Isaiah Norton, Walid Ibn Essayed, Fan Zhang, Sonia Pujol, Alex Yarmarkovich, Alexandra J Golby, Gordon Kindlmann, Demian Wasserman, Raul San Jose Estepar, Yogesh Rathi, Steve Pieper, Ron Kikinis, Hans J Johnson, Carl-Fredrik Westin, and Lauren J O'Donnell. 2017. “SlicerDMRI: Open Source Diffusion MRI Software for Brain Cancer Research.” Cancer Res, 77, 21, Pp. e101-e103.Abstract
Diffusion MRI (dMRI) is the only noninvasive method for mapping white matter connections in the brain. We describe SlicerDMRI, a software suite that enables visualization and analysis of dMRI for neuroscientific studies and patient-specific anatomic assessment. SlicerDMRI has been successfully applied in multiple studies of the human brain in health and disease, and here, we especially focus on its cancer research applications. As an extension module of the 3D Slicer medical image computing platform, the SlicerDMRI suite enables dMRI analysis in a clinically relevant multimodal imaging workflow. Core SlicerDMRI functionality includes diffusion tensor estimation, white matter tractography with single and multi-fiber models, and dMRI quantification. SlicerDMRI supports clinical DICOM and research file formats, is open-source and cross-platform, and can be installed as an extension to 3D Slicer ( More information, videos, tutorials, and sample data are available at Cancer Res; 77(21); e101-3. ©2017 AACR.
K Fischer, S Ohori, FC Meral, M Uehara, S Giannini, T Ichimura, RN Smith, FA Jolesz, I Guleria, Y. Zhang, PJ White, NJ McDannold, K Hoffmeister, MM Givertz, and R Abdi. 2017. “Testing the Efficacy of Contrast-Enhanced Ultrasound in Detecting Transplant Rejection using a Murine Model of Heart Transplantation.” Am J Transplant, 17, 7, Pp. 1791-1801.Abstract
One of the key unmet needs to improve long-term outcomes of heart transplantation is to develop accurate, noninvasive, and practical diagnostic tools to detect transplant rejection. Early intragraft inflammation and endothelial cell injuries occur prior to advanced transplant rejection. We developed a novel diagnostic imaging platform to detect early declines in microvascular perfusion (MP) of cardiac transplants using contrast-enhanced ultrasonography (CEUS). The efficacy of CEUS in detecting transplant rejection was tested in a murine model of heart transplants, a standard preclinical model of solid organ transplant. As compared to the syngeneic groups, a progressive decline in MP was demonstrated in the allografts undergoing acute transplant rejection (40%, 64%, and 92% on days 4, 6, and 8 posttransplantation, respectively) and chronic rejection (33%, 33%, and 92% on days 5, 14, and 30 posttransplantation, respectively). Our perfusion studies showed restoration of MP following antirejection therapy, highlighting its potential to help monitor efficacy of antirejection therapy. Our data suggest that early endothelial cell injury and platelet aggregation contributed to the early MP decline observed in the allografts. High-resolution MP mapping may allow for noninvasive detection of heart transplant rejection. The data presented have the potential to help in the development of next-generation imaging approaches to diagnose transplant rejection.
Dimitris Mitsouras, Thomas C Lee, Peter Liacouras, Ciprian N Ionita, Todd Pietilla, Stephan E. Maier, and Robert V. Mulkern. 2017. “Three-dimensional Printing of MRI-visible Phantoms and MR Image-guided Therapy Simulation.” Magn Reson Med, 77, 2, Pp. 613-22.Abstract

PURPOSE: To demonstrate the use of anatomic MRI-visible three-dimensional (3D)-printed phantoms and to assess process accuracy and material MR signal properties. METHODS: A cervical spine model was generated from computed tomography (CT) data and 3D-printed using an MR signal-generating material. Printed phantom accuracy and signal characteristics were assessed using 120 kVp CT and 3 Tesla (T) MR imaging. The MR relaxation rates and diffusion coefficient of the fabricated phantom were measured and (1) H spectra were acquired to provide insight into the nature of the proton signal. Finally, T2 -weighted imaging was performed during cryoablation of the model. RESULTS: The printed model produced a CT signal of 102 ± 8 Hounsfield unit, and an MR signal roughly 1/3(rd) that of saline in short echo time/short repetition time GRE MRI (456 ± 36 versus 1526 ± 121 arbitrary signal units). Compared with the model designed from the in vivo CT scan, the printed model differed by 0.13 ± 0.11 mm in CT, and 0.62 ± 0.28 mm in MR. The printed material had T2 ∼32 ms, T2*∼7 ms, T1 ∼193 ms, and a very small diffusion coefficient less than olive oil. MRI monitoring of the cryoablation demonstrated iceball formation similar to an in vivo procedure. CONCLUSION: Current 3D printing technology can be used to print anatomically accurate phantoms that can be imaged by both CT and MRI. Such models can be used to simulate MRI-guided interventions such as cryosurgeries. Future development of the proposed technique can potentially lead to printed models that depict different tissues and anatomical structures with different MR signal characteristics. 

Mohsen Ghafoorian, Alireza Mehrtash, Tina Kapur, Nico Karssemeijer, Elena Marchiori, Mehran Pesteie, Charles RG Guttmann, Frank-Erik de Leeuw, Clare MC Tempany, Bram van Ginneken, Andriy Fedorov, Purang Abolmaesumi, Bram Plate, and William M Wells. 2017. “Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation.” Int Conf Med Image Comput Comput Assist Interv 20 (Pt3), Pp. 516-24.Abstract
Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis and treatment. However, variations in MRI acquisition protocols result in different appearances of normal and diseased tissue in the images. Convolutional neural networks (CNNs), which have shown to be successful in many medical image analysis tasks, are typically sensitive to the variations in imaging protocols. Therefore, in many cases, networks trained on data acquired with one MRI protocol, do not perform satisfactorily on data acquired with different protocols. This limits the use of models trained with large annotated legacy datasets on a new dataset with a different domain which is often a recurring situation in clinical settings. In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, (1) How much data from the new domain is required for a decent adaptation of the original network?; and, (2) What portion of the pre-trained model parameters should be retrained given a certain number of the new domain training samples? To address these questions, we conducted extensive experiments in white matter hyperintensity segmentation task. We trained a CNN on legacy MR images of brain and evaluated the performance of the domain-adapted network on the same task with images from a different domain. We then compared the performance of the model to the surrogate scenarios where either the same trained network is used or a new network is trained from scratch on the new dataset. The domain-adapted network tuned only by two training examples achieved a Dice score of 0.63 substantially outperforming a similar network trained on the same set of examples from scratch.
Ghafoorian MICCAI 2017
Walid I Essayed, Fan Zhang, Prashin Unadkat, Rees G Cosgrove, Alexandra J Golby, and Lauren J O'Donnell. 2017. “White Matter Tractography for Neurosurgical Planning: A Topography-based Review of the Current State of the Art.” Neuroimage Clin, 15, Pp. 659-72.Abstract
We perform a review of the literature in the field of white matter tractography for neurosurgical planning, focusing on those works where tractography was correlated with clinical information such as patient outcome, clinical functional testing, or electro-cortical stimulation. We organize the review by anatomical location in the brain and by surgical procedure, including both supratentorial and infratentorial pathologies, and excluding spinal cord applications. Where possible, we discuss implications of tractography for clinical care, as well as clinically relevant technical considerations regarding the tractography methods. We find that tractography is a valuable tool in variable situations in modern neurosurgery. Our survey of recent reports demonstrates multiple potentially successful applications of white matter tractography in neurosurgery, with progress towards overcoming clinical challenges of standardization and interpretation.
Pelin Aksit Ciris, Cheng-Chieh Cheng, Chang-Sheng Mei, Lawrence P. Panych, and Bruno Madore. 2017. “Dual-Pathway Sequences for MR Thermometry: When and Where to use Them.” Magn Reson Med, 77, 3, Pp. 1193-200.Abstract

PURPOSE: Dual-pathway sequences have been proposed to help improve the temperature-to-noise ratio (TNR) in MR thermometry. The present work establishes how much of an improvement these so-called "PSIF-FISP" sequences may bring in various organs and tissues. METHODS: Simulations and TNR calculations were validated against analytical equations, phantom, abdomen, and brain scans. Relative TNRs for PSIF-FISP, as compared to a dual-FISP reference standard, were calculated for flip angle (FA) = 1 to 85 º and repetition time (TR) = 6 to 60 ms, for gray matter, white matter, cervix, endometrium, myometrium, prostate, kidney medulla and cortex, bone marrow, pancreas, spleen, muscle, and liver tissues. RESULTS: PSIF-FISP was TNR superior in the kidney, pelvis, spleen, or gray matter at most tested TR and FA settings, and benefits increased at shorter TRs. PSIF-FISP was TNR superior in other tissues, e.g., liver, muscle, pancreas, for only short TR settings (20 ms or less). The TNR benefits of PSIF-FISP increased slightly with FA, and strongly with decreasing TR. Up to two- to three-fold reductions in TR with 20% TNR gains were achievable. In any given tissue, TNR performance is expected to further improve with heating, due to changes in relaxation rates. CONCLUSION: Dual-pathway PSIF-FISP can improve TNR and acquisition speed over standard gradient-recalled echo sequences, but optimal acquisition parameters are tissue dependent. Magn Reson Med 77:1193-1200, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Tzu-Cheng Chao, Jr-yuan George Chiou, Stephan E. Maier, and Bruno Madore. 2017. “Fast Diffusion Imaging with High Angular Resolution.” Magn Reson Med, 77, 2, Pp. 696-706.Abstract

PURPOSE: High angular resolution diffusion imaging (HARDI) is a well-established method to help reveal the architecture of nerve bundles, but long scan times and geometric distortions inherent to echo planar imaging (EPI) have limited its integration into clinical protocols. METHODS: A fast imaging method is proposed here that combines accelerated multishot diffusion imaging (AMDI), multiplexed sensitivity encoding (MUSE), and crossing fiber angular resolution of intravoxel structure (CFARI) to reduce spatial distortions and reduce total scan time. A multishot EPI sequence was used to improve geometrical fidelity as compared to a single-shot EPI acquisition, and acceleration in both k-space and diffusion sampling enabled reductions in scan time. The method is regularized and self-navigated for motion correction. Seven volunteers were scanned in this study, including four with volumetric whole brain acquisitions. RESULTS: The average similarity of microstructural orientations between undersampled datasets and their fully sampled counterparts was above 85%, with scan times below 5 min for whole-brain acquisitions. Up to 2.7-fold scan time acceleration along with four-fold distortion reduction was achieved. CONCLUSION: The proposed imaging strategy can generate HARDI results with relatively good geometrical fidelity and low scan duration, which may help facilitate the transition of HARDI from a successful research tool to a practical clinical one. Magn Reson Med, 2016. © 2016 Wiley Periodicals, Inc.

Mao Li, Karol Miller, Grand Roman Joldes, Ron Kikinis, and Adam Wittek. 2016. “Biomechanical Model for Computing Deformations for Whole-body Image Registration: A Meshless Approach.” Int J Numer Method Biomed Eng, 32, 12.Abstract

Patient-specific biomechanical models have been advocated as a tool for predicting deformations of soft body organs/tissue for medical image registration (aligning two sets of images) when differences between the images are large. However, complex and irregular geometry of the body organs makes generation of patient-specific biomechanical models very time-consuming. Meshless discretisation has been proposed to solve this challenge. However, applications so far have been limited to 2D models and computing single organ deformations. In this study, 3D comprehensive patient-specific nonlinear biomechanical models implemented using meshless Total Lagrangian explicit dynamics algorithms are applied to predict a 3D deformation field for whole-body image registration. Unlike a conventional approach that requires dividing (segmenting) the image into non-overlapping constituents representing different organs/tissues, the mechanical properties are assigned using the fuzzy c-means algorithm without the image segmentation. Verification indicates that the deformations predicted using the proposed meshless approach are for practical purposes the same as those obtained using the previously validated finite element models. To quantitatively evaluate the accuracy of the predicted deformations, we determined the spatial misalignment between the registered (i.e. source images warped using the predicted deformations) and target images by computing the edge-based Hausdorff distance. The Hausdorff distance-based evaluation determines that our meshless models led to successful registration of the vast majority of the image features. Copyright © 2016 John Wiley & Sons, Ltd.

Pelin A Ciris, Mukund Balasubramanian, Antonio L Damato, Ravi T Seethamraju, Clare M Tempany-Afdhal, Robert V. Mulkern, and Akila N Viswanathan. 2016. “Characterizing Gradient Echo Signal Decays in Gynecologic Cancers at 3T using a Gaussian Augmentation of the Monoexponential (GAME) Model.” J Magn Reson Imaging, 44, 4, Pp. 1020-30.Abstract

PURPOSE: To assess whether R2* mapping with a standard Monoexponential (ME) or a Gaussian Augmentation of the Monoexponential (GAME) decay model better characterizes gradient-echo signal decays in gynecological cancers after external beam radiation therapy at 3T, and evaluate implications of modeling for noninvasive identification of intratumoral hypoxia. MATERIALS AND METHODS: Multi-gradient-echo signals were acquired on 25 consecutive patients with gynecologic cancers and three healthy participants during inhalation of different oxygen concentrations at 3T. Data were fitted with both ME and GAME models. Models were compared using F-tests in tumors and muscles in patients, muscles, cervix, and uterus in healthy participants, and across oxygenation levels. RESULTS: GAME significantly improved fitting over ME (P < 0.05): Improvements with GAME covered 34% of tumor regions-of-interest on average, ranging from 6% (of a vaginal tumor) to 68% (of a cervical tumor) in individual tumors. Improvements with GAME were more prominent in areas that would be assumed hypoxic based on ME alone, reaching 90% as ME R2* approached 100 Hz. Gradient echo decay parameters at different oxygenation levels were not significantly different (P = 0.81). CONCLUSION: R2* may prove sensitive to hypoxia; however, inaccurate representations of underlying data may limit the success of quantitative assessments. Although the degree to which R2 or σ values correlate with hypoxia remains unknown, improved characterization with GAME increases the potential for determining any correlates of fit parameters with biomarkers, such as oxygenation status. J. MAGN. RESON. IMAGING 2016;44:1020-1030.

Jeffrey P Guenette, Nathan Himes, Andreas A Giannopoulos, Tatiana Kelil, Dimitris Mitsouras, and Thomas C Lee. 2016. “Computer-Based Vertebral Tumor Cryoablation Planning and Procedure Simulation Involving Two Cases using MRI-Visible 3D Printing and Advanced Visualization.” AJR Am J Roentgenol, 207, 5, Pp. 1128-31.Abstract

OBJECTIVE: We report the development and use of MRI-compatible and MRI-visible 3D printed models in conjunction with advanced visualization software models to plan and simulate safe access routes to achieve a theoretic zone of cryoablation for percutaneous image-guided treatment of a C7 pedicle osteoid osteoma and an L1 lamina osteoblastoma. Both models altered procedural planning and patient care. CONCLUSION: Patient-specific MRI-visible models can be helpful in planning complex percutaneous image-guided cryoablation procedures.

Melissa Anne Mallory, Katya Losk, Kristen Camuso, Stephanie Caterson, Suniti Nimbkar, and Mehra Golshan. 2016. “Does "Two is Better Than One" Apply to Surgeons? Comparing Single-Surgeon Versus Co-surgeon Bilateral Mastectomies.” Ann Surg Oncol, 23, 4, Pp. 1111-6.Abstract
BACKGROUND: Bilateral mastectomies (BM) are traditionally performed by single surgeons (SS); a co-surgeon (CS) technique, where each surgeon concurrently performs a unilateral mastectomy, offers an alternative approach. We examined differences in general surgery time (GST), overall surgery time (OST), and patient complications for BM performed by CS and SS. METHODS: Patients undergoing BM with tissue expander reconstruction (BMTR) between January 2010 and May 2014 at our center were identified through operative case logs. GST (incision to end of BM procedure), reconstruction duration (RST) (plastic surgery start to end of reconstruction) and OST (OST = GST + RST) was calculated. Patient age, presence/stage of cancer, breast weight, axillary procedure performed, and 30-day postoperative complications were extracted from medical records. Differences in GST and OST between CS and SS cases were assessed with a t test. A multivariate linear regression was fit to identify factors associated with GST. RESULTS: A total of 116 BMTR cases were performed [CS, n = 67 (57.8 %); SS, n = 49 (42.2 %)]. Demographic characteristics did not differ between groups. GST and OST were significantly shorter for CS cases, 75.8 versus 116.8 min, p < .0001, and 255.2 versus 278.3 min, p = .005, respectively. Presence of a CS significantly reduces BMTR time (β = -38.82, p < .0001). Breast weight (β = 0.0093, p = .03) and axillary dissection (β = 28.69, p = .0003) also impacted GST. CONCLUSIONS: The CS approach to BMTR reduced both GST and OST; however, the degree of time savings (35.1 and 8.3 %, respectively) was less than hypothesized. A larger study is warranted to better characterize time, cost, and outcomes of the CS-approach for BM.
Fan Zhang, Peter Savadjev, Weidong Cai, Yang Song, Ragini Verma, Carl-Fredrik Westin, and Lauren J O'Donnell. 2016. “Fiber Clustering Based White Matter Connectivity Analysis for Prediction of Autism Spectrum Disorder using Diffusion Tensor Imaging.” In IEEE International Symposium on Biomedical Imaging, Pp. 564-7.Abstract

Autism Spectrum Disorder (ASD) has been suggested to associate with alterations 
in brain connectivity. In this study, we focus on a fiber clustering tractography segmentation 
strategy to observe white matter connectivity alterations in ASD. Compared to another 
popular parcellation-based approach for tractography segmentation based on cortical 
regions, we hypothesized that the clustering-based method could provide a more 
anatomically correspondent division of white matter. We applied this strategy to conduct a population-based group statistical analysis for the automated prediction of ASD. We obtained a maximum classification accuracy of 81.33% be- tween ASDs and controls, compared to the results of 78.00% from the parcellation-based method.

Zhang ISBI 2016 Paper
Frank Preiswerk, Matthew Toews, Cheng-Chieh Cheng, Jr-yuan George Chiou, Chang-Sheng Mei, Lena F Schaefer, W. Scott Hoge, Benjamin M Schwartz, Lawrence P Panych, and Bruno Madore. 2016. “Hybrid MRI Ultrasound Acquisitions, and Scannerless Real-time Imaging.” Magn Reson Med, 78, 3, Pp. 897-908.Abstract

PURPOSE: To combine MRI, ultrasound, and computer science methodologies toward generating MRI contrast at the high frame rates of ultrasound, inside and even outside the MRI bore. METHODS: A small transducer, held onto the abdomen with an adhesive bandage, collected ultrasound signals during MRI. Based on these ultrasound signals and their correlations with MRI, a machine-learning algorithm created synthetic MR images at frame rates up to 100 per second. In one particular implementation, volunteers were taken out of the MRI bore with the ultrasound sensor still in place, and MR images were generated on the basis of ultrasound signal and learned correlations alone in a "scannerless" manner. RESULTS: Hybrid ultrasound-MRI data were acquired in eight separate imaging sessions. Locations of liver features, in synthetic images, were compared with those from acquired images: The mean error was 1.0 pixel (2.1 mm), with best case 0.4 and worst case 4.1 pixels (in the presence of heavy coughing). For results from outside the bore, qualitative validation involved optically tracked ultrasound imaging with/without coughing. CONCLUSION: The proposed setup can generate an accurate stream of high-speed MR images, up to 100 frames per second, inside or even outside the MR bore. Magn Reson Med, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

Terry M Peters and Cristian A Linte. 2016. “Image-guided interventions and computer-integrated therapy: Quo vadis?” Med Image Anal, 33, Pp. 56-63.Abstract
Significant efforts have been dedicated to minimizing invasiveness associated with surgical interventions, most of which have been possible thanks to the developments in medical imaging, surgical navigation, visualization and display technologies. Image-guided interventions have promised to dramatically change the way therapies are delivered to many organs. However, in spite of the development of many sophisticated technologies over the past two decades, other than some isolated examples of successful implementations, minimally invasive therapy is far from enjoying the wide acceptance once envisioned. This paper provides a large-scale overview of the state-of-the-art developments, identifies several barriers thought to have hampered the wider adoption of image-guided navigation, and suggests areas of research that may potentially advance the field.
Tina Kapur, Steve Pieper, Andriy Fedorov, J-C Fillion-Robin, Michael Halle, Lauren O'Donnell, Andras Lasso, Tamas Ungi, Csaba Pinter, Julien Finet, Sonia Pujol, Jayender Jagadeesan, Junichi Tokuda, Isaiah Norton, Raul San Jose Estepar, David Gering, Hugo JWL Aerts, Marianna Jakab, Nobuhiko Hata, Luiz Ibanez, Daniel Blezek, Jim Miller, Stephen Aylward, Eric WL Grimson, Gabor Fichtinger, William M Wells, William E Lorensen, Will Schroeder, and Ron Kikinis. 2016. “Increasing the Impact of Medical Image Computing using Community-based Open-access Hackathons: The NA-MIC and 3D Slicer Experience.” Med Image Anal, 33, Pp. 176-80.Abstract

The National Alliance for Medical Image Computing (NA-MIC) was launched in 2004 with the goal of investigating and developing an open source software infrastructure for the extraction of information and knowledge from medical images using computational methods. Several leading research and engineering groups participated in this effort that was funded by the US National Institutes of Health through a variety of infrastructure grants. This effort transformed 3D Slicer from an internal, Boston-based, academic research software application into a professionally maintained, robust, open source platform with an international leadership and developer and user communities. Critical improvements to the widely used underlying open source libraries and tools-VTK, ITK, CMake, CDash, DCMTK-were an additional consequence of this effort. This project has contributed to close to a thousand peer-reviewed publications and a growing portfolio of US and international funded efforts expanding the use of these tools in new medical computing applications every year. In this editorial, we discuss what we believe are gaps in the way medical image computing is pursued today; how a well-executed research platform can enable discovery, innovation and reproducible science ("Open Science"); and how our quest to build such a software platform has evolved into a productive and rewarding social engineering exercise in building an open-access community with a shared vision.

J Duryea, C Cheng, LF Schaefer, S. Smith, and B Madore. 2016. “Integration of Accelerated MRI and Post-Processing Software: A Promising Method for Studies of Knee Osteoarthritis.” Osteoarthritis Cartilage, 24, 11, Pp. 1905-9.Abstract
OBJECTIVE: Magnetic resonance imaging (MRI) is a widely used imaging modality for studies of knee osteoarthritis (OA). Compared to radiography, MRI offers exceptional soft tissue imaging and true three-dimensional (3D) visualization. However, MRI is expensive both due to the cost of acquisition and evaluation of the images. The goal of our study is to develop a new method to address the cost of MRI by combining innovative acquisition methods and automated post-processing software. METHODS: Ten healthy volunteers were scanned with three different MRI protocols: A standard 3D dual-echo steady state (DESS) pulse sequence, an accelerated DESS (DESS), acquired at approximately half the time compared to DESS, and a multi-echo time DESS (DESS), which is capable of producing measurements of T2 relaxation time. A software tool was used to measure cartilage volume. Accuracy was quantified by comparing DESS to DESS and DESS and precision was measured using repeat readings and acquisitions. T2 precision was determined using duplicate DESS acquisitions. Intra-class correlation coefficients (ICCs), root-mean square standard deviation (RMSSD), and the coefficient of variation (CoV) were used to quantify accuracy and precision. RESULTS: The accuracies of DESS and DESS were CoV = 3.7% and CoV = 6.6% respectively, while precision was 3.8%, 3.0%, and 3.1% for DESS, DESS and DESS. T2 repositioning precision was 5.8%. CONCLUSION: The results demonstrate that accurate and precise quantification of cartilage volume is possible using a combination of substantially faster MRI acquisition and post-processing software. Precise measurements of cartilage T2 and volume can be made using the same acquisition.
Thanissara Chansakul, Paul N Chen, Thomas C Lee, and Travis Tierney. 2016. “Interventional MR Imaging for Deep-Brain Stimulation Electrode Placement.” Radiology, 281, 3, Pp. 940-6.Abstract

Purpose To investigate the safety and targeting errors of deep-brain stimulation (DBS) electrodes placed under interventional magnetic resonance (MR) imaging, which allows near real-time anatomic placement without physiologic mapping. Materials and Methods Retrospectively evaluated were 10 consecutive patients (five women, five men) with a mean age of 59.9 years (age range, 17-79 years). These patients underwent interventional MR imaging-guided DBS placement for movement disorders from September 2013 to August 2014 for placement of 19 DBS electrodes in cases where traditional frame-based surgery may be challenging because of the following: dystonia resulting in difficulty in placing the patients in frame, patient's inability or unwillingness to tolerate awake surgery, or anatomic anomaly or variant that could increase the risk of bleeding from microelectrode mapping. Outcomes measured included perioperative hemorrhage, death, and stroke, and electrode functionality assessed at 2 weeks after the operation. In addition, the mean radial error and mean trajectory error were calculated. Results No intraoperative neurologic complications (n = 10 [95% confidence interval: 0%, 31%]) were observed. One patient developed aspiration pneumonia in the postoperative period. Mean radial error was 0.7 mm ± 0.4 (standard deviation) and mean trajectory error was 0.5 mm ± 0.4. All leads delivered clinically effective stimulation. Conclusion Interventional MR imaging-guided DBS electrode placement may be a safe and effective alternative to conventional frame-based surgery in well-selected patients.

Eva C Gombos, Jagadeesan Jayender, Danielle M Richman, Diana L Caragacianu, Melissa A Mallory, Ferenc A Jolesz, and Mehra Golshan. 2016. “Intraoperative Supine Breast MR Imaging to Quantify Tumor Deformation and Detection of Residual Breast Cancer: Preliminary Results.” Radiology, 281, 3, Pp. 720-9.Abstract
Purpose To use intraoperative supine magnetic resonance (MR) imaging to quantify breast tumor deformation and displacement secondary to the change in patient positioning from imaging (prone) to surgery (supine) and to evaluate residual tumor immediately after breast-conserving surgery (BCS). Materials and Methods Fifteen women gave informed written consent to participate in this prospective HIPAA-compliant, institutional review board-approved study between April 2012 and November 2014. Twelve patients underwent lumpectomy and postsurgical intraoperative supine MR imaging. Six of 12 patients underwent both pre- and postsurgical supine MR imaging. Geometric, structural, and heterogeneity metrics of the cancer and distances of the tumor from the nipple, chest wall, and skin were computed. Mean and standard deviations of the changes in volume, surface area, compactness, spherical disproportion, sphericity, and distances from key landmarks were computed from tumor models. Imaging duration was recorded. Results The mean differences in tumor deformation metrics between prone and supine imaging were as follows: volume, 23.8% (range, -30% to 103.95%); surface area, 6.5% (range, -13.24% to 63%); compactness, 16.2% (range, -23% to 47.3%); sphericity, 6.8% (range, -9.10% to 20.78%); and decrease in spherical disproportion, -11.3% (range, -60.81% to 76.95%). All tumors were closer to the chest wall on supine images than on prone images. No evidence of residual tumor was seen on MR images obtained after the procedures. Mean duration of pre- and postoperative supine MR imaging was 25 minutes (range, 18.4-31.6 minutes) and 19 minutes (range, 15.1-22.9 minutes), respectively. Conclusion Intraoperative supine breast MR imaging, when performed in conjunction with standard prone breast MR imaging, enables quantification of breast tumor deformation and displacement secondary to changes in patient positioning from standard imaging (prone) to surgery (supine) and may help clinicians evaluate for residual tumor immediately after BCS. (©) RSNA, 2016 Online supplemental material is available for this article.
Ehud J Schmidt, Ronald D Watkins, Menekhem M Zviman, Michael A Guttman, Wei Wang, and Henry A Halperin. 2016. “A Magnetic Resonance Imaging–Conditional External Cardiac De brillator for Resuscitation within the Magnetic Resonance Imaging Scanner Bore.” Circ Cardiovasc Imaging., 9, Pp. e005091.Abstract

Subjects undergoing cardiac arrest within a magnetic resonance imaging (MRI) scanner are currently removed from the bore and then from the MRI suite, before the delivery of cardiopulmonary resuscitation and de brillation, potentially increasing the risk of mortality. This precludes many higher-risk (acute ischemic and acute stroke) patients from undergoing MRI and MRI-guided intervention. An MRI-conditional cardiac de brillator should enable scanning with de brillation pads attached and the generator ON, enabling application of de brillation within the seconds of MRI after a cardiac event. An MRI-conditional external de brillator may improve patient acceptance for MRI procedures. Methods and Results—A commercial external de brillator was rendered 1.5 Tesla MRI-conditional by the addition of novel radiofrequency lters between the generator and commercial disposable surface pads. The radiofrequency lters reduced emission into the MRI scanner and prevented cable/surface pad heating during imaging, while preserving all the de brillator monitoring and delivery functions. Human volunteers were imaged using high speci c absorption rate sequences to validate MRI image quality and lack of heating. Swine were electrically brillated (n=4) and thereafter de brillated both outside and inside the MRI bore. MRI image quality was reduced by 0.8 or 1.6 dB, with the generator in monitoring mode and operating on battery or AC power, respectively. Commercial surface pads did not create artifacts deeper than 6 mm below the skin surface. Radiofrequency heating was within US Food and Drug Administration guidelines. De brillation was completely successful inside and outside the MRI bore. Conclusions—A prototype MRI-conditional de brillation system successfully de brillated in the MRI without degrading the image quality or increasing the time needed for de brillation. It can increase patient acceptance for MRI procedures.

Clare M. Tempany. 2016. “Multimodal Image Guided Therapy: Novel Personalized Approaches in Oncology. Keynote Speech at the 2016 MICCAI Meeting.” Int Conf Med Image Comput Comput Assist Interv. 2016 Oct; 19.Abstract Clare Tempany MICCAI 2016 Invited Talk