EIGEN IMAGE FILTERING OF NMR IMAGE SEQUENCES

描述(改编自申请者摘要)：任何肿瘤，是否 无论是恶性的还是良性的，在坚硬的颅骨范围内 如果它继续扩大，以牺牲 大脑的体积需求。因此，重要的是能够 确定病变的位置、大小、生长速度和架构 用于诊断、治疗，最重要的是，用于确定 患者的预后。磁共振成像(MRI)已经证明 用来演示一个 肿瘤对周围解剖结构的影响。磁共振成像在诊断中的应用 分割和定量分析是当前感兴趣的领域 脑肿瘤的勾画，但其临床可行性尚未 已经建立了。一种图像处理技术(特征图像滤波) 已经被开发和报道了。这项技术有能力使用 在磁共振图像序列中存在的多参数信息 相同的解剖位置，将感兴趣的对象与其他对象分开 物体。因此，提高了它的可视化，更容易获得 物体的形态测量，以及重现性和 这些测量的准确性。这项提案的总体目标是 是为了确定这项技术在临床上的可行性 为了从正常脑组织中分割出脑肿瘤 对病变及其子区域(架构)的描述。这个 这项建议的目标可分为：1.)验证 分割技术：和2.)该技术在肿瘤中的应用 容量测量和引导活检取样。这项研究将是 使用交互式半自动版本的 特征图像过滤软件。这项技术的验证将是 通过将分割后的病变图像(特征图像)与 动物(大鼠)肿瘤模型的组织学图像及与 本征象与活检结果来自选定的临床病例。这个 特征图像技术将被用来分割病变，子区域 病变和病变之间的部分体积平均的区域 正常组织。这些特征图像将被用来确定体积 病变、其子区域和部分体积平均的区域， 并确定采集活检样本的空间坐标。这个 特征图像滤波技术的优点是它基于 可靠的数学证明，是唯一的线性滤波器 提供部分卷信息。能够对对象进行分段 并将图像分析技术应用于它们可以显著地 改进诊断、治疗计划和治疗评估 利用医学成像技术。

DESCRIPTION (Adapted from Applicant's Abstract): Any tumor, whether malignant or benign, within the rigid confines of the skull is potentially fatal if it continues to enlarge at the expense of the volumetric requirement of the brain. Thus, it is important to be able to determine the lesions location, size, growth rate, and architecture for diagnosis, treatment, and most importantly, for determining the prognosis for the patient. Magnetic resonance imaging (MRI) has proven to be useful in demonstrating the three-dimensional relationship of a tumor to surrounding anatomical structure. The use of MRI for segmentation and quantitative analysis is of current interest in delineation of brain tumors, however its clinical feasibility has not been established. An image processing technique (Eigenimage Filtering) has been developed and reported. This technique has the ability to use multiparameter information present in a sequence of MR images of the same anatomical site, to segment an object of interest from other objects. Thus, improving its visualization, the ease of obtaining morphological measurements of the object, and the reproducibility and accuracy of these measurements. The overall objective of this proposal is to establish the clinical feasibility of this technique to the segmentation of brain tumors from normal brain tissue for the purpose of delineation of the lesions and its sub regions (architecture). The objective of this proposal can be partitioned into; 1.) validation of the segmentation techniques: and 2.) application of the technique to tumor volume measurements and guiding biopsy sampling. The study will be conducted utilizing an interactive semi-automatic version of the Eigenimage Filtering software. The validation of the technique will be through the comparison of the segmented lesion images (eigenimages) with histology images of an animal (rat) tumor model and with comparison of the eigenimages with biopsy results from selected clinical cases. The eigenimage technique will be used to segment lesions, sub-regions of the lesions and regions of partial volume averaging between the lesion and normal tissue. These eigenimages will be used to determine the volume of the lesion, its sub regions, and regions of partial volume averaging, and to determine the spatial coordinates for taking biopsy samples. The advantage of the Eigenimage Filter technique is that it is based upon solid mathematical justification and is the only linear filter that provides partial volume information. The ability to segment objects of interest and apply image analysis techniques to them can significantly improve the diagnostic, treatment planning, and treatment evaluation utilizing medical imaging.