Search

Glioblastoma is the most common primary malignant tumor of the central nervous system, most of which occur in the brain. Primary spinal cord glioblastoma is extremely rare. A 47-year-old woman presented with recurrent and progressive paresthesia and paralysis of right lower extremity. MR examination revealed a marked nodular enlargement of spinal cord at the T12 level, showing heterogeneous enhancement. FDG PET/CT showed this intraspinal lesion with high FDG uptake. Imaging findings indicated a possible diagnosis of malignant neoplasm. Surgical resection was performed, and the pathologic results confirmed a primary spinal cord glioblastoma.

Purpose: The magnetic resonance imaging (MRI)-Linac system combines a MRI scanner and a linear accelerator (Linac) to realize real-time localization and adaptive radiotherapy for tumors. Given that the Australian MRI-Linac system has a 30-cm diameter of spherical volume (DSV) with a shimmed homogeneity of ±4.05 parts per million (ppm), a gradient nonlinearity (GNL) of <5% can only be assured within 15 cm from the system's isocenter. GNL increases from the isocenter and escalates close to and outside of the edge of the DSV. Gradient nonlinearity can cause large geometric distortions, which may provide inaccurate tumor localization and potentially degrade the radiotherapy treatment. In this study, we aimed to characterize and correct the geometric distortions both inside and outside of the DSV., Methods: On the basis of phantom measurements, an inverse electromagnetic (EM) method was developed to reconstitute the virtual current density distribution that could generate gradient fields. The obtained virtual EM source was capable of characterizing the GNL field both inside and outside of the DSV. With the use of this GNL field information, our recently developed "GNL-encoding" reconstruction method was applied to correct the distortions implemented in the k-space domain., Results: Both phantom and in vivo human images were used to validate the proposed method. The results showed that the maximal displacements within an imaging volume of 30 cm × 30 cm × 30 cm after using the fifth-order spherical harmonic (SH) method and the proposed method were 6.1 ± 0.6 mm and 1.8 ± 0.6 mm, respectively. Compared with the fifth-order SH-based method, the new solution decreased the percentage of markers (within an imaging volume of 30 cm × 30 cm × 30 cm) with ≥1.5-mm distortions from 6.3% to 1.3%, indicating substantially improved geometric accuracy., Conclusions: The experimental results indicated that the proposed method could provide substantially improved geometric accuracy for the region outside of the DSV, when comparing with the fifth-order SH-based method., (© 2019 American Association of Physicists in Medicine.)

In magnetic resonance imaging (MRI), system imperfections and eddy currents can cause gradient field deviation (GFD), leading to various image distortions, such as increased noise, ghosting artifacts, and geometric deformation. These distortions can degrade the clinical value of MR images. Generally, non-Cartesian image sequences, such as radial sampling, produce larger gradient deviations than Cartesian sampling, as a result of stronger eddy current-induced gradient delays and phase errors. In this paper, we developed a GFD encoding method to reduce image noise and artifacts for radial MRI. In the proposed method, a hybrid norm (combination of L2 and L1 norms) optimization problem was formed, which incorporated a wavelet sub-band adaptive regularization mechanism. The new approach seeks a regularized solution not only offering corrected images with reduced artifacts and geometric deformation, but also good preservation of anatomical structural details. The new method was evaluated with simulation and experiment at 9.4 T MRI. The results demonstrated that the proposed method can provide over 50% noise reduction and 15% artifact reduction compared with the traditional regridding method, suggesting substantially reduced GFD-induced distortions and improved image quality.

By using entropy and local neighborhood information, we present in this study a robust adaptive Gaussian regularizing Chan-Vese (CV) model to segment the myocardium from magnetic resonance images with intensity inhomogeneity. By utilizing the circular Hough transformation (CHT) our model is able to detect epicardial and endocardial contours of the left ventricle (LV) as circles automatically, and the circles are used as the initialization. In the cost functional of our model, the interior and exterior energies are weighted by the entropy to improve the robustness of the evolving curve. Local neighborhood information is used to evolve the level set function to reduce the impact of the heterogeneity inside the regions and to improve the segmentation accuracy. An adaptive window is utilized to reduce the sensitivity to initialization. The Gaussian kernel is used to regularize the level set function, which can not only ensure the smoothness and stability of the level set function, but also eliminate the traditional Euclidean length term and re-initialization. Extensive validation of the proposed method on patient data demonstrates its superior performance over other state-of-the-art methods.

Antiepileptic drugs (AEDs) are the first choice in magnetic resonance imaging (MRI)-negative patients with epilepsy, although the responses to AEDs are diverse. Preoperative evaluation and postoperative prognosis in MRI-negative epilepsy have been reported. However, there are few tools for predicting the response to AEDs. Herein, we developed an AED response scale based on clinical factors and video-electroencephalography (VEEG) in MRI-negative patients with epilepsy. A total of 132 consecutive patients with MRI-negative epilepsy at the Epilepsy Center of Henan Provincial People's Hospital between August 2016 and August 2018 were included. Patients were further divided into drug-responsive epilepsy ([DSE-MRI (−)]; n = 101) and drug-resistant epilepsy ([DRE-MRI (−)]; n = 31) groups. The clinical and VEEG factors were evaluated in univariate analyses and multivariate logistic regression analyses. A scale was derived and the scores categorized into 3 risk levels of DRE-MRI (−). A scale was established based on 4 independent risk factors for DRE-MRI (−). The scale had a sensitivity of 83.87%, specificity of 80.20%, positive likelihood ratio of 4.24, negative likelihood ratio of 0.20, and showed good discrimination with the area under the curve (AUC) of 0.886 (0.826–0.946). The categorization of the risk score based on this scale was: low risk (0–3 points), medium risk (3–5 points), and high risk (> 5 points). We established a DRE-MRI (−) scale with a good sensitivity and specificity, which may be useful for clinicians when making medical decisions in patients with MRI-negative epilepsy. • AEDs are the first choice in MRI-negative epilepsy patients. • A scale was established based on clinical factors and VEEG, which showed a good discrimination and may be useful for making medical decisions in patients with MRI-negative epilepsy. • The risk lever was categorized as low risk (0–3 points), medium risk (3–5 points), and high risk (> 5 points) [ABSTRACT FROM AUTHOR]