Fat quantification using whole-body magnetic resonance imaging (WB-MRI) in malignant bone disease

MRI越来越成为一种数据驱动的模式，这可以通过许多定量成像生物标志物的成功来理解。我相信MRI有潜力成为肿瘤学疾病管理的有力工具。这个博士研究生将，除了发展我作为一个科学家的能力，给我的科学基础和机会，是一个积极的和未来的参与者在这一变革性的变化发生在医疗保健.在皇家马斯登医院（RMH）/癌症研究所（ICR），在那里我作为一个MRI临床科学家工作，是一个主要的全身磁共振成像（WB-MRI）中心，在开发WB-MRI的定性和定量方法方面有着良好的记录。国家和国际指南建议骨髓瘤[1]和转移性前列腺癌[2]患者进行WB-MRI。WB-MRI包括扩散加权MRI（DW-MRI），其对骨病变的检测具有高灵敏度，并能够通过表观扩散系数（ADC）和脂肪水成像定量异常细胞，它提供了关于正常骨髓脂肪被肿瘤取代和治疗疾病中脂肪返回的信息。MRI检查通常包括使用相对脂肪分数（FFr）对脂肪含量的近似评估，使用来自两点T1加权脂肪-水成像的幅度图像中的信号进行量化。已证明FFr在评估骨髓瘤和转移性前列腺癌和乳腺癌方面具有临床实用性，并且在对治疗有反应的骨髓瘤患者中，FFr的变化可能先于ADC的变化[3]。没有商定的FFr阈值来识别活动、治疗或缓解病变。脂肪含量（质子密度脂肪分数或PDFF）的完全定量评估目前尚未使用FFr实现，其中包括偏倚来源（T1和T2* 弛豫时间、噪声、光谱模型的选择）[4]。骨髓中脂肪定量的临床应用也已在非恶性疾病中得到证实[5]。先前在肝脏MRI中的工作已经导致了用于肝脏中PDFF估计的成功临床工具的开发，其消除了这些偏差并且在所有主要制造商的MRI扫描仪上可用。尚未评估改善恶性骨疾病脂肪定量的潜力。因此，有一个临床需要开发工具，以提供PDFF估计在WB-MRI恶性骨疾病，并评估性能评估骨病变和responsibility.Currently，我的工作的准确性和重复性的技术评估的最后阶段的各种狄克逊技术在盆腔转移性前列腺癌患者。这个项目的发现和局限性作为一个跳板的工作，这将是这个学生的一部分：主要建议的目的1。确定治疗和未治疗病变以及外观正常的骨髓中的1H光谱模型。2.评估脂肪定量技术对治疗前和治疗后测量的敏感性。3.确定PDFF中区分反应性和非反应性病变的阈值

MRI is increasingly a data-driven modality, as can be appreciated by the success of numerous quantitative imaging biomarkers. I believe MRI has the potential to be a powerful metrological tool for disease management in oncology. This PhD studentship would, beyond developing my abilities as a scientist, give me the scientific grounding and opportunity to be an active and future participant in this transformative change happening within healthcare.The Royal Marsden Hospital (RMH) / Institute of Cancer Research (ICR), where I work as an MRI clinical scientist, is a major centre for whole-body MRI (WB-MRI) with a strong track-record in development of qualitative and quantitative methods for WB-MRI. National and international guidelines recommend WB-MRI in patients with myeloma [1] and metastatic prostate cancer [2]. WB-MRI includes diffusion-weighted MRI (DW-MRI), which has high sensitivity for detection of bone lesions as well as enabling quantification of abnormal cellularity via the apparent diffusion coefficient (ADC), and fat-water imaging, which provides information about the displacement of normal marrow fat by tumours and return of fat in treated disease.WB-MRI exams usually include an approximate assessment of fat content using the relative fat fraction (FFr), quantified using the signal in magnitude images from two-point T1-weighted fat-water imaging. FFr has demonstrated clinical utility in assessment of myeloma and metastatic prostate and breast cancers, and changes in FFr may precede changes in ADC in patients with myeloma who respond to treatment [3]. There are no agreed thresholds in FFr to identify active, treated, or responding lesions. Fully quantitative assessment of fat content (proton density fat fraction, or PDFF) is not currently achieved using FFr, which includes sources of bias (T1 and T2* relaxation times, noise, choice of spectral model) [4]. Clinical applications of fat quantitation in bone marrow have also been demonstrated in non-malignant diseases [5]. Previous work in liver MRI has led to the development of successful clinical tools for PDFF estimation in liver, which remove these biases and are available on MRI scanners from all main manufacturers. The potential for improving fat quantification in malignant bone disease has not been assessed. There is, therefore, a clinical need to develop tools to provide PDFF estimation in WB-MRI for malignant bone disease, and to assess performance in assessment of bone lesions and response to treatment.Currently, I'm working on the final stages of a technical assessment of accuracy and repeatability of various Dixon techniques in the pelvis for patients with metastatic prostate cancer. This project's findings and limitations serve as a springboard for the work that would be a part of this studentship:Main Proposal Aims1. Determine the 1H spectral model in treated and untreated lesions and normal-appearing marrow. 2. Assess the sensitivity of fat quantification techniques to pre- and post-treatment measurements. 3. Determine a threshold in PDFF that distinguishes responding from non-responding lesions