippocampi affected by MTS, improved reader performance in detecting MTS-related hippocampal signal abnormality and could have high impact on pediatric patients undergoing work-up for seizures.
Authors: Gloria J Guzmán Pérez-Carrillo; Christopher Owen; Katherine E Schwetye; Spencer McFarlane; Ananth K Vellimana; Soe Mar; Michelle M Miller-Thomas; Joshua S Shimony; Matthew D Smyth; Tammie L S Benzinger Journal: J Neurosurg Pediatr Date: 2017-03-24 Impact factor: 2.375
Authors: S F Berkovic; A M McIntosh; R M Kalnins; G D Jackson; G C Fabinyi; G A Brazenor; P F Bladin; J L Hopper Journal: Neurology Date: 1995-07 Impact factor: 9.910
Authors: Maeike Zijlmans; Gérard A P de Kort; Theodore D Witkamp; Geertjan M Huiskamp; Jan-Henry Seppenwoolde; Alexander C van Huffelen; Frans S S Leijten Journal: J Magn Reson Imaging Date: 2009-08 Impact factor: 4.813
Authors: Maria Thom; Joan Y W Liu; Pam Thompson; Rahul Phadke; Marta Narkiewicz; Lillian Martinian; Derek Marsdon; Matthias Koepp; Luis Caboclo; Claudia B Catarino; Sanjay M Sisodiya Journal: Brain Date: 2011-09-08 Impact factor: 13.501