Approximately 7% of the 30.2 million MR exams performed in 2006 were abdominal MRIs, and the liver is by far the most common abdominal organ imaged using MR . When successful, lesion characterization by MR is exquisite and elegant, and the definitive nature of the information can aid treatment follow-up, stop unnecessary workups and prevent invasive biopsies. All too often, however, liver MR is unsuccessful because of patient difficulties with breath-holds, problems with timing or acquisition in the critical (and non-repeatable) timed post-contrast series of images, operator dependence, image artifacts, or differences in levels of certainty in interpretation between various radiologists arising from inherently qualitative interpretations. Weighted images are at best surrogates of the underlying parameter and often poorly reflect the parameters . Moreover, when quantitative information is available, it can outperform even expert readers of clinical contrasts . If the liver examination could be performed quickly during free-breathing, without relying on operator expertise, and could provide quantitative parameters which can be used to definitively diagnose disease, the impact on the diagnosis and treatment of liver diseases would be significant. We propose to leverage rapid imaging, parameter quantitation, and body MRI expertise in our group to provide a rapid, quantitative, high quality 3D exam in under 10 minutes. A standard liver MR exam consists of multiple scans highlighting different contrast mechanisms: T2 weighted scans without and with fat saturation, T1-weighted in- and opposed-phase gradient echo, diffusion images, and T1-weighted fat saturated 3D gradient echo scans pre- and at least 3 timed phases post-contrast). We will develop, optimize and validate methods to generate quantitative measurements of each mechanism, by developing quantitative 3D high resolution DCE perfusion, fat fraction, T1 and T2 mapping, and improved 3D diffusion mapping. The diagnostic value of the new protocol will then be extensively tested on patients with biopsy proven pathologies, and will be compared to the present clinical standard.
Despite exquisite soft tissue contrast and a massive impact on diagnosis, follow-up and treatment of liver lesions and disorders, liver MRI still has significat limitations due to complexity of scanning, inefficient image acquisition, qualitative rather than quantitative imaging, and complicated image interpretation. We propose to completely change liver MRI by providing free-breathing 3D high resolution perfusion, fat fraction, T1 and T2 mapping, and markedly improved, short breath-hold high resolution 3D diffusion mapping in which we overcome these limitations using the novel rapid image acquisition, reconstruction, registration, and analysis tools being developed by our team. The total time needed for this new exam will be less than 10 minutes. This exam will be extensively tested on patients after development to determine the clinical utility in comparison to the present clinical standard.
|Chen, Yong; Jiang, Yun; Pahwa, Shivani et al. (2016) MR Fingerprinting for Rapid Quantitative Abdominal Imaging. Radiology 279:278-86|
|Yang, Alice C; Kretzler, Madison; Sudarski, Sonja et al. (2016) Sparse Reconstruction Techniques in Magnetic Resonance Imaging: Methods, Applications, and Challenges to Clinical Adoption. Invest Radiol 51:349-64|
|Janowczyk, Andrew; Basavanhally, Ajay; Madabhushi, Anant (2016) Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology. Comput Med Imaging Graph :|
|Chen, Yong; Lee, Gregory R; Aandal, Gunhild et al. (2016) Rapid volumetric T1 mapping of the abdomen using three-dimensional through-time spiral GRAPPA. Magn Reson Med 75:1457-65|
|Antunes, Jacob; Viswanath, Satish; Rusu, Mirabela et al. (2016) Radiomics Analysis on FLT-PET/MRI for Characterization of Early Treatment Response in Renal Cell Carcinoma: A Proof-of-Concept Study. Transl Oncol 9:155-62|
|Wech, Tobias; Seiberlich, Nicole; Schindele, Andreas et al. (2016) Development of Real-Time Magnetic Resonance Imaging of Mouse Hearts at 9.4 Tesla--Simulations and First Application. IEEE Trans Med Imaging 35:912-20|
|Shiradkar, Rakesh; Podder, Tarun K; Algohary, Ahmad et al. (2016) Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI. Radiat Oncol 11:148|
|Litjens, Geert J S; Elliott, Robin; Shih, Natalie N C et al. (2016) Computer-extracted Features Can Distinguish Noncancerous Confounding Disease from Prostatic Adenocarcinoma at Multiparametric MR Imaging. Radiology 278:135-45|
|Romo-Bucheli, David; Janowczyk, Andrew; Gilmore, Hannah et al. (2016) Automated Tubule Nuclei Quantification and Correlation with Oncotype DX risk categories in ER+ Breast Cancer Whole Slide Images. Sci Rep 6:32706|
|Xu, Jun; Xiang, Lei; Liu, Qingshan et al. (2016) Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images. IEEE Trans Med Imaging 35:119-30|
Showing the most recent 10 out of 36 publications