Global and structured waves of rs-fMRI signal identified as putative propagation of spontaneous neural activity.

Conventional resting-state fMRI (rs-fMRI) studies have focused on investigating the synchronous neural activity in functionally relevant distant regions that are termed as resting-state networks. On the other hand, less is known about the spatiotemporal dynamics of the spontaneous activity of the brain. By examining the characteristics of both rs-fMRI and vascular time lag that was measured using dynamic susceptibility contrast-enhanced perfusion weighted imaging, the present study identifies several structured propagation of the rs-fMRI signal as putative neural streams. Temporal shift of both rs-fMRI and perfusion imaging data in each voxel compared with the averaged whole-brain signal was computed using cross-correlation analysis. In contrast to the uniformity of the vascular time lag across subjects, whole-brain rs-fMRI time lag was estimated to be composed of three independent components. After regression of vascular time lag, independent component analysis was applied to rs-fMRI data. The putative neural streams showed slow propagation of the signal from task-positive regions to main nodes of the default mode network, which may represent a mode of transmission underlying the interactions among the resting-state networks.

Machine Learning for Computer-aided Diagnosis.

Machine learning algorithms are to analyze any dataset to extract data-driven model, prediction rule, or decision rule from the dataset. Various machine learning algorithms are now used to develop high-performance medical image processing systems such as computer-aided detection (CADe) system which detects clinically significant objects from medical images and computer-aided diagnosis (CADx) system which quantifies malignancy of manually or automatically detected clinical objects. In this paper, we introduce some applications of machine learning algorithms to the development of medical image processing system.

Model-based iterative reconstruction technique for ultralow-dose chest CT: comparison of pulmonary nodule detectability with the adaptive statistical iterative reconstruction technique.

PURPOSE: The purpose of this study was to evaluate whether model-based iterative reconstruction (MBIR) enables dose reduction over adaptive iterative reconstruction (ASIR) while maintaining diagnostic performance. METHODS: In this institutional review board-approved and Health Insurance Portability and Accountability Act-compliant study, 59 patients (mean [SD] age, 64.7 [13.4] years) gave informed consent to undergo reference-, low-, and ultralow-dose chest computed tomography (CT) with 64-row multidetector CT. The reference- and low-dose CT involved the use of automatic tube current modulation with fixed noise indices (31.5 and 70.44 at 0.625 mm, respectively) and were reconstructed with 50% ASIR-filtered back projection blending. The ultralow-dose CT was acquired with a fixed tube current-time product of 5 mA s and reconstructed with MBIR. Two radiologists evaluated 2.5- and 0.625-mm-slice-thick axial images from low-dose ASIR and ultralow-dose MBIR, recorded the pattern of each nodule candidate, and assigned each a confidence score. A reference standard was established by a consensus panel of 2 different radiologists, who identified 84 noncalcified nodules with diameters of 4 mm or greater on reference-dose ASIR (ground-glass opacity, n = 18; partly solid, n = 11; solid, n = 55). Sensitivity in nodule detection was assessed using the McNemar test. Jackknife alternative free-response receiver operating characteristic (JAFROC) analysis was applied to assess the results including confidence scores. RESULTS: Compared with the low-dose CT, a 78.1% decrease in dose-length product was seen with the ultralow-dose CT. No significant differences were observed between the low-dose ASIR and the ultralow-dose MBIR for overall nodule detection in sensitivity (P = 0.48-0.69) or the JAFROC analysis (P = 0.57). Likewise, no significant differences were seen for ground-glass opacity, partly solid, or solid nodule detection in sensitivity (P = 0.08-0.65) or the JAFROC analysis (P = 0.21-0.90). CONCLUSIONS: Model-based iterative reconstruction enables nearly an 80% reduction in radiation dose for chest CT from a low-dose level to an ultralow-dose level, without affecting nodule detectability.

Adaptive statistical iterative reconstruction for volume-rendered computed tomography portovenography: improvement of image quality.

PURPOSE: Adaptive statistical iterative reconstruction (ASIR) is a reconstruction technique for computed tomography (CT) that reduces image noise. The purpose of our study was to investigate whether ASIR improves the quality of volume-rendered (VR) CT portovenography. MATERIALS AND METHODS: Institutional review board approval, with waived consent, was obtained. A total of 19 patients (12 men, 7 women; mean age 69.0 years; range 25-82 years) suspected of having liver lesions underwent three-phase enhanced CT. VR image sets were prepared with both the conventional method and ASIR. The required time to make VR images was recorded. Two radiologists performed independent qualitative evaluations of the image sets. The Wilcoxon signed-rank test was used for statistical analysis. Contrast-noise ratios (CNRs) of the portal and hepatic vein were also evaluated. RESULTS: Overall image quality was significantly improved by ASIR (P < 0.0001 and P = 0.0155 for each radiologist). ASIR enhanced CNRs of the portal and hepatic vein significantly (P < 0.0001). The time required to create VR images was significantly shorter with ASIR (84.7 vs. 117.1 s; P = 0.014). CONCLUSION: ASIR enhances CNRs and improves image quality in VR CT portovenography. It also shortens the time required to create liver VR CT portovenographs.