A semi-automated volumetric software for segmentation and perfusion parameter quantification of brain tumors using 320-row multidetector computed tomography: a validation study.

PURPOSE: We developed a semi-automated volumetric software, NPerfusion, to segment brain tumors and quantify perfusion parameters on whole-brain CT perfusion (WBCTP) images. The purpose of this study was to assess the feasibility of the software and to validate its performance compared with manual segmentation. METHODS: Twenty-nine patients with pathologically proven brain tumors who underwent preoperative WBCTP between August 2012 and February 2015 were included. Three perfusion parameters, arterial flow (AF), equivalent blood volume (EBV), and Patlak flow (PF, which is a measure of permeability of capillaries), of brain tumors were generated by a commercial software and then quantified volumetrically by NPerfusion, which also semi-automatically segmented tumor boundaries. The quantification was validated by comparison with that of manual segmentation in terms of the concordance correlation coefficient and Bland-Altman analysis. RESULTS: With NPerfusion, we successfully performed segmentation and quantified whole volumetric perfusion parameters of all 29 brain tumors that showed consistent perfusion trends with previous studies. The validation of the perfusion parameter quantification exhibited almost perfect agreement with manual segmentation, with Lin concordance correlation coefficients (ρ c) for AF, EBV, and PF of 0.9988, 0.9994, and 0.9976, respectively. On Bland-Altman analysis, most differences between this software and manual segmentation on the commercial software were within the limit of agreement. CONCLUSIONS: NPerfusion successfully performs segmentation of brain tumors and calculates perfusion parameters of brain tumors. We validated this semi-automated segmentation software by comparing it with manual segmentation. NPerfusion can be used to calculate volumetric perfusion parameters of brain tumors from WBCTP.

Respiratory syncytial virus-related encephalitis: magnetic resonance imaging findings with diffusion-weighted study.

INTRODUCTION: Respiratory syncytial virus (RSV) is a common pathogen causing acute respiratory infection in children. Herein, we describe the incidence and clinical and magnetic resonance imaging (MRI) findings of RSV-related encephalitis, a major neurological complication of RSV infection. METHODS: We retrospectively reviewed the medical records and imaging findings of the patients over the past 7 years who are admitted to our medical center and are tested positive for RSV-RNA by reverse transcriptase PCR. In total, 3,856 patients were diagnosed with RSV bronchiolitis, and 28 of them underwent brain MRI for the evaluation of neurologic symptoms; 8 of these 28 patients had positive imaging findings. Five of these 8 patients were excluded because of non-RSV-related pathologies, such as subdural hemorrhage, brain volume loss due to status epilepticus, periventricular leukomalacia, preexisting ventriculomegaly, and hypoxic brain injury. RESULTS: The incidence of RSV-related encephalitis was as follows: 3/3,856 (0.08 %) of the patients are positive for RSV RNA, 3/28 (10.7 %) of the patient underwent brain MRI for neurological symptom, and 3/8 (37.5 %) of patients revealed abnormal MR findings. The imaging findings were suggestive of patterns of rhombenmesencephalitis, encephalitis with acute disseminated encephalomyelitis, and limbic encephalitis. They demonstrated no diffusion abnormality on diffusion-weighted image and symptom improvement on the follow-up study. CONCLUSION: Encephalitis with RSV bronchiolitis occurs rarely. However, on brain MRI performed upon suspicion of neurologic involvement, RSV encephalitis is not infrequently observed among the abnormal MR findings and may mimic other viral and limbic encephalitis. Physicians should be aware of this entity to ensure proper diagnosis and neurologic care of RSV-positive patients.

Could both intrinsic and extrinsic iodine be successfully suppressed on virtual non-contrast CT images for detecting thyroid calcification?

PURPOSE: Although virtual non-contrast (VNC) successfully removes iodinated contrast, uncertainty exists regarding the feasibility of VNC to suppress iodine for detecting thyroid calcification. Therefore, we evaluated whether both intrinsic and extrinsic iodine attenuation were suppressed on VNC images. MATERIAL AND METHODS: We enrolled 128 patients (male: female 17:111; age 48.0 ± 10.4 years) who underwent dual-layer dual-energy CT (DL-DECT) examination before their thyroid cancer surgeries. Two additional sets of VNC (VNCu, VNCc) images were retrospectively generated from their true unenhanced (TUE) and true contrast-enhanced (TCE) series. We compared CT attenuation values measured on the VNCu and VNCc images by drawing identical regions of interest encompassing thyroid parenchyma, then subjectively determined the concordance of calcification. RESULTS: Although CT attenuation discrepancies between the VNCu and VNCc were significant (2.0 ± 5.7HU, p < 0.001),61.7%, 89.1%, and 100.0% of all measurements were < 5HU, < 10HU, and < 15HU. Based on Bland-Altman analysis, the limits of agreement were - 9.2HU and 13.2HU, whereas the proportional differences were small for VNC images generated from both TUE and TCE images. There was no discordance between two VNC image sets in detecting thyroid calcification. CONCLUSIONS: VNC technique could be a feasible method to suppress both intrinsic and extrinsically administered iodine for detecting thyroid calcification.

Early Prediction of Periventricular Leukomalacia Using Quantitative Texture Analysis of Serial Cranial Ultrasound Scans in Very Preterm Infants.

We compared texture parameters of serial cranial ultrasound (cUS) images of periventricular leukomalacia (PVL) and normal periventricular echogenicity (PVE) in very preterm infants and evaluated the early predictive values of texture analysis (TA) for PVL. Ten individuals with PVL and 10 control individuals with PVE assessed with an initial cUS within 1 wk of birth and follow-up cUS at 2-3 and 4-6 wk of life were included. TA was performed on the region of interest of PVE at the parieto-occipital area on serial cUS. Opposite changes in variance were obtained between the first two cUS sessions in both groups (p = 0.017 in PVL and p = 0.005 in PVE). The variance-to-mean ratio (VMR) between the second and first cUS sessions differed (p = 0.016) and reliably stratified the groups (area under the receiver operating characteristic curve: 0.820, 95% confidence interval: 0.587-1.000, sensitivity: 100%, specificity: 60%). TA of serial cUS helps to predict PVL within 3 wk of life.