{"title":"商用硬件上大型医疗数据集的高保真可视化","authors":"Luigi Gallo, Alessio Pierluigi Placitelli","doi":"10.1155/2013/892967","DOIUrl":null,"url":null,"abstract":"Recent advances in CT and MRI static and dynamic scanning techniques have led to great improvements in the resolution and size of volumetric medical datasets, and this trend is still ongoing. However, the explosion of dataset size prevents clinicians from taking advantage of an interactive, high-resolution exploration of volumetric medical data on commodity hardware, due to the memory constraints of modern graphics cards. This paper presents a hybrid CPU-GPU volume ray-casting method and some hybrid-based inspection tools aimed at providing interactive, medical-quality visualization using an ordinary desktop PC. Experimental results show that the hybrid method provides a near-interactive high-fidelity visualization of large medical datasets even if only limited hardware resources are available.","PeriodicalId":93456,"journal":{"name":"ISRN biomedical engineering","volume":"2013 1","pages":"1-9"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2013/892967","citationCount":"3","resultStr":"{\"title\":\"High-Fidelity Visualization of Large Medical Datasets on Commodity Hardware\",\"authors\":\"Luigi Gallo, Alessio Pierluigi Placitelli\",\"doi\":\"10.1155/2013/892967\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recent advances in CT and MRI static and dynamic scanning techniques have led to great improvements in the resolution and size of volumetric medical datasets, and this trend is still ongoing. However, the explosion of dataset size prevents clinicians from taking advantage of an interactive, high-resolution exploration of volumetric medical data on commodity hardware, due to the memory constraints of modern graphics cards. This paper presents a hybrid CPU-GPU volume ray-casting method and some hybrid-based inspection tools aimed at providing interactive, medical-quality visualization using an ordinary desktop PC. Experimental results show that the hybrid method provides a near-interactive high-fidelity visualization of large medical datasets even if only limited hardware resources are available.\",\"PeriodicalId\":93456,\"journal\":{\"name\":\"ISRN biomedical engineering\",\"volume\":\"2013 1\",\"pages\":\"1-9\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1155/2013/892967\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ISRN biomedical engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1155/2013/892967\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISRN biomedical engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2013/892967","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
High-Fidelity Visualization of Large Medical Datasets on Commodity Hardware
Recent advances in CT and MRI static and dynamic scanning techniques have led to great improvements in the resolution and size of volumetric medical datasets, and this trend is still ongoing. However, the explosion of dataset size prevents clinicians from taking advantage of an interactive, high-resolution exploration of volumetric medical data on commodity hardware, due to the memory constraints of modern graphics cards. This paper presents a hybrid CPU-GPU volume ray-casting method and some hybrid-based inspection tools aimed at providing interactive, medical-quality visualization using an ordinary desktop PC. Experimental results show that the hybrid method provides a near-interactive high-fidelity visualization of large medical datasets even if only limited hardware resources are available.