Usefulness of Olfactory Bulb Measurement in 3D-FIESTA in Differentiating Parkinson Disease from Atypical Parkinsonism.

BACKGROUND AND PURPOSE: Parkinson disease is a prevalent disease, with olfactory dysfunction recognized as an early nonmotor manifestation. It is sometimes difficult to differentiate Parkinson disease from atypical parkinsonism using conventional MR imaging and motor symptoms. It is also known that olfactory loss occurs to a lesser extent or is absent in atypical parkinsonism. To the best of our knowledge, no study has examined olfactory bulb changes to differentiate Parkinson disease from atypical parkinsonism, even in an early diagnosis, and its association with conventional MR imaging findings. Hence, we aimed to assess the utility of olfactory bulb measurements in differentiating Parkinson disease from atypical parkinsonism even in the early stage. MATERIALS AND METHODS: In this retrospective study, we enrolled 108 patients with Parkinson disease, 13 with corticobasal syndrome, 15 with multiple system atrophy, and 17 with progressive supranuclear palsy who developed parkinsonism. Thirty-nine age-matched healthy subjects served as controls. All subjects underwent conventional MR imaging and 3D FIESTA for olfactory bulb measurements using manual ROI quantification of the cross-sectional olfactory bulb area using the coronal plane. Bilateral olfactory bulb measurements were averaged. For group comparisons, we used the Welch t test, and we assessed diagnostic accuracy using receiver operating characteristic analysis. RESULTS: Patients with Parkinson disease had a mean olfactory bulb area of 4.2 (SD, 1.0 mm2), significantly smaller than in age-matched healthy subjects (6.6 [SD, 1.7 mm2], P < .001), and those with corticobasal syndrome (5.4 [SD, 1.2 mm2], P < .001), multiple system atrophy (6.5 [SD, 1.2 mm2], P < .001), and progressive supranuclear palsy (5.4 [SD, 1.2 mm2], P < .001). The receiver operating characteristic analysis for the olfactory bulb area measurements showed good diagnostic performance in differentiating Parkinson disease from atypical parkinsonism, with an area under the curve of 0.87, an optimal cutoff value of 5.1 mm2, and a false-positive rate of 18%. When we compared within 2 years of symptom onset, the olfactory bulb in Parkinson disease (4.2 [SD, 1.1 mm2]) remained significantly smaller than in atypical parkinsonism (versus corticobasal syndrome (6.1 [SD, 0.7 mm2]), P < .001; multiple system atrophy (6.3 [SD, 1.4 mm2]), P < .001; and progressive supranuclear palsy (5.2 [1.3 mm2], P = .003, respectively). CONCLUSIONS: 3D FIESTA-based olfactory bulb measurement holds promise for distinguishing Parkinson disease from atypical parkinsonism, especially in the early stage.

Zero Echo Time Magnetic Resonance Imaging; Techniques and Clinical Utility in Musculoskeletal System.

Zero echo time (ZTE) sequence is recent advanced magnetic resonance technique that utilizes ultrafast readouts to capture signals from short-T2 tissues. This sequence enables T2- and T2* weighted imaging of tissues with short intrinsic relaxation times by using an extremely short TE, and are increasingly used in the musculoskeletal system. We review the imaging physics of these sequences, practical limitations, and image reconstruction, and then discuss the clinical utilities in various disorders of the musculoskeletal system. ZTE can be readily incorporated into the clinical workflow, and is a promising technique to avoid unnecessary radiation exposure, cost, and time-consuming by computed tomography in some cases. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 1.

Feasibility of Breath-Hold Zero TE Magnetic Resonance Imaging Sequence for Evaluation of Pulmonary Arteriovenous Malformations After Embolotherapy.

ABSTRACT: We obtained breath-hold zero TE (ZTE) magnetic resonance imaging for the evaluation of pulmonary arteriovenous malformations before and after embolotherapy. To the best of our knowledge, there have been no reports of ZTE for the entire lung imaging in single breath-hold scan time such as 20 seconds. Breath-hold ZTE magnetic resonance imaging can be a useful technique for magnetic resonance-based follow-up of vascular lung diseases without using contrast media, reducing the undesired artifacts from metallic devices.

The effect of CT texture-based analysis using machine learning approaches on radiologists' performance in differentiating focal-type autoimmune pancreatitis and pancreatic duct carcinoma.

PURPOSE: To develop a support vector machine (SVM) classifier using CT texture-based analysis in differentiating focal-type autoimmune pancreatitis (AIP) and pancreatic duct carcinoma (PD), and to assess the radiologists' diagnostic performance with or without SVM. MATERIALS AND METHODS: This retrospective study included 50 patients (20 patients with focal-type AIP and 30 patients with PD) who underwent dynamic contrast-enhanced CT. Sixty-two CT texture-based features were extracted from 2D images of the arterial and portal phase CTs. We conducted data compression and feature selections using principal component analysis (PCA) and produced the SVM classifier. Four readers participated in this observer performance study and the statistical significance of differences with and without the SVM was assessed by receiver operating characteristic (ROC) analysis. RESULTS: The SVM performance indicated a high performance in differentiating focal-type AIP and PD (AUC = 0.920). The AUC for all 4 readers increased significantly from 0.827 to 0.911 when using the SVM outputs (p = 0.010). The AUC for inexperienced readers increased significantly from 0.781 to 0.905 when using the SVM outputs (p = 0.310). The AUC for experienced readers increased from 0.875 to 0.912 when using the SVM outputs, however, there was no significant difference (p = 0.018). CONCLUSION: The use of SVM classifier using CT texture-based features improved the diagnostic performance for differentiating focal-type AIP and PD on CT.

Vertebrobasilar Infarction Due to Bow Hunter's Syndrome in a Patient with Rheumatoid Arthritis: A Case Report.

A 60-year-old woman with a 37-year history of rheumatoid arthritis (RA) had a sudden onset of headache. Head MRI showed acute multiple infarctions in the vertebrobasilar region, and MR angiography showed stenosis of the right vertebral artery (VA). 3D-CT angiography of the craniovertebral junction showed atlantoaxial subluxation and stenosis of the right VA just distal to the transverse foramen of C2, which was due to osteophytes and degenerative changes secondary to RA. Digital subtraction angiography clearly demonstrated occlusion of the right VA during rightward head rotation. Based on those findings, rotatory instability at C1-2 was considered as the primary cause of the vertebrobasilar infarctions, and Bow Hunter's syndrome was diagnosed. The patient underwent C1-5 posterior fixation, and brain infarction has not recurred.

The effect of deep convolutional neural networks on radiologists' performance in the detection of hip fractures on digital pelvic radiographs.

PURPOSE: The purpose of our study is to develop deep convolutional neural network (DCNN) for detecting hip fractures using CT and MRI as a gold standard, and to evaluate the diagnostic performance of 7 readers with and without DCNN. METHODS: The study population consisted of 327 patients who underwent pelvic CT or MRI and were diagnosed with proximal femoral fractures. All radiographs were manually checked and annotated by radiologists referring to CT and MRI for selecting ROI. At first, a DCNN with the GoogLeNet model was trained by 302 cases. The remaining 25 cases and 25 control subjects were used for the observer performance study and for the testing of DCNN. Seven readers took part in this study. A continuous rating scale was used to record each observer's confidence level. Subsequently, each observer interpreted with the DCNN outputs and rated them again. The area under the curve (AUC) was used to compare the fracture detection. RESULTS: The average AUC of the 7 readers was 0.832. The AUC of DCNN alone was 0.905. The average AUC of the 7 readers with DCNN outputs was 0.876. The AUC of readers with DCNN output were higher than those without(p < 0.05). The AUC of the 2 experienced readers with DCNN output exceeded the AUC of DCNN alone. CONCLUSION: For detecting the hip fractures on radiographs, DCNN developed using CT and MRI as a gold standard by radiologists improved the diagnostic performance including the experienced readers.