No abstract available.
Humans

Purpose: The aim of this study was to investigate the change in signal sensitivity over different acquisition start times and optimize the scanning window to provide the maximal signal sensitivity of [1- 13C]pyruvate and its metabolic products, lactate and alanine, using spatially localized hyperpolarized 3D 13C magnetic resonance spectroscopic imaging (MRSI). Materials and Methods: We acquired 3D 13C MRSI data from the brain (n = 3), kidney (n = 3), and liver (n = 3) of rats using a 3T clinical scanner and a custom RF coil after the injection of hyperpolarized [1- 13C]pyruvate. For each organ, we obtained three consecutive 3D 13C MRSI datasets with different acquisition start times per animal from a total of three animals. The mean signal-to-noise ratios (SNRs) of pyruvate, lactate, and alanine were calculated and compared between different acquisition start times. Based on the SNRs of lactate and alanine, we identified the optimal acquisition start timing for each organ. Results: For the brain, the acquisition start time of 18 s provided the highest mean SNR of lactate. At 18 s, however, the lactate signal predominantly originated from not the brain, but the blood vessels; therefore, the acquisition start time of 22 s was recommended for 3D 13C MRSI of the rat brain. For the kidney, all three metabolites demonstrated the highest mean SNR at the acquisition start time of 32 s. Similarly, the acquisition start time of 22 s provided the highest SNRs for all three metabolites in the liver. Conclusion In this study, the acquisition start timing was optimized in an attempt to maximize metabolic signals in hyperpolarized 3D 13C MRSI examination with [1- 13C] pyruvate as a substrate. We investigated the changes in metabolic signal sensitivity in the brain, kidney, and liver of rats to establish the optimal acquisition start time for each organ. We expect the results from this study to be of help in future studies.

Timely analysis of imaging data is critical for diagnosis and decision-making for proper treatment strategy in the cases of ischemic stroke. Various efforts have been made to develop computer-assisted systems to improve the accuracy of stroke diagnosis and acute stroke triage. The widespread emergence of artificial intelligence technology has been integrated into the field of medicine. Artificial intelligence can play an important role in providing care to patients with stroke. In the past few decades, numerous studies have explored the use of machine learning and deep learning algorithms for application in the management of stroke. In this review, we will start with a brief introduction to machine learning and deep learning and provide clinical applications of machine learning and deep learning in various aspects of stroke management, including rapid diagnosis and improved triage, identifying large vessel occlusion, predicting time from stroke onset, automated ASPECTS (Alberta Stroke Program Early CT Score) measurement, lesion segmentation, and predicting treatment outcome. This work is focused on providing the current application of artificial intelligence techniques in the imaging of ischemic stroke, including MRI and CT.

Purpose: This study aimed to compare the effects of different normalization methods on radiomics features extracted from magnetic resonance imaging (MRI). Materials and Methods: Preoperative T1-contrast enhanced MRI data from 212 patients with meningiomas were obtained from two university hospitals. The tumors were segmented using 3D Slicer software, and the PyRadiomics framework was used to extract radiomics features. We developed four experiments to predict the histological grade of meningiomas prior to surgery. The first experiment was performed without normalization.The next three experiments used the StandardScaler, MinMaxScaler, and RobustScaler to normalize radiomics features. The PyCaret framework was used for feature selection and to explore an optimized machine learning model for predicting meningioma grades. The prediction models were trained and validated using data from the first hospital. External test data from the second hospital were used to test the performance of the final models. Results: Our testing results demonstrated that meningioma grade prediction performance depends highly on the choice of the normalization method. The RobustScaler demonstrated a higher level of accuracy and sensitivity than the other normalization methods. The area under the receiver operating characteristic curve and specificity of the RobustScaler method were comparable to those of no-normalization but higher than those of the Standard and MinMaxScaler methods. Conclusion The results of our study suggest that careful consideration of the normalization method may provide a way to optimize the experimental results.Keywords: Meningiomas; Radiomics features; Magnetic resonance ima