Shun Zhang

Chronic active multiple sclerosis lesions, characterized by a hyperintense rim of iron-enriched, activated microglia and macrophages, have been linked to greater tissue damage. Post-mortem studies have determined that chronic active lesions are primarily related to the later stages of multiple sclerosis; however, the occurrence of these lesions, and their relationship to earlier disease stages may be greatly underestimated. Detection of chronic active lesions across the patient spectrum of multiple sclerosis requires a validated imaging tool to accurately identify lesions with persistent inflammation. Quantitative susceptibility mapping provides efficient in vivo quantification of susceptibility changes related to iron deposition and the potential to identify lesions harbouring iron-laden inflammatory cells. The PET tracer 11C-PK11195 targets the translocator protein expressed by activated microglia and infiltrating macrophages. Accordingly, this study aimed to validate that lesions with a hyperintense rim on quantitative susceptibility mapping from both relapsing and progressive patients demonstrate a higher level of innate immune activation as measured on 11C-PK11195 PET. Thirty patients were enrolled in this study, 24 patients had relapsing remitting multiple sclerosis, six had progressive multiple sclerosis, and all patients had concomitant MRI with a gradient echo sequence and PET with 11C-PK11195. A total of 406 chronic lesions were detected, and 43 chronic lesions with a hyperintense rim on quantitative susceptibility mapping were identified as rim+ lesions. Susceptibility (relative to CSF) was higher in rim+ (2.42 ± 17.45 ppb) compared to rim- lesions (-14.6 ± 19.3 ppb, P < 0.0001). Among rim+ lesions, susceptibility within the rim (20.04 ± 14.28 ppb) was significantly higher compared to the core (-5.49 ± 14.44 ppb, P < 0.0001), consistent with the presence of iron. In a mixed-effects model, 11C-PK11195 uptake, representing activated microglia/macrophages, was higher in rim+ lesions compared to rim- lesions (P = 0.015). Validating our in vivo imaging results, multiple sclerosis brain slabs were imaged with quantitative susceptibility mapping and processed for immunohistochemistry. These results showed a positive translocator protein signal throughout the expansive hyperintense border of rim+ lesions, which co-localized with iron containing CD68+ microglia and macrophages. In conclusion, this study provides evidence that suggests that a hyperintense rim on quantitative susceptibility measure within a chronic lesion is a correlate for persistent inflammatory activity and that these lesions can be identified in the relapsing patients. Utilizing quantitative susceptibility measure to differentiate chronic multiple sclerosis lesion subtypes, especially chronic active lesions, would provide a method to assess the impact of these lesions on disease progression.

// Rifeng Jiang 1, * , Jingjing Jiang 1, * , Lingyun Zhao 1 , Jiaxuan Zhang 1 , Shun Zhang 1 , Yihao Yao 1 , Shiqi Yang 1 , Jingjing Shi 1 , Nanxi Shen 1 , Changliang Su 1 , Ju Zhang 1 , Wenzhen Zhu 1 1 Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China * These authors have contributed equally to this work Correspondence to: Wenzhen Zhu, e-mail: zhuwenzhen@hotmail.com Keywords: diffusion kurtosis imaging, glioma, grading, cellular proliferation, Ki-67 Received: April 29, 2015&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Accepted: October 22, 2015&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Published: November 03, 2015 ABSTRACT Conventional diffusion imaging techniques are not sufficiently accurate for evaluating glioma grade and cellular proliferation, which are critical for guiding glioma treatment. Diffusion kurtosis imaging (DKI), an advanced non-Gaussian diffusion imaging technique, has shown potential in grading glioma; however, its applications in this tumor have not been fully elucidated. In this study, DKI and diffusion weighted imaging (DWI) were performed on 74 consecutive patients with histopathologically confirmed glioma. The kurtosis and conventional diffusion metric values of the tumor were semi-automatically obtained. The relationships of these metrics with the glioma grade and Ki-67 expression were evaluated. The diagnostic efficiency of these metrics in grading was further compared. It was demonstrated that compared with the conventional diffusion metrics, the kurtosis metrics were more promising imaging markers in distinguishing high-grade from low-grade gliomas and distinguishing among grade II, III and IV gliomas; the kurtosis metrics also showed great potential in the prediction of Ki-67 expression. To our best knowledge, we are the first to reveal the ability of DKI to assess the cellular proliferation of gliomas, and to employ the semi-automatic method for the accurate measurement of gliomas. These results could have a significant impact on the diagnosis and subsequent therapy of glioma.

Purpose To map the cerebral metabolic rate of oxygen (CMRO 2 ) by estimating the oxygen extraction fraction (OEF) from gradient echo imaging (GRE) using phase and magnitude of the GRE data. Theory and Methods 3D multi‐echo gradient echo imaging and perfusion imaging with arterial spin labeling were performed in 11 healthy subjects. CMRO 2 and OEF maps were reconstructed by joint quantitative susceptibility mapping (QSM) to process GRE phases and quantitative blood oxygen level‐dependent (qBOLD) modeling to process GRE magnitudes. Comparisons with QSM and qBOLD alone were performed using ROI analysis, paired t‐tests, and Bland‐Altman plot. Results The average CMRO 2 value in cortical gray matter across subjects were 140.4 ± 14.9, 134.1 ± 12.5, and 184.6 ± 17.9 μmol/100 g/min, with corresponding OEFs of 30.9 ± 3.4%, 30.0 ± 1.8%, and 40.9 ± 2.4% for methods based on QSM, qBOLD, and QSM+qBOLD, respectively. QSM+qBOLD provided the highest CMRO 2 contrast between gray and white matter, more uniform OEF than QSM, and less noisy OEF than qBOLD. Conclusion Quantitative CMRO 2 mapping that fits the entire complex GRE data is feasible by combining QSM analysis of phase and qBOLD analysis of magnitude.