Purpose: To introduce a new method for removing background artifacts in field maps and apply it to enhance the accuracy of susceptibility mapping.Methods: A field artifact removal method is introduced that is based on the sophisticated harmonic artifact reduction for phase data (SHARP) method exploiting the harmonic mean value property. The new method uses Tikhonov regularization at the deconvolution stage and is referred to as regularization enabled SHARP (RESHARP). RESHARP was compared with SHARP in a field‐forward susceptibility simulation and in human brain experiments, considering effects on both field maps and the resulting susceptibility maps.Results: From the simulation, RESHARP was able to reduce error in the field map by 17.4% as compared with SHARP, resulting in a more accurate single‐angle susceptibility map with 6.5% relative error (compared with 48.5% using SHARP). Using RESHARP in vivo, field and susceptibility maps of the brain displayed fewer artifacts particularly at the brain boundaries, and susceptibility measurements of iron‐rich deep gray matter were also more consistent than SHARP across healthy subjects of similar age.Conclusion: Compared with SHARP, RESHARP removes background field artifact more effectively, leading to more accurate susceptibility measurements in iron‐rich deep gray matter. Magn Reson Med 71:1151–1157, 2014. © 2013 Wiley Periodicals, Inc.

Objectives: Rapid and accurate diagnosis of coronavirus disease 2019 (COVID-19) is critical during the epidemic. We aim to identify differences in CT imaging and clinical manifestations between pneumonia patients with and without COVID-19, and to develop and validate a diagnostic model for COVID-19 based on radiological semantic and clinical features alone. Methods: A consecutive cohort of 70 COVID-19 and 66 non-COVID-19 pneumonia patients were retrospectively recruited from five institutions. Patients were divided into primary (n = 98) and validation (n = 38) cohorts. The chi-square test, Student’s t test, and Kruskal-Wallis H test were performed, comparing 1745 lesions and 67 features in the two groups. Three models were constructed using radiological semantic and clinical features through multivariate logistic regression. Diagnostic efficacies of developed models were quantified by receiver operating characteristic curve. Clinical usage was evaluated by decision curve analysis and nomogram. Results: Eighteen radiological semantic features and seventeen clinical features were identified to be significantly different. Besides ground-glass opacities (p = 0.032) and consolidation (p = 0.001) in the lung periphery, the lesion size (1–3 cm) is also significant for the diagnosis of COVID-19 (p = 0.027). Lung score presents no significant difference (p = 0.417). Three diagnostic models achieved an area under the curve value as high as 0.986 (95% CI 0.966~1.000). The clinical and radiological semantic models provided a better diagnostic performance and more considerable net benefits. Conclusions: Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. A model composed of radiological semantic and clinical features has an excellent performance for the diagnosis of COVID-19. Key Points: • Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. • A diagnostic model for COVID-19 was developed and validated using radiological semantic and clinical features, which had an area under the curve value of 0.986 (95% CI 0.966~1.000) and 0.936 (95% CI 0.866~1.000) in the primary and validation cohorts, respectively.

Longitudinal evolution of intracranial hemorrhage by using quantitative susceptibility mapping demonstrated susceptibility differences in different disease stages, which are not found at conventional MRI. PurposeTo follow the evolution of intracranial hemorrhage (ICH) by using quantitative susceptibility mapping (QSM).Materials and MethodsThirty-six patients with ICH confirmed at CT were enrolled to follow ICH evolution on day 2, 7, and 30 after symptom onset between August 2013 and April 2017. QSM was reconstructed from MRI gradient-echo phase images acquired at 1.5 T or 3.0 T. ICH regions were manually drawn on two-dimensional sections of co-registered CT and MR images independently by two raters. The ICH areas and mean values were compared between CT and MRI by using Bland-Altman plots and Pearson correlation. QSM time evolution of ICH was assessed by using paired t tests and was compared with conventional T2-weighted fluid-attenuated inversion recovery, or T1-weighted or T2*-weighted magnitude intensities.ResultsSignificant reductions in ICH susceptibility were found between day 2 and day 7 (P < .001) and between day 7 and day 30 (P = .003), corresponding to different disease stages. The ICH areas meas...

Purpose: To investigate subcortical gray matter segmentation using transverse relaxation rate (R₂*) and quantitative susceptibility mapping (QSM) and apply it to voxel‐based analysis in multiple sclerosis (MS).Materials and Methods: Voxel‐based variation in R₂* and QSM within deep gray matter was examined and compared to standard whole‐structure analysis using 37 MS subjects and 37 matched controls. Deep gray matter nuclei (caudate, putamen, globus pallidus, and thalamus) were automatically segmented and morphed onto a custom atlas based on QSM and standard T₁‐weighted images. Segmentation accuracy and scan–rescan reliability were tested.Results: When considering only significant regions as returned by the multivariate voxel‐based analysis, increased R₂* and QSM was found in MS subjects compared to controls in portions of all four nuclei studied (P < 0.002). For R₂*, regional analysis yielded at least 66‐fold improved P‐value significance in all nuclei over standard whole‐structure analysis, while for QSM only thalamus benefited, with 5‐fold improvement in significance. Improved segmentation over standard methods, particularly for globus pallidus (2.8 times higher Dice score), was achieved by incorporating high‐contrast QSM into the atlas. Voxel‐based reliability was highest for QSM (<1% variation).Conclusion: Automatic segmentation of iron‐rich deep gray matter can be improved by incorporating QSM. Voxel‐based evaluation yielded increased R₂* and QSM in MS subjects in all four nuclei studied with R₂*, benefiting the most from localized analysis over whole‐structure measures. J. MAGN. RESON. IMAGING 2015;42:1601–1610.