João de Almeida Martins¹, Samo Lasic¹, Yuan Zheng², Qing Wei², Sirui Li³, Wenbo Sun³, Haibo Xu³, Karin Bryske¹, and Daniel Topgaard^1,4
1Random Walk Imaging, Lund, Sweden, ²United Imaging Healthcare, Shanghai, China, ³Zhongnan Hospital of Wuhan University, Wuhan, China, ⁴Lund University, Lund, Sweden

For voxels containing multiple cell or tissue types, DTI metrics are challenging to interpret in terms of specific microstructural properties. We address this problem by adapting encoding and inversion strategies from solid-state and low-field NMR to determine diffusion tensor distributions (DTDs) with dimensions corresponding to cell densities, shapes, and orientations. Three patients with glioma, meningioma, or cerebral cyst underwent 5 min DTD imaging giving 15 distinct parameter maps for quantitative analysis and intuitive microsctructural interpretation. The DTD-derived metrics showed good agreement with expected tissue properties and structural insights not accessible with DTI.

Yaoming Qu¹, Zhibo Wen¹, and Qin Qin^2,3
1Imaging diagnostic department, Zhujiang hospital of southern medical university, Guangzhou, China, ²The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ³F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States

Velocity-selective arterial spin labeling (VSASL) employing Fourier-transform based velocity-selective pulse trains is an emerging method for quantifying cerebral blood flow (CBF) with high sensitivity to perfusion signal. Its utility was assessed for glioma patients at 3T by a comparison between pseudo-continuous ASL with DSC-PWI. We demonstrated the existence of various and prolonged arterial transit time (ATT) in high-grade gliomas. Detecting by the dependence of the CBF based on Tmax, lesser sensitivity to ATT in VSASL was reported. VSASL showed great promise for accurate quantification of CBF and could potentially improve the diagnostic performance of ASL in preoperative grading of gliomas.

Kyu Sung Choi¹, Sung-Hye You², Yoseob Han¹, Jong Chul Ye¹, Seung Hong Choi³, and Bumseok Jeong^1
1Korea Advanced Institute for Science and Technology, Daejeon, Korea, Republic of, ²Korea University College of Medicine, Seoul, Korea, Republic of, ³Seoul National University Hospital, Seoul, Korea, Republic of

AIF_DCE has been known to be sensitive to noise, because of the relatively weak T1 contrast-enhanced MR signal intensity (SI) compared to the T2* SI of DSC-MRI, leading to PK parameters – K_trans, V_e, and _Vp – with low reliability. In this study, we developed a neural network model generating an AIF similar to the AIF obtained from DSC-MRI – AIF_{generated DSC} – and demonstrated that the accuracy and reliability of K_trans and V_e derived from AIF_{generated DSC} can be improved compared to those from AIF_DCE without obtaining DSC-MRI, not leading to an additional deposition of gadolinium in the brain.

Giulia Buizza¹, Chiara Paganelli¹, Lorenzo Preda², Francesca Valvo², Daniel C. Alexander³, Guido Baroni^1,2, and Marco Palombo^3
1CartCasLab at Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy, ²National Center Of Oncological Hadrontherapy (CNAO), Pavia, Italy, ³Centre for Medical Image Computing and Dept of Computer Science, University College London, London, United Kingdom

Tumour microstructure can be probed with diffusion-weighted MRI (DW-MRI), but the clinically-adopted apparent diffusion coefficient (ADC) lacks a clear link to microstructure. Aim of this work was to detail the ADC-microstructure relationship using a computational framework. Relying on a sparse representation of simulated DW-MRI data, we estimated diffusivity (D), cell radius (R) and volume fraction (vf) for 27 low and high-grade meningioma patients, which significantly differed in ADC, D and vf. Preliminary results showed the potential of the proposed framework for meningioma grading and proton-therapy response assessment, although extension to richer data and histological validation need to be further addressed.

Xiaobing Fan¹, Xueyan Zhou², Aritrick Chatterjee¹, Aytekin Oto¹, and Gregory S. Karczmar^1
1Radiology, The University of Chicago, Chicago, IL, United States, ²Harbin University, Harbin, China

We compared standard Tofts model with a two-tissue compartment model (2TCM) of dynamic contrast enhanced (DCE) MRI for diagnosis of prostate cancer. The 2TCM has one slow and one fast exchanging compartment. The standard Tofts model parameters (K^trans and k_ep) were compared with the 2TCM parameters (K_i^trans and kⁱ_ep, i=1,2). There was a strong correlation between K^trans and K₁^trans for cancer, but weak correlation between k_ep and k¹_ep. This demonstrated that the Tofts model often does not fit contrast agent concentration curves accurately, and the 2TCM can provide new diagnostic information with fewer false positives in diagnosis of prostate cancer.

Elisa Roccia¹, Radhouene Neji^1,2, Thomas Benkert³, Berthold Kiefer³, Vicky Goh⁴, and Isabel Dregely^1
1Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ²Siemens Healthcare Limited, Frimley, United Kingdom, ³Siemens Healthcare GmbH, Erlangen, Germany, ⁴Cancer Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

Diffusion imaging is a key contrast in assessing prostate cancer. However, current single-shot EPI-based techniques are often distorted and fundamentally limited in resolution. The aim of this study is to develop multi-shot diffusion-prepared gradient echo imaging to obtain accurate 3D ADC maps in the prostate. We developed a 3D Cartesian centric trajectory with self-navigation, and a shot rejection approach to correct for inter-shot magnitude errors. We used a custom dictionary of acquisition specific signal evolutions to estimate ADC. We have shown in simulations and in vivo that accurate ADC values could be recovered.

Yuko Someya¹, Mami Iima^1,2, Hirohiko Imai³, Akihiko Yoshizawa⁴, Yuji Nakamoto¹, Masako Kataoka¹, Hiroyoshi Isoda¹, and Kaori Togashi^1
1Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan, ²Clinical Innovative Medicine, Institute for Advancement of Clinical and Translational Science, Kyoto University Hospital, Kyoto, Japan, ³Kyoto University Graduate School of Informatics, Kyoto, Japan, ⁴Diagnostic Pathology, Kyoto University Graduate School of Medicine, Kyoto, Japan

The association of time-dependent DWI (shifted ADC [sADC], IVIM, and non-Gaussian DWI) at different diffusion times and CEST (MTR_asym, APT signal intensity) parameters was investigated with histological biomarkers in a breast cancer xenograft model. ADC values decreased with increased diffusion times. sADC values at a diffusion time=5ms had significant negative correlation with Ki-67 (r=−0.63, P<0.05). MTR_asym had a significant positive correlation with Ki-67 positive area (r=0.73, P<0.05). Significant association was found between f_IVIM and microvessel density (r=0.80, P<0.01). These results indicate their utility for investigating microstructure and microcirculation of breast cancers without using contrast agents.

Kine Mari Bakke^1,2, Sebastian Meltzer¹, Endre Grøvik³, Anne Negård^4,5, Stein Harald Holmedal⁴, Kjell-Inge Gjesdal⁴, Atle Bjørnerud^2,3, Anne Hansen Ree^1,5, and Kathrine Røe Redalen^6
1Department of Oncology, Akershus University Hospital, Lørenskog, Norway, ²Department of Physics, University of Oslo, Oslo, Norway, ³Department of Diagnostic Physics, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway, ⁴Department of Radiology, Akershus University Hospital, Lørenskog, Norway, ⁵Institute of Clinical Medicine, University of Oslo, Oslo, Norway, ⁶Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway

Three different MRI methods for obtaining perfusion related parameters were compared and evaluated as biomarkers in a study of 94 rectal cancer patients. The methods were dynamic contrast enhanced (DCE) MRI and dynamic susceptibility contrast (DSC) MRI analysed from a multi-echo dynamic EPI sequence, as well as intravoxel incoherent motion (IVIM) MRI analysed from a diffusion weighted sequence with 7 b-values. Tumour blood flow from DSC MRI was correlated to D^* from IVIM MRI as well as K^trans and v_p from DCE MRI. Blood flow was also related to progression free survival, overall survival, treatment response and sex differences. 

Jianbo Cao¹, Stephen Pickup¹, Peter O’Dwyer^2,3, Mark Rosen^1,3, and Rong Zhou^1,3
1Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States, ²Pancreatic Cancer Research Center, University of Pennsylvania, Philadelphia, PA, United States, ³Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, United States

Pancreatic ductal adenocarcinoma (PDA) is characterized by a dense stroma, which poses a substantial barrier to drug penetration and motivates development of stroma-directed interventions. We aim to test the utility of DCE-MRI to predict PDA responses to such treatment. We compared individual versus group-arterial input function approach and metric including K^trans, k_ep and V_p derived from three commonly used pharmacokinetic models. Our data provides rationale for choice of PK model and AIF approach which lead to quantitative DCE-MRI marker of optimal sensitivity and specificity for detection of PDA responses to human hyaluronidase that reduces PDA stroma.