Medical Imaging Process & Technology最新文献

Pharmaceutical management of hemorrhagic stroke: Optimizing outcomes following intracranial hemorrhage evacuation 出血性卒中的药物治疗:颅内出血后的最佳结果

Medical Imaging Process & Technology Pub Date : 2023-10-10 DOI: 10.24294/mipt.v6i1.2276

Siddharth Shah, Brandon Lucke-Wold

{"title":"Pharmaceutical management of hemorrhagic stroke: Optimizing outcomes following intracranial hemorrhage evacuation","authors":"Siddharth Shah, Brandon Lucke-Wold","doi":"10.24294/mipt.v6i1.2276","DOIUrl":"https://doi.org/10.24294/mipt.v6i1.2276","url":null,"abstract":"Stroke can be mainly categorized into hemorrhagic and ischemic stroke. Intracerebral hemorrhage (ICH) is a subtype of hemorrhagic stroke that is caused due to unconstrained bleeding within the parenchyma of the brain. ICH is one of the major conditions that have a high rate of disease and a high rate of death in a given population. Risk factors for ICH emerged to be age, male gender, hypertension, and intake of alcohol in huge quantities. The frequency of ICH is increased where hypertension is mainly untreated. To improve the prognosis and outcomes of an ICH patient, we need to perform emergent evacuation of blood from the brain parenchyma and prevent edema formation while restricting further neuronal damage due to surgical intervention. Evidence-based guidelines exist for ICH and form the basis for a care framework. The pharmaceutical management of ICH from current literature includes an aggressive reduction in blood pressure, tranexamic acid use, and recombinant activated factor VII administration. In addition, advanced imaging, surgical evacuation of ICH, and minimally invasive surgery techniques for hematoma evacuation could provide great benefits to patients with a large ICH.","PeriodicalId":282599,"journal":{"name":"Medical Imaging Process & Technology","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136296364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

2D brain MRI image synthesis based on lightweight denoising diffusion probabilistic model 基于轻量去噪扩散概率模型的二维脑MRI图像合成

Medical Imaging Process & Technology Pub Date : 2023-10-09 DOI: 10.24294/mipt.v6i1.2518

Jincheng Peng, Guoyue Chen, Kazuki Saruta, Yuki Terata

{"title":"2D brain MRI image synthesis based on lightweight denoising diffusion probabilistic model","authors":"Jincheng Peng, Guoyue Chen, Kazuki Saruta, Yuki Terata","doi":"10.24294/mipt.v6i1.2518","DOIUrl":"https://doi.org/10.24294/mipt.v6i1.2518","url":null,"abstract":"In recent years, brain health has received increasing attention, but conventional acquisition of brain MRI (magnetic resonance imaging) images still suffer from issues such as missing data, artifacts, and high costs, which hinders research and diagnosis. With the application of deep learning in medical image synthesis, low-cost, efficient, and high-quality medical MRI synthesis techniques have become a prominent research focus and have gradually matured. However, traditional methods for synthesizing magnetic resonance imaging (MRI) mostly rely on generative adversarial networks, which require fine-tuning of parameters and learning rates to achieve stringent Nash equilibrium conditions, leading to problems such as gradient explosions and mode collapse. Building upon the latest research in synthetic models DDPM (denoising diffusion probabilistic model), we propose a novel approach for 2D brain MRI image synthesis based on a lightweight denoising diffusion probabilistic model. This method improves the attention module in the denoising diffusion probabilistic model to make it more lightweight. Additionally, we adopt the smooth L1 loss function as a replacement for the traditional mean absolute error (L1 loss) by comparing the error between the 2D brain MRI images with added noise and the real noise for training the model. Finally, we validate the proposed model on the MRI Brain Tumor Classification dataset, demonstrating that it achieves high-quality synthesis results while significantly reducing the parameter count of the DDPM model.","PeriodicalId":282599,"journal":{"name":"Medical Imaging Process & Technology","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135044020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Modulation transfer function evaluation of cone beam computed and microcomputed tomography by using slanted edge phantom 斜边影对锥束计算机和微计算机断层成像的调制传递函数评价

Medical Imaging Process & Technology Pub Date : 2019-03-25 DOI: 10.24294/MIPT.V0I0.1102

S. Bence

引用次数: 1

Towards the second edition of the book “QSAR-mapping and SBGN-Mapping for Biological Samples (Lecture Course and Special Practicum)” 《生物样品qsar -制图与sbgn -制图(讲座课程与专题实习)》第二版

Medical Imaging Process & Technology Pub Date : 2018-09-05 DOI: 10.24294/mipt.v1i2.1001

O. Gradov

引用次数: 0