Deformable mapping of MRI and MRSI data to TRUS images to guide prostate biopsy

将 MRI 和 MRSI 数据变形映射到 TRUS 图像以指导前列腺活检

• 批准号: 7472018
• 负责人: STEPHEN M PIZER
• 金额: $ 18.61万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-24 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7472018
• 关键词: Atlases; Biopsy; Class; Clinical; Data; Development; Image; Likelihood Functions; Localized; Magnetic Resonance; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy; Malignant Neoplasms; Maps; Methodology; Modeling; Needles; Normal tissue morphology; Patients; Pilot Projects; Placement; Probability; Procedures; Prostate; Rectum; Research; Risk; Shapes; Staging; System; Testing; Texture; Tissues; Training; Ultrasonography; base; cancer diagnosis; density; magnetic resonance spectroscopic imaging; novel; prototype; rectal

DESCRIPTION (provided by applicant): The most common procedure for prostate biopsy for cancer diagnosis and staging involves the introduction of small-gauge needles from the rectum under the guidance of trans-rectal ultrasound imaging (TRUS). In most cases TRUS differentiates poorly or not at all between normal tissue and cancer in the prostate and therefore provides minimal guidance for targeting localized regions at risk within the prostate. Our hypothesis is that this shortcoming can be overcome by methodology to deformably map a patient-specific atlas of suspicious tissue volumes within the prostate, built from pre-biopsy magnetic resonance (MR) and magnetic resonance spectroscopic (MRS) images, to TRUS images during biopsy to serve as targets for accurate needle placement. The near-term research described in this proposal involves pilot studies using a novel and especially effective class of statistically trainable deformable-shape model (SDSM) called m-reps to perform this mapping. The most common procedure for prostate biopsy for cancer diagnosis and staging involves the introduction of small-gauge needles from the rectum under the guidance of trans-rectal ultrasound imaging (TRUS). In most cases TRUS differentiates poorly or not at all between normal tissue and cancer in the prostate and therefore provides minimal guidance for targeting localized regions at risk within the prostate. The proposed methodology will overcome this shortcoming by mapping a patient-specific atlas of suspicious tissue volumes from pre-biopsy magnetic resonance (MR) and magnetic resonance spectroscopic (MRS) images to TRUS images during biopsy to serve as targets for accurate needle placement.

描述（由申请人提供）：用于癌症诊断和分期的前列腺活检的最常见程序涉及在经直肠超声成像（TRUS）引导下从直肠引入小号针。在大多数情况下，TRUS在前列腺中的正常组织和癌症之间的区别很差或根本没有区别，因此为靶向前列腺内的局部风险区域提供了最小的指导。我们的假设是，这个缺点可以通过方法学来克服，以可变形地映射前列腺内可疑组织体积的患者特异性图谱，从活检前的磁共振（MR）和磁共振波谱（MRS）图像构建到活检期间的TRUS图像，以作为准确的针放置的目标。本提案中描述的近期研究涉及使用一种新颖且特别有效的统计可训练变形模型（SDSM）（称为m-reps）进行这种映射的试点研究。用于癌症诊断和分期的前列腺活检的最常见程序涉及在经直肠超声成像（TRUS）的引导下从直肠引入小规格针。在大多数情况下，TRUS在前列腺中的正常组织和癌症之间的区别很差或根本没有区别，因此为靶向前列腺内的局部风险区域提供了最小的指导。所提出的方法将通过在活检期间将来自活检前磁共振（MR）和磁共振波谱（MRS）图像的可疑组织体积的患者特异性图谱映射到TRUS图像以用作准确的针放置的目标来克服该缺点。