Mohamed E. M. K. Abdelaziz¹, Libaihe Tian¹, Thomas Lottner², Simon Reiss², Klaus Düring³, Guang-Zhong Yang⁴, Michael Bock², and Burak Temelkuran^1
1Hamlyn Centre for Robotic Surgery, Imperial College London, London, United Kingdom, ²Dept. of Radiology, Medical Physics, Medical Center, University of Freiburg, Freiburg, Germany, ³MaRVis Interventional GmBH, Krün, Germany, ⁴Shanghai Jiao Tong University, Shanghai, China

MR-guided intravascular interventions often lack steerable devices due to potential RF heating of long metallic components. In this study, we present a novel 7F MR safe and passively visible steerable catheter. Real-time MR image guidance of this catheter was demonstrated in a vessel phantom in a clinical 3T MR scanner to evaluate the catheter’s mechanical efficacy and MR visibility. Small vascular structures such as the renal artery could be probed efficiently with the polymer-only based catheter. With the integrated iron markers it was visible in its entirety, and the limited artifact size allowed for the effective visualization of surrounding tissue.

Daniel Christopher Hoinkiss¹, Han Nijsink², Paul Borm³, Sabrina Haase¹, Jan Strehlow¹, Jurgen Futterer², and Torben Pätz^1
1Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany, ²Radboud University Medical Centre (Radboudumc), Nijmegen, Netherlands, ³Nano4imaging GmbH, Aachen, Germany

MR-guided endovascular interventions will enable widely distributed therapies to be performed without radiation. We present a workflow for interventional slice steering based on endovascular device tracking to increase the precision during MR-guided interventions. Merging real-time tracking information based on passive MRI markers with pre-calculated therapy planning allows the slice steering to automatically adjust itself to the predicted vessel pathways for smooth and accurate device monitoring.

Saikat Sengupta¹, Xinqiang Yan¹, Tamarya Hoyt², Anthony Gunderman³, and Yue Chen^3
1Department of Radiology, Vanderbilt University Institute of Imaging Science, Nashville, TN, United States, ²Department of Radiology, Vanderbilt University Medical Center, Nashville, TN, United States, ³Department of Mechanical Engineering, University of Arkansas, Fayetteville, AR, United States

Artifacts caused by large magnetic susceptibility differences between metallic probes and the surrounding tissue are a persistent problem in many interventional MRI applications. In previous work, we presented the concept, design and modeling of active shims for metallic needles. In this work, we present the experimental demonstration of recovery of metallic probe induced signal loss at 3 Tesla. Effective recovery of lost signal and correction of field inhomogeneity is shown across needle orientations and imaging sequences.

Caiyun Shi^1,2, Dong Liang^1,2,3, Zhilang Qiu¹, Xin Liu^1,2, Yanjie Zhu^1,2, and Haifeng Wang^1,2
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, ²Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China, ³Research Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, China

Susceptibility-based positive contrast MR imaging exhibits excellent efficacy for visualizing the MR compatible metallic devices, by taking advantage of their high magnetic susceptibility. In this work, a novel method is developed to realize the 3D susceptibility-based positive contrast MR imaging on in vivo experiments of one human patient with one tumour of the scapula. The method is based on a modified 3D SPACE  sequence and a PDF background field removal to achieve positive contrast imaging.

Xinqiang Yan^1,2, Steven P. Allen³, Craig H. Meyer³, and William A. Grissom^1,2,4
1Vanderbilt University Institute of Imaging Science, Nashville, TN, United States, ²Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States, ³Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States, ⁴Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States

Transcranial MR-guided focused ultrasound (tcMRgFUS) neurosurgery is a non-invasive treatment for essential tremor and many emerging applications. In FDA-approved Insightec tcMRgFUS system, however, RF reflections create curved dark bands in brain images. In this work, we reported the first B₁⁺ and temperature maps in a healthy volunteer with passive crossed wires to alleviate B₁⁺ dark bands and inhomogeneity in a tcMRgFUS system. Consistent with simulation results and previous experimental results in phantoms, the “propeller-beanie” antenna can significantly alleviate dark band artifacts in Insightec tcMRgFUS system, which improves temperature precision and may enable the use of diffusion imaging to monitor treatment. 

Dominik F. Bauer^1,2, Eva Oelschlegel^1,2, Alena-Kathrin Golla^1,2, Anne Adlung^1,2, Tom Russ^1,2, Ingo Hermann^1,2, Irène Brumer^1,2, Julian Rosenkranz³, Fabian Tollens⁴, Sven Clausen⁵, Philipp Aumüller⁵, Lothar R. Schad^1,2, Dominik Nörenberg⁴, and Frank G. Zöllner^1,2
1Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, ²Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, ³Fraunhofer Institute for Manufacturing Engineering and Automation, Project Group for Automation in Medicine and Biotechnology, Mannheim, Germany, ⁴Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Mannheim, Germany, ⁵Department of Radiation Oncology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany

We present an anthropomorphic pelvis phantom with lesions for transperineal and transrectal prostate needle interventions. The human-sized artificial pelvis includes bones, bladder, prostate with four lesions, urethra, arteries, veins and six lymph nodes embedded in ballistic gelatin. It is puncturable and shows realistic contrast in multiparametric MRI (mpMRI) as well as CT imaging. The phantom can be used for the evaluation, training and finetuning of multimodal prostatic intervention procedures, such as prostate brachytherapy and biopsy.

Maarten Terpstra^1,2, Matteo Maspero^1,2, Tom Bruijnen^1,2, Joost Verhoeff¹, Jan Lagendijk¹, and Cornelis A.T. van den Berg^1,2
1Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands, ²Computational Imaging Group for MR diagnostics & therapy, University Medical Center Utrecht, Utrecht, Netherlands

Motion estimation from MRI is important for image-guided radiotherapy. Specifically, for online adaptive MR-guided radiotherapy, the motion fields need to be available with high temporal resolution and a low latency.  To achieve the required speed, MR acquisition is generally heavily accelerated, which results in image artifacts. Previously we have presented a deep learning method for real-time motion estimation in 2D that is able to resolve image artifacts. Here, we extend this method to 3D by training on prospectively undersampled respiratory-resolved data showing that our method produces high-quality motion fields at R=30 and even generalizes to CT without retraining.

Olivier Jaubert^1,2, Javier Montalt-Tordera², Dan Knight^2,3, Gerry J. Coghlan^2,3, Simon Arridge¹, Jennifer Steeden², and Vivek Muthurangu^2
1Department of Computer Science, UCL, London, United Kingdom, ²Centre for Cardiovascular Imaging, UCL, London, United Kingdom, ³Department of Cardiology, Royal Free London NHS Foundation Trust, London, United Kingdom

MR guided catheterization requires both fast imaging and fast reconstruction techniques for interactive imaging. Recent deep learning methods outperformed classical iterative reconstructions with shorter reconstruction times. We propose a low latency framework relying on deep artefact suppression using a 2D residual U-Net with convolutional long short term memory layers trained on multiple orientations. The framework was demonstrated to reconstruct an interactively acquired bSSFP tiny golden angle radial sequence for catheter guidance. The proposed approach enabled real-time imaging (latency/network time=39/19ms) in 3 catheterized patients with promising image quality and reconstruction times.

Xinzhou Li^1,2, Yu-Hsiu Lee³, David S. Lu¹, Tsu-Chin Tsao³, and Holden H. Wu^1,2
1Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States, ²Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, ³Mechanical and Aerospace Engineering, University of California, Los Angeles, Los Angeles, CA, United States

Misalignment between the MRI scan plane and needle trajectory degrades visualization and localization of the needle. This may prolong procedure time and increase errors in MRI-guided interventions. By leveraging an accurate deep learning-based needle localization algorithm, this work proposed an automatic workflow to realign the MRI scan plane with the needle. A scan plane control module was implemented for scan parameter updates. In one degree-of-freedom needle insertion experiments, the automatic workflow accurately aligned the scan plane with the needle (orientation difference 1.9°) with processing time <2 sec.

Haonan Xiao¹, Tian Li¹, Jiang Zhang¹, Ruiyan Ni¹, Ge Ren¹, Yibao Zhang², Weiwei Liu², Weihu Wang², Hao Wu², Victor Lee³, Andy Cheung³, Hing-Chiu Chang³, and Jing Cai^1
1The Hong Kong Polytechnic University, Hong Kong, China, ²Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China, ³The University of Hong Kong, Hong Kong, China

We have developed and validated an ultra-quality 4D-MRI synthesis technique using deep learning-based deformable image registrations. The displacement vector fields between breathing frames were obtained from low-quality 4D-MRI. They were then applied to high-quality stationary T1, T2, and diffusion weighted images to generate ultra-quality 4D-MRI. The synthetic 4D-MRIs were verified in terms of tumor motion accuracy and image quality. All the motion errors were in a sub-voxel level, and the image quality was significantly improved. This technique holds great potential in volumetric tumor tracking with high accuracy.