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.
{"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}
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.
{"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}
Modulation transfer function (MTF) is a well known and widely accepted method for evaluating the spatial resolution of a digital radiographic imaging system. In the present study our aim was to evaluate the MTF obtained from CBCT and micro-CT images. A cylinder shaped phantom designed for slanted-edge method was scanned by a CBCT device at a 100 µm isometric voxel size and by a micro-CT device at a 20 µm isometric voxel size, simultaneously. The MTF curves were calculated and the mean spatial resolutions at 10% MTF were 3.33 + 0.29 lp/mm in the case of CBCT images and 13.35 + 2.47 lp/mm in the case of micro-CT images. The values showed a strong positive correlation regarding the CBCT and the micro-CT spatial resolution values, respectively. Our results suggests that CBCT imaging devices with a voxel size of 100 µm or below might aid the validation of fine anatomical structures and allowing the opportunity for reliable micromorphometric examinations
{"title":"Modulation transfer function evaluation of cone beam computed and microcomputed tomography by using slanted edge phantom","authors":"S. Bence","doi":"10.24294/MIPT.V0I0.1102","DOIUrl":"https://doi.org/10.24294/MIPT.V0I0.1102","url":null,"abstract":"Modulation transfer function (MTF) is a well known and widely accepted method for evaluating the spatial resolution of a digital radiographic imaging system. In the present study our aim was to evaluate the MTF obtained from CBCT and micro-CT images. A cylinder shaped phantom designed for slanted-edge method was scanned by a CBCT device at a 100 µm isometric voxel size and by a micro-CT device at a 20 µm isometric voxel size, simultaneously. The MTF curves were calculated and the mean spatial resolutions at 10% MTF were 3.33 + 0.29 lp/mm in the case of CBCT images and 13.35 + 2.47 lp/mm in the case of micro-CT images. The values showed a strong positive correlation regarding the CBCT and the micro-CT spatial resolution values, respectively. Our results suggests that CBCT imaging devices with a voxel size of 100 µm or below might aid the validation of fine anatomical structures and allowing the opportunity for reliable micromorphometric examinations","PeriodicalId":282599,"journal":{"name":"Medical Imaging Process & Technology","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134623573","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}
I am very glad to present to the readers of “Medical Imaging Process & Technology” journal the forthcoming second edition of our book “QSAR-mapping and SBGN-mapping for Biological Samples (Lecture Course and Special Practicum)”, which is actually an introduction to the novel microscopic imaging approaches for numerous biomedical applications. It is possible to imagine that imaging and morphology of biological structures is not a rigorous mapping of different “material points” and structures in the field of view (or ROI’s), because the living state of the sample is a complex of many spatiotemporal dynamic processes.
我非常高兴地向《医学成像过程与技术》杂志的读者们介绍我们即将出版的第二版《生物样品QSAR-mapping and SBGN-mapping (Lecture Course and Special Practicum)》，这本书实际上是对许多生物医学应用的新型显微成像方法的介绍。可以想象，生物结构的成像和形态学并不是视场(或ROI)中不同“物质点”和结构的严格映射，因为样品的生活状态是许多时空动态过程的综合体。
{"title":"Towards the second edition of the book “QSAR-mapping and SBGN-Mapping for Biological Samples (Lecture Course and Special Practicum)”","authors":"O. Gradov","doi":"10.24294/mipt.v1i2.1001","DOIUrl":"https://doi.org/10.24294/mipt.v1i2.1001","url":null,"abstract":"I am very glad to present to the readers of “Medical Imaging Process & Technology” journal the forthcoming second edition of our book “QSAR-mapping and SBGN-mapping for Biological Samples (Lecture Course and Special Practicum)”, which is actually an introduction to the novel microscopic imaging approaches for numerous biomedical applications. It is possible to imagine that imaging and morphology of biological structures is not a rigorous mapping of different “material points” and structures in the field of view (or ROI’s), because the living state of the sample is a complex of many spatiotemporal dynamic processes. ","PeriodicalId":282599,"journal":{"name":"Medical Imaging Process & Technology","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114298561","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}
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