A non-uniform quantization scheme for visualization of CT images

Epidemiology, characteristics, and prognostic factors of lymphoplasmacyte-rich meningioma: a systematic literature review

BACKGROUNDS

Lymphoplasmacyte-rich meningioma(LPM) is a rare subtype of meningioma with a low degree of malignancy and an overall preferable prognosis. The purpose of this article is to increase the understanding of the disease, reduce misdiagnosis, and improve prognosis.

METHODS

A search was conducted in the PubMed database for English articles published from 1993 to 2023. The keywords were "lymphoplasmacyte-rich (all fields) and meningioma (all fields) and English (lang)" and "lymphoplasmacyte-rich meningioma (title/abstract) and English (lang)".We further analyzed the clinical manifestations, imaging manifestations, pathological features, treatment strategies, and prognosis of LPM.The possible prognostic indicators were analyzed by the log-rank test and Pearson's chi-squared test.

RESULTS

Fourteen reports with 95 LPM patients were included in this report, including 47 males and 48 females who were diagnosed between the ages of 9 and 79, with an average age of 45 years. The most common clinical manifestations are headache and limb movement disorders. In most cases, the tumor occurred on the convex portion of the brain. All tumors showed significant enhancement, with homogeneous enhancement being more common, and most patients showed peritumoral edema. Postoperative pathological EMA, LCA, and vimentin positivity were helpful for the final diagnosis of the patient. Log-rank tests showed a correlation between complete resection and better prognosis and recurrence.

CONCLUSION

There is a lack of significant differences in the clinical symptoms and imaging manifestations of LPM compared to other diseases that need to be differentiated, and a clear diagnosis requires pathological examination. After standardized surgical treatment, the recurrence rate and mortality rate of LPM are both low. Complete surgical resection of tumors is associated with a better prognosis and lower recurrence rate.

Optimization Algorithms and Machine Learning Techniques in Medical Image Analysis

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Variance-aware weight quantization of multi-level resistive switching devices based on Pt/LaAlO3/SrTiO3 heterostructures

Resistive switching devices have been regarded as a promising candidate of multi-bit memristors for synaptic applications. The key functionality of the memristors is to realize multiple non-volatile conductance states with high precision. However, the variation of device conductance inevitably causes the state-overlap issue, limiting the number of available states. The insufficient number of states and the resultant inaccurate weight quantization are bottlenecks in developing practical memristors. Herein, we demonstrate a resistive switching device based on Pt/LaAlO₃/SrTiO₃ (Pt/LAO/STO) heterostructures, which is suitable for multi-level memristive applications. By redistributing the surface oxygen vacancies, we precisely control the tunneling of two-dimensional electron gas (2DEG) through the ultrathin LAO barrier, achieving multiple and tunable conductance states (over 27) in a non-volatile way. To further improve the multi-level switching performance, we propose a variance-aware weight quantization (VAQ) method. Our simulation studies verify that the VAQ effectively reduces the state-overlap issue of the resistive switching device. We also find that the VAQ states can better represent the normal-like data distribution and, thus, significantly improve the computing accuracy of the device. Our results provide valuable insight into developing high-precision multi-bit memristors based on complex oxide heterostructures for neuromorphic applications.

A comparative study of medical image enhancement algorithms and quality assessment metrics on COVID-19 CT images

Medical imaging can help doctors in better diagnosis of several conditions. During the present COVID-19 pandemic, timely detection of novel coronavirus is crucial, which can help in curing the disease at an early stage. Image enhancement techniques can improve the visual appearance of COVID-19 CT scans and speed-up the process of diagnosis. In this study, we analyze some state-of-the-art image enhancement techniques for their suitability in enhancing the CT scans of COVID-19 patients. Six quantitative metrics, Entropy, SSIM, AMBE, PSNR, EME, and EMEE, are used to evaluate the enhanced images. Two experienced radiologists were involved in the study to evaluate the performance of the enhancement techniques and the quantitative metrics used to assess them.