Evaluation of signal intensity changes in dentate nucleus and globus pallidus on magnetic resonance imaging after intrathecal gadolinium-based contrast agent administration.

PURPOSE: Gadolinium deposition has been reported in several normal anatomical structures in the brain after repeated administration of intravenous gadolinium-based contrast agents (GBCAs) used in magnetic resonance imaging (MRI). This study presents preliminary results to see if there is any gadolinium deposition in the dentate nucleus and globus pallidus after using intrathecal GBCAs. METHODS: Between November 2018 and November 2020, 29 patients who underwent intrathecal contrast-enhanced MR cisternography with the suspicion of rhinorrhea were included in this prospective study. In contrast-enhanced MR cisternography, gadoterate meglumine was administered by intrathecal injection at a dose of 1 ml. One month later, patients had a control MRI with 3D T1 SPACE fat-saturated (FS) and susceptibility weighted images (SWI) sequences. The ratio of dentate nucleus signal intensity to middle cerebellar peduncle signal intensity (DN/MCP ratio) and the ratio of globus pallidus signal intensity to thalamus signal intensity (GP/T ratio) were calculated using region of interest (ROI) on pre-contrast and control MRI sequences. RESULTS: There was no significant difference for DN/MCP ratio and GP/T ratio on 3D T1 SPACE FS and SWI sequences after intrathecal GBCAs administration compared to baseline MRI. CONCLUSION: Administration of intrathecal GBCAs did not cause a measurable change in the signal intensity of the dentate nucleus and globus pallidus after a single injection.

An extensive study for binary characterisation of adrenal tumours.

On adrenal glands, benign tumours generally change the hormone equilibrium, and malign tumours usually tend to spread to the nearby tissues and to the organs of the immune system. These features can give a trace about the type of adrenal tumours; however, they cannot be observed all the time. Different tumour types can be confused in terms of having a similar shape, size and intensity features on scans. To support the evaluation process, biopsy process is applied that includes injury and complication risks. In this study, we handle the binary characterisation of adrenal tumours by using dynamic computed tomography images. Concerning this, the usage of one more imaging modalities and biopsy process is wanted to be excluded. The used dataset consists of 8 subtypes of adrenal tumours, and it seemed as the worst-case scenario in which all handicaps are available against tumour classification. Histogram, grey level co-occurrence matrix and wavelet-based features are investigated to reveal the most effective one on the identification of adrenal tumours. Binary classification is proposed utilising four-promising algorithms that have proven oneself on the task of binary-medical pattern classification. For this purpose, optimised neural networks are examined using six dataset inspired by the aforementioned features, and an efficient framework is offered before the use of a biopsy. Accuracy, sensitivity, specificity, and AUC are used to evaluate the performance of classifiers. Consequently, malign/benign characterisation is performed by proposed framework, with success rates of 80.7%, 75%, 82.22% and 78.61% for the metrics, respectively. Graphical abstract.

Adrenal tumor segmentation method for MR images.

BACKGROUND AND OBJECTIVE: Adrenal tumors, which occur on adrenal glands, are incidentally determined. The liver, spleen, spinal cord, and kidney surround the adrenal glands. Therefore, tumors on the adrenal glands can be adherent to other organs. This is a problem in adrenal tumor segmentation. In addition, low contrast, non-standardized shape and size, homogeneity, and heterogeneity of the tumors are considered as problems in segmentation. METHODS: This study proposes a computer-aided diagnosis (CAD) system to segment adrenal tumors by eliminating the above problems. The proposed hybrid method incorporates many image processing methods, which include active contour, adaptive thresholding, contrast limited adaptive histogram equalization (CLAHE), image erosion, and region growing. RESULTS: The performance of the proposed method was assessed on 113 Magnetic Resonance (MR) images using seven metrics: sensitivity, specificity, accuracy, precision, Dice Coefficient, Jaccard Rate, and structural similarity index (SSIM). The proposed method eliminates some of the discussed problems with success rates of 74.84%, 99.99%, 99.84%, 93.49%, 82.09%, 71.24%, 99.48% for the metrics, respectively. CONCLUSIONS: This study presents a new method for adrenal tumor segmentation, and avoids some of the problems preventing accurate segmentation, especially for cyst-based tumors.