Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Background: and Purpose Various mechanisms are involved in the etiology of stroke caused by atherosclerosis of the middle cerebral artery (MCA). Here, we compared differences in plaque nature and hemodynamic parameters according to stroke mechanism in patients with MCA atherosclerosis. Methods: Consecutive patients with asymptomatic and symptomatic MCA atherosclerosis (≥50% stenosis) were enrolled. MCA plaque characteristics (location and plaque enhancement) and wall shear stress (WSS) were measured using high-resolution vessel wall and four-dimensional flow magnetic resonance imaging, respectively, at five points (initial, upstream, minimal lumen, downstream, and terminal). These parameters were compared between patients with asymptomatic and symptomatic MCA atherosclerosis with infarctions of different mechanisms (artery-to-artery embolism vs. local branch occlusion). Results: In total, 110 patients (46 asymptomatic, 32 artery-to-artery embolisms, and 32 local branch occlusions) were investigated. Plaques were evenly distributed in the MCA of patients with asymptomatic MCA atherosclerosis, more commonly observed in the distal MCA of patients with artery-to-artery embolism, and in the middle MCA of patients with local branch occlusion. Maximum WSS and plaque enhancement were more prominent in the minimum lumen area of patients with asymptomatic MCA atherosclerosis or those with local branch occlusion, and were more prominent in the upstream area in those with artery-to-artery embolism. The elevated variability in the maximum WSS was related to stroke caused by artery-to-artery embolism. Conclusion Stroke caused by artery-to-artery embolism was related to plaque enhancement and the highest maximum WSS at the upstream point of the plaque, and was associated with elevated variability of maximum WSS.

Purpose: This study aimed to propose an effective end-to-end process in medical imaging using an independent task learning (ITL) algorithm and to evaluate its performance in maxillary sinusitis applications. Materials and Methods: For the internal dataset, 2122 Waters’ view X-ray images, which included 1376 normal and 746 sinusitis images, were divided into training (n=1824) and test (n=298) datasets. For external validation, 700 images, including 379 normal and 321 sinusitis images, from three different institutions were evaluated. To develop the automatic diagnosis system algorithm, four processing steps were performed: 1) preprocessing for ITL, 2) facial patch detection, 3) maxillary sinusitis detection, and 4) a localization report with the sinusitis detector. Results: The accuracy of facial patch detection, which was the first step in the end-to-end algorithm, was 100%, 100%, 99.5%, and 97.5% for the internal set and external validation sets #1, #2, and #3, respectively. The accuracy and area under the receiver operating characteristic curve (AUC) of maxillary sinusitis detection were 88.93% (0.89), 91.67% (0.90), 90.45% (0.86), and 85.13% (0.85) for the internal set and external validation sets #1, #2, and #3, respectively. The accuracy and AUC of the fully automatic sinusitis diagnosis system, including site localization, were 79.87% (0.80), 84.67% (0.82), 83.92% (0.82), and 73.85% (0.74) for the internal set and external validation sets #1, #2, and #3, respectively. Conclusion ITL application for maxillary sinusitis showed reasonable performance in internal and external validation tests, compared with applications used in previous studies.

Although various neoplasms may develop in the ventricular system, glioblastomas are rare. An 80-year-old woman visited our hospital with intractable headache related to a right ventricular large mass, which exhibited heterogeneous enhancement involving the body, trigone, and occipital horn of the right lateral ventricle on MRI. The mass was diagnosed as glioblastoma multiforme on surgical pathology. Herein, the authors present a case and review the existing literature with regarding to incidence, pathophysiology, prognostic factors, imaging and pathologic findings of intraventricular glioblastoma multiforme.

No abstract available.
Arteries ; Intracranial Arteriosclerosis

PURPOSE: This study aimed to compare cerebral hemispheric volumes between pharmacologic treatment and supportive management of patent ductus arteriosus (PDA). METHODS: The study was conducted retrospectively. The subjects of period 1 group were very low birth weight infants whose PDA were treated with pharmacologic closure. Period 2 group were treated with supportive management. Regional brain volumes measured using magnetic resonance imaging were compared between the two groups. RESULTS: total of 12 infants were included. Their median gestational age was 27⁺⁶ (range: 24⁺¹–31⁺¹) weeks and birth weight was 1,065 g (range: 690–1,380). Between the two groups, there was no difference in Apgar score, incidence of bronchopulmonary dysplasia, necrotizing enterocolitis, and culture proven sepsis. The regional brain volumes such as gray matter (Period 1 group, 76,833 mm³ [55,759–100,388] vs. Period 2 group, 79,870 mm³ [59,957–113,018], P=0.59), white matter (82,993 mm³ [63,130–121,311] vs. 92,576 mm³ [77,200–104,506], P=0.18), cerebrospinal fluid (17,167 mm³ [9,279–22,760] vs. 14,348 mm³ [7,018–27,604], P=0.94), basal ganglia (2,065 mm³ [1,697–2,482] vs. 2,306 mm³ [2,065–3,009], P=0.18), and cerebellum (18,374 mm³ [14,843–24,657] vs. 18,096 mm³ [16,134–23,627], P=0.94) were not different between the two groups. CONCLUSION: Regional brain volumes were not different between pharmacological and conservative treatment in infants with PDA. Further wellcontrolled studies are required to evaluate the advantages or disadvantages of supportive management without pharmacologic treatment of PDA.
Apgar Score ; Basal Ganglia ; Birth Weight ; Brain* ; Bronchopulmonary Dysplasia ; Cerebellum ; Cerebrospinal Fluid ; Ductus Arteriosus, Patent* ; Enterocolitis, Necrotizing ; Gestational Age ; Gray Matter ; Humans ; Ibuprofen* ; Incidence ; Infant* ; Infant, Newborn ; Infant, Premature ; Infant, Very Low Birth Weight* ; Magnetic Resonance Imaging ; Pilot Projects* ; Retrospective Studies ; Sepsis ; White Matter

PURPOSE: T2* relaxation time which includes susceptibility information represents unique feature of tissue. The objective of this study was to investigate T2* relaxation times of the normal glandular tissue and fat of breast using a 3T MRI system. MATERIALS AND METHODS: Seven-echo MR Images were acquired from 52 female subjects (age 49 +/- 12 years; range, 25 to 75) using a three-dimensional (3D) gradient-echo sequence. Echo times were between 2.28 ms to 25.72 ms in 3.91 ms steps. Voxel-based T2* relaxation times and R2* relaxation rate maps were calculated by using the linear curve fitting for each subject. The 3D regions-of-interest (ROI) of the normal glandular tissue and fat were drawn on the longest echo-time image to obtain T2* and R2* values. Mean values of those parameters were calculated over all subjects. RESULTS: The 3D ROI sizes were 4818 +/- 4679 voxels and 1455 +/- 785 voxels for the normal glandular tissue and fat, respectively. The mean T2* values were 22.40 +/- 5.61 ms and 36.36 +/- 8.77 ms for normal glandular tissue and fat, respectively. The mean R2* values were 0.0524 +/- 0.0134/ms and 0.0297 +/- 0.0069/ms for the normal glandular tissue and fat, respectively. CONCLUSION: T2* and R2* values were measured from human breast tissues. T2* of the normal glandular tissue was shorter than that of fat. Measurement of T2* relaxation time could be important to understand susceptibility effects in the breast cancer and the normal tissue.
Breast Neoplasms ; Breast* ; Female ; Humans ; Magnetic Resonance Imaging* ; Relaxation*