Publications

2018
Wenpeng Gao, Baichuan Jiang, Daniel F Kacher, Barry Fetics, Erez Nevo, Thomas C Lee, and Jagadeesan Jayender. 2018. “Real-time Probe Tracking using EM-optical Sensor for MRI-guided Cryoablation .” Int J Med Robot, 14, 1.Abstract
BACKGROUND: A method of real-time, accurate probe tracking at the entrance of the MRI bore is developed, which, fused with pre-procedural MR images, will enable clinicians to perform cryoablation efficiently in a large workspace with image guidance. METHODS: Electromagnetic (EM) tracking coupled with optical tracking is used to track the probe. EM tracking is achieved with an MRI-safe EM sensor working under the scanner's magnetic field to compensate the line-of-sight issue of optical tracking. Unscented Kalman filter-based probe tracking is developed to smooth the EM sensor measurements when occlusion occurs and to improve the tracking accuracy by fusing the measurements of two sensors. RESULTS: Experiments with a spine phantom show that the mean targeting errors using the EM sensor alone and using the proposed method are 2.21 mm and 1.80 mm, respectively. CONCLUSION: The proposed method achieves more accurate probe tracking than using an EM sensor alone at the MRI scanner entrance.
Fan Zhang, Weining Wu, Lipeng Ning, Gloria McAnulty, Deborah Waber, Borjan Gagoski, Kiera Sarill, Hesham M Hamoda, Yang Song, Weidong Cai, Yogesh Rathi, and Lauren J O'Donnell. 2018. “Suprathreshold Fiber Cluster Statistics: Leveraging White Matter Geometry to Enhance Tractography Statistical Analysis.” Neuroimage, 171, Pp. 341-54.Abstract
This work presents a suprathreshold fiber cluster (STFC) method that leverages the whole brain fiber geometry to enhance statistical group difference analyses. The proposed method consists of 1) a well-established study-specific data-driven tractography parcellation to obtain white matter tract parcels and 2) a newly proposed nonparametric, permutation-test-based STFC method to identify significant differences between study populations. The basic idea of our method is that a white matter parcel's neighborhood (nearby parcels with similar white matter anatomy) can support the parcel's statistical significance when correcting for multiple comparisons. We propose an adaptive parcel neighborhood strategy to allow suprathreshold fiber cluster formation that is robust to anatomically varying inter-parcel distances. The method is demonstrated by application to a multi-shell diffusion MRI dataset from 59 individuals, including 30 attention deficit hyperactivity disorder patients and 29 healthy controls. Evaluations are conducted using both synthetic and in-vivo data. The results indicate that the STFC method gives greater sensitivity in finding group differences in white matter tract parcels compared to several traditional multiple comparison correction methods.
Dariya Malyarenko, Andriy Fedorov, Laura Bell, Melissa Prah, Stefanie Hectors, Lori Arlinghaus, Mark Muzi, Meiyappan Solaiyappan, Michael Jacobs, Maggie Fung, Amita Shukla-Dave, Kevin McManus, Michael Boss, Bachir Taouli, Thomas E Yankeelov, Christopher Chad Quarles, Kathleen Schmainda, Thomas L Chenevert, and David C Newitt. 2018. “Toward Uniform Implementation of Parametric Map Digital Imaging and Communication in Medicine Standard in Multisite Quantitative Diffusion Imaging Studies.” J Med Imaging (Bellingham), 5, 1, Pp. 011006.Abstract
This paper reports on results of a multisite collaborative project launched by the MRI subgroup of Quantitative Imaging Network to assess current capability and provide future guidelines for generating a standard parametric diffusion map Digital Imaging and Communication in Medicine (DICOM) in clinical trials that utilize quantitative diffusion-weighted imaging (DWI). Participating sites used a multivendor DWI DICOM dataset of a single phantom to generate parametric maps (PMs) of the apparent diffusion coefficient (ADC) based on two models. The results were evaluated for numerical consistency among models and true phantom ADC values, as well as for consistency of metadata with attributes required by the DICOM standards. This analysis identified missing metadata descriptive of the sources for detected numerical discrepancies among ADC models. Instead of the DICOM PM object, all sites stored ADC maps as DICOM MR objects, generally lacking designated attributes and coded terms for quantitative DWI modeling. Source-image reference, model parameters, ADC units and scale, deemed important for numerical consistency, were either missing or stored using nonstandard conventions. Guided by the identified limitations, the DICOM PM standard has been amended to include coded terms for the relevant diffusion models. Open-source software has been developed to support conversion of site-specific formats into the standard representation.
Seiji Arai, Oliver Jonas, Matthew A Whitman, Eva Corey, Steven P Balk, and Sen Chen. 2018. “Tyrosine Kinase Inhibitors Increase MCL1 Degradation and in Combination with BCLXL/BCL2 Inhibitors Drive Prostate Cancer Apoptosis.” Clin Cancer Res, 24, 21, Pp. 5458-5470.Abstract
Clinically available BH3 mimetic drugs targeting BCLXL and/or BCL2 (navitoclax and venetoclax, respectively) are effective in some hematologic malignancies, but have limited efficacy in solid tumors. This study aimed to identify combination therapies that exploit clinical BH3 mimetics for prostate cancer. Prostate cancer cells or xenografts were treated with BH3 mimetics as single agents or in combination with other agents, and effects on MCL1 and apoptosis were assessed. MCL1 was also targeted directly using RNAi, CRISPR, or an MCL1-specific BH3 mimetic, S63845. We initially found that MCL1 depletion or inhibition markedly sensitized prostate cancer cells to apoptosis mediated by navitoclax, but not venetoclax, and , indicating that they are primed to undergo apoptosis and protected by MCL1 and BCLXL. Small-molecule EGFR kinase inhibitors (erlotinib, lapatinib) also dramatically sensitized to navitoclax-mediated apoptosis, and this was associated with markedly increased proteasome-dependent degradation of MCL1. This increased MCL1 degradation appeared to be through a novel mechanism, as it was not dependent upon GSK3β-mediated phosphorylation and subsequent ubiquitylation by the ubiquitin ligases βTRCP and FBW7, or through other previously identified MCL1 ubiquitin ligases or deubiquitinases. Inhibitors targeting additional kinases (cabozantinib and sorafenib) similarly caused GSK3β-independent MCL1 degradation, and in combination with navitoclax drove apoptosis and These results show that prostate cancer cells are primed to undergo apoptosis and that cotargeting BCLXL and MCL1, directly or indirectly through agents that increase MCL1 degradation, can induce dramatic apoptotic responses. .
Alexander J Kim, Sankha Basu, Carolyn Glass, Edgar L Ross, Nathalie Agar, Qing He, and David Calligaris. 2018. “Unique Intradural Inflammatory Mass Containing Precipitated Morphine: Confirmatory Analysis by LESA-MS and MALDI-MS.” Pain Pract, 18, 7, Pp. 889-94.Abstract
Opioids are often used for analgesia via continuous intrathecal delivery by implantable devices. A higher concentration and daily dose of opioid have been postulated as risk factors for intrathecal granuloma formation. We present a 42-year-old female patient with chronic abdominal pain from refractory pancreatitis, with an intrathecal drug delivery device implanted 21 years prior, delivering continuous intrathecal morphine. After many years without concerning physical signs or complaints, with gradual increases in daily morphine dose, she presented with rapidly progressive neurologic deficits, including lower extremity, bladder, and bowel symptoms. These symptoms were determined to be secondary to mass effect and local inflammation related to an intrathecal catheter tip granuloma, detected on magnetic resonance imaging of the spine. The mass was urgently resected. On histopathologic examination, this granuloma was found to be unique, in that in addition to the expected inflammatory components, it appeared to contain precipitated nonpolarizable crystals. These were identified as precipitated morphine using liquid extraction surface analysis-tandem mass spectrometry (LESA-MS/MS) and matrix-assisted laser desorption ionization-Fourier transform ion cyclotron resonance-mass spectrometry imaging (MALDI-FTICR-MSI). In addition to the unique finding of precipitated morphine crystals, the long-term follow-up of both morphine concentration and daily dose increases provides insight into the formation of intrathecal granulomas.
Momen Abayazid, Takahisa Kato, Stuart G Silverman, and Nobuhiko Hata. 2018. “Using Needle Orientation Sensing as Surrogate Signal for Respiratory Motion Estimation in Rercutaneous Interventions.” Int J Comput Assist Radiol Surg, 13, 1, Pp. 125-33.Abstract
PURPOSE: To develop and evaluate an approach to estimate the respiratory-induced motion of lesions in the chest and abdomen. MATERIALS AND METHODS: The proposed approach uses the motion of an initial reference needle inserted into a moving organ to estimate the lesion (target) displacement that is caused by respiration. The needles position is measured using an inertial measurement unit (IMU) sensor externally attached to the hub of an initially placed reference needle. Data obtained from the IMU sensor and the target motion are used to train a learning-based approach to estimate the position of the moving target. An experimental platform was designed to mimic respiratory motion of the liver. Liver motion profiles of human subjects provided inputs to the experimental platform. Variables including the insertion angle, target depth, target motion velocity and target proximity to the reference needle were evaluated by measuring the error of the estimated target position and processing time. RESULTS: The mean error of estimation of the target position ranged between 0.86 and 1.29 mm. The processing maximum training and testing time was 5 ms which is suitable for real-time target motion estimation using the needle position sensor. CONCLUSION: The external motion of an initially placed reference needle inserted into a moving organ can be used as a surrogate, measurable and accessible signal to estimate in real-time the position of a moving target caused by respiration; this technique could then be used to guide the placement of subsequently inserted needles directly into the target.
Jie Luo, Sarah Frisken, Ines Machado, Miaomiao Zhang, Steve Pieper, Polina Golland, Matthew Toews, Prashin Unadkat, Alireza Sedghi, Haoyin Zhou, Alireza Mehrtash, Frank Preiswerk, Cheng-Chieh Cheng, Alexandra Golby, Masashi Sugiyama, and William M Wells. 2018. “Using the Variogram for Vector Outlier Screening: Application to Feature-based Image Registration.” Int J Comput Assist Radiol Surg, 13, 12, Pp. 1871-80.Abstract
PURPOSE: Matching points that are derived from features or landmarks in image data is a key step in some medical imaging applications. Since most robust point matching algorithms claim to be able to deal with outliers, users may place high confidence in the matching result and use it without further examination. However, for tasks such as feature-based registration in image-guided neurosurgery, even a few mismatches, in the form of invalid displacement vectors, could cause serious consequences. As a result, having an effective tool by which operators can manually screen all matches for outliers could substantially benefit the outcome of those applications. METHODS: We introduce a novel variogram-based outlier screening method for vectors. The variogram is a powerful geostatistical tool for characterizing the spatial dependence of stochastic processes. Since the spatial correlation of invalid displacement vectors, which are considered as vector outliers, tends to behave differently than normal displacement vectors, they can be efficiently identified on the variogram. RESULTS: We validate the proposed method on 9 sets of clinically acquired ultrasound data. In the experiment, potential outliers are flagged on the variogram by one operator and further evaluated by 8 experienced medical imaging researchers. The matching quality of those potential outliers is approximately 1.5 lower, on a scale from 1 (bad) to 5 (good), than valid displacement vectors. CONCLUSION: The variogram is a simple yet informative tool. While being used extensively in geostatistical analysis, it has not received enough attention in the medical imaging field. We believe there is a good deal of potential for clinically applying the proposed outlier screening method. By way of this paper, we also expect researchers to find variogram useful in other medical applications that involve motion vectors analyses.
Fan Zhang, Peter Savadjiev, Weidong Cai, Yang Song, Yogesh Rathi, Birkan Tunç, Drew Parker, Tina Kapur, Robert T Schultz, Nikos Makris, Ragini Verma, and Lauren J O'Donnell. 2018. “Whole Brain White Matter Connectivity Analysis using Machine Learning: An Application to Autism.” Neuroimage, 172, Pp. 826-37.Abstract
In this paper, we propose an automated white matter connectivity analysis method for machine learning classification and characterization of white matter abnormality via identification of discriminative fiber tracts. The proposed method uses diffusion MRI tractography and a data-driven approach to find fiber clusters corresponding to subdivisions of the white matter anatomy. Features extracted from each fiber cluster describe its diffusion properties and are used for machine learning. The method is demonstrated by application to a pediatric neuroimaging dataset from 149 individuals, including 70 children with autism spectrum disorder (ASD) and 79 typically developing controls (TDC). A classification accuracy of 78.33% is achieved in this cross-validation study. We investigate the discriminative diffusion features based on a two-tensor fiber tracking model. We observe that the mean fractional anisotropy from the second tensor (associated with crossing fibers) is most affected in ASD. We also find that local along-tract (central cores and endpoint regions) differences between ASD and TDC are helpful in differentiating the two groups. These altered diffusion properties in ASD are associated with multiple robustly discriminative fiber clusters, which belong to several major white matter tracts including the corpus callosum, arcuate fasciculus, uncinate fasciculus and aslant tract; and the white matter structures related to the cerebellum, brain stem, and ventral diencephalon. These discriminative fiber clusters, a small part of the whole brain tractography, represent the white matter connections that could be most affected in ASD. Our results indicate the potential of a machine learning pipeline based on white matter fiber clustering.
2017
E George, P Liacouras, TC Lee, and D Mitsouras. 2017. “3D-Printed Patient-Specific Models for CT-and MRI-Guided Procedure Planning.” AJNR Am J Neuroradiol, 38, 7, Pp. E46-7.
Andre Mastmeyer, Guillaume Pernelle, Ruibin Ma, Lauren Barber, and Tina Kapur. 2017. “Accurate Model-based Segmentation of Gynecologic Brachytherapy Catheter Collections in MRI-images.” Med Image Anal, 42, Pp. 173-88.Abstract
The gynecological cancer mortality rate, including cervical, ovarian, vaginal and vulvar cancers, is more than 20,000 annually in the US alone. In many countries, including the US, external-beam radiotherapy followed by high dose rate brachytherapy is the standard-of-care. The superior ability of MR to visualize soft tissue has led to an increase in its usage in planning and delivering brachytherapy treatment. A technical challenge associated with the use of MRI imaging for brachytherapy, in contrast to that of CT imaging, is the visualization of catheters that are used to place radiation sources into cancerous tissue. We describe here a precise, accurate method for achieving catheter segmentation and visualization. The algorithm, with the assistance of manually provided tip locations, performs segmentation using image-features, and is guided by a catheter-specific, estimated mechanical model. A final quality control step removes outliers or conflicting catheter trajectories. The mean Hausdorff error on a 54 patient, 760 catheter reference database was 1.49  mm; 51 of the outliers deviated more than two catheter widths (3.4  mm) from the gold standard, corresponding to catheter identification accuracy of 93% in a Syed-Neblett template. In a multi-user simulation experiment for evaluating RMS precision by simulating varying manually-provided superior tip positions, 3σ maximum errors were 2.44  mm. The average segmentation time for a single catheter was 3 s on a standard PC. The segmentation time, accuracy and precision, are promising indicators of the value of this method for clinical translation of MR-guidance in gynecologic brachytherapy and other catheter-based interventional procedures.
Marc Niethammer, Kilian M Pohl, Firdaus Janoos, and William M Wells. 2017. “Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models.” SIAM J. Imaging Sci., 10, 3, Pp. 1069–1103.Abstract
Segmentation is a fundamental task for extracting semantically meaningful regions from an image. The goal of segmentation algorithms is to accurately assign object labels to each image location. However, image noise, shortcomings of algorithms, and image ambiguities cause uncertainty in label assignment. Estimating this uncertainty is important in multiple application domains, such as segmenting tumors from medical images for radiation treatment planning. One way to estimate these uncertainties is through the computation of posteriors of Bayesian models, which is computationally prohibitive for many practical applications. However, most computationally efficient methods fail to estimate label uncertainty. We therefore propose in this paper the active mean fields (AMF) approach, a technique based on Bayesian modeling that uses a mean-field approximation to efficiently compute a segmentation and its corresponding uncertainty. Based on a variational formulation, the resulting convex model combines any label-likelihood measure with a prior on the length of the segmentation boundary. A specific implementation of that model is the Chan-Vese segmentation model, in which the binary segmentation task is defined by a Gaussian likelihood and a prior regularizing the length of the segmentation boundary. Furthermore, the Euler-Lagrange equations derived from the AMF model are equivalent to those of the popular Rudin-Osher-Fatemi (ROF) model for image denoising. Solutions to the AMF model can thus be implemented by directly utilizing highly efficient ROF solvers on log-likelihood ratio fields. We qualitatively assess the approach on synthetic data as well as on real natural and medical images. For a quantitative evaluation, we apply our approach to the tt icgbench dataset.
Rahul Sastry, Wenya Linda Bi, Steve Pieper, Sarah Frisken, Tina Kapur, William Wells, and Alexandra J Golby. 2017. “Applications of Ultrasound in the Resection of Brain Tumors.” J Neuroimaging, 27, 1, Pp. 5-15.Abstract

Neurosurgery makes use of preoperative imaging to visualize pathology, inform surgical planning, and evaluate the safety of selected approaches. The utility of preoperative imaging for neuronavigation, however, is diminished by the well-characterized phenomenon of brain shift, in which the brain deforms intraoperatively as a result of craniotomy, swelling, gravity, tumor resection, cerebrospinal fluid (CSF) drainage, and many other factors. As such, there is a need for updated intraoperative information that accurately reflects intraoperative conditions. Since 1982, intraoperative ultrasound has allowed neurosurgeons to craft and update operative plans without ionizing radiation exposure or major workflow interruption. Continued evolution of ultrasound technology since its introduction has resulted in superior imaging quality, smaller probes, and more seamless integration with neuronavigation systems. Furthermore, the introduction of related imaging modalities, such as 3-dimensional ultrasound, contrast-enhanced ultrasound, high-frequency ultrasound, and ultrasound elastography, has dramatically expanded the options available to the neurosurgeon intraoperatively. In the context of these advances, we review the current state, potential, and challenges of intraoperative ultrasound for brain tumor resection. We begin by evaluating these ultrasound technologies and their relative advantages and disadvantages. We then review three specific applications of these ultrasound technologies to brain tumor resection: (1) intraoperative navigation, (2) assessment of extent of resection, and (3) brain shift monitoring and compensation. We conclude by identifying opportunities for future directions in the development of ultrasound technologies.

C Chennubhotla, LP Clarke, A Fedorov, D Foran, G Harris, E Helton, R Nordstrom, F Prior, D. Rubin, JH Saltz, E Shalley, and A Sharma. 2017. “An Assessment of Imaging Informatics for Precision Medicine in Cancer.” Yearb Med Inform, 26, 1, Pp. 110-9.Abstract
Precision medicine requires the measurement, quantification, and cataloging of medical characteristics to identify the most effective medical intervention. However, the amount of available data exceeds our current capacity to extract meaningful information. We examine the informatics needs to achieve precision medicine from the perspective of quantitative imaging and oncology. The National Cancer Institute (NCI) organized several workshops on the topic of medical imaging and precision medicine. The observations and recommendations are summarized herein. Recommendations include: use of standards in data collection and clinical correlates to promote interoperability; data sharing and validation of imaging tools; clinician's feedback in all phases of research and development; use of open-source architecture to encourage reproducibility and reusability; use of challenges which simulate real-world situations to incentivize innovation; partnership with industry to facilitate commercialization; and education in academic communities regarding the challenges involved with translation of technology from the research domain to clinical utility and the benefits of doing so. This article provides a survey of the role and priorities for imaging informatics to help advance quantitative imaging in the era of precision medicine. While these recommendations were drawn from oncology, they are relevant and applicable to other clinical domains where imaging aids precision medicine.
David Black, Julian Hettig, Maria Luz, Christian Hansen, Ron Kikinis, and Horst Hahn. 2017. “Auditory Feedback to Support Image-Guided Medical Needle Placement.” Int J Comput Assist Radiol Surg, 12, 9, Pp. 1655-63.Abstract
PURPOSE: During medical needle placement using image-guided navigation systems, the clinician must concentrate on a screen. To reduce the clinician's visual reliance on the screen, this work proposes an auditory feedback method as a stand-alone method or to support visual feedback for placing the navigated medical instrument, in this case a needle. METHODS: An auditory synthesis model using pitch comparison and stereo panning parameter mapping was developed to augment or replace visual feedback for navigated needle placement. In contrast to existing approaches which augment but still require a visual display, this method allows view-free needle placement. An evaluation with 12 novice participants compared both auditory and combined audiovisual feedback against existing visual methods. RESULTS: Using combined audiovisual display, participants show similar task completion times and report similar subjective workload and accuracy while viewing the screen less compared to using the conventional visual method. The auditory feedback leads to higher task completion times and subjective workload compared to both combined and visual feedback. CONCLUSION: Audiovisual feedback shows promising results and establishes a basis for applying auditory feedback as a supplement to visual information to other navigated interventions, especially those for which viewing a patient is beneficial or necessary.
Lauren J O'Donnell, Yannick Suter, Laura Rigolo, Pegah Kahali, Fan Zhang, Isaiah Norton, Angela Albi, Olutayo Olubiyi, Antonio Meola, Walid I Essayed, Prashin Unadkat, Pelin Aksit Ciris, William M Wells, Yogesh Rathi, Carl-Fredrik Westin, and Alexandra J Golby. 2017. “Automated White Matter Fiber Tract Identification in Patients with Brain Tumors.” Neuroimage Clin, 13, Pp. 138-53.Abstract

We propose a method for the automated identification of key white matter fiber tracts for neurosurgical planning, and we apply the method in a retrospective study of 18 consecutive neurosurgical patients with brain tumors. Our method is designed to be relatively robust to challenges in neurosurgical tractography, which include peritumoral edema, displacement, and mass effect caused by mass lesions. The proposed method has two parts. First, we learn a data-driven white matter parcellation or fiber cluster atlas using groupwise registration and spectral clustering of multi-fiber tractography from healthy controls. Key fiber tract clusters are identified in the atlas. Next, patient-specific fiber tracts are automatically identified using tractography-based registration to the atlas and spectral embedding of patient tractography. Results indicate good generalization of the data-driven atlas to patients: 80% of the 800 fiber clusters were identified in all 18 patients, and 94% of the 800 fiber clusters were found in 16 or more of the 18 patients. Automated subject-specific tract identification was evaluated by quantitative comparison to subject-specific motor and language functional MRI, focusing on the arcuate fasciculus (language) and corticospinal tracts (motor), which were identified in all patients. Results indicate good colocalization: 89 of 95, or 94%, of patient-specific language and motor activations were intersected by the corresponding identified tract. All patient-specific activations were within 3mm of the corresponding language or motor tract. Overall, our results indicate the potential of an automated method for identifying fiber tracts of interest for neurosurgical planning, even in patients with mass lesions.

PB Shyn, S Tremblay-Paquet, K Palmer, S Tatli, K Tuncali, OI Olubiyi, N Hata, and SG Silverman. 2017. “Breath-hold PET/CT-guided Tumor Ablation under General Anesthesia: Accuracy of Tumor Image Registration and Projected Ablation Zone Overlap.” Clin Radiol, 72, 3, Pp. 223-9.Abstract

AIM: To assess single-breath-hold combined positron-emission tomography/computed tomography (PET/CT) for accuracy of tumour image registration and projected ablation volume overlap in patients undergoing percutaneous PET/CT-guided tumour-ablation procedures under general anaesthesia. MATERIALS AND METHODS: Eight patients underwent 12 PET/CT-guided tumour-ablation procedures to treat 20 tumours in the lung, liver, or adrenal gland. Using breath-hold PET/CT, the centre of the tumour was marked on each PET and CT acquisition by four readers to assess two- (2D) and three-dimensional (3D) spatial misregistration. Overlap of PET and CT projected ablation volumes were compared using the Dice similarity coefficient (DSC). Interobserver differences were assessed with repeated measure analysis of variance (ANOVA). Technical success and local progression rates were noted. RESULTS: Mean tumour 2D PET/CT misregistrations were 1.02 mm (range 0.01-5.02), 1.89 (0.03-7.85), and 3.05 (0-10) in the x, y, and z planes. Mean 3D misregistration was 4.4 mm (0.36-10.74). Mean projected PET/CT ablation volume DSC was 0.72 (±0.19). No significant interobserver differences in 3D misregistration (p=0.73) or DSC (p=0.54) were observed. Technical success of ablations was 100%; one (5.3%) of 19 tumours progressed. CONCLUSION: Accurate spatial registration of tumours and substantial overlap of projected ablation volumes are achievable when comparing PET and CT acquisitions from single-breath-hold PET/CT. The results suggest that tumours visible only at PET could be accurately targeted and ablated using this technique.

Klaus H Maier-Hein, Peter F Neher, Jean-Christophe Houde, Marc-Alexandre Côté, Eleftherios Garyfallidis, Jidan Zhong, Maxime Chamberland, Fang-Cheng Yeh, Ying-Chia Lin, Qing Ji, Wilburn E Reddick, John O Glass, David Qixiang Chen, Yuanjing Feng, Chengfeng Gao, Ye Wu, Jieyan Ma, H Renjie, Qiang Li, Carl-Fredrik Westin, Samuel Deslauriers-Gauthier, Omar Ocegueda J González, Michael Paquette, Samuel St-Jean, Gabriel Girard, François Rheault, Jasmeen Sidhu, Chantal MW Tax, Fenghua Guo, Hamed Y Mesri, Szabolcs Dávid, Martijn Froeling, Anneriet M Heemskerk, Alexander Leemans, Arnaud Boré, Basile Pinsard, Christophe Bedetti, Matthieu Desrosiers, Simona Brambati, Julien Doyon, Alessia Sarica, Roberta Vasta, Antonio Cerasa, Aldo Quattrone, Jason Yeatman, Ali R Khan, Wes Hodges, Simon Alexander, David Romascano, Muhamed Barakovic, Anna Auría, Oscar Esteban, Alia Lemkaddem, Jean-Philippe Thiran, Ertan H Cetingul, Benjamin L Odry, Boris Mailhe, Mariappan S Nadar, Fabrizio Pizzagalli, Gautam Prasad, Julio E Villalon-Reina, Justin Galvis, Paul M Thompson, Francisco De Santiago Requejo, Pedro Luque Laguna, Luis Miguel Lacerda, Rachel Barrett, Flavio Dell'Acqua, Marco Catani, Laurent Petit, Emmanuel Caruyer, Alessandro Daducci, Tim B Dyrby, Tim Holland-Letz, Claus C Hilgetag, Bram Stieltjes, and Maxime Descoteaux. 2017. “The Challenge of Mapping the Human Connectome Based on Diffusion Tractography.” Nat Commun, 8, 1, Pp. 1349.Abstract
Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations.
Michael A Silva, Alfred P See, Walid I Essayed, Alexandra J Golby, and Yanmei Tie. 2017. “Challenges and Techniques for Presurgical Brain Mapping with Functional MRI.” Neuroimage Clin, 17, Pp. 794-803.Abstract
Functional magnetic resonance imaging (fMRI) is increasingly used for preoperative counseling and planning, and intraoperative guidance for tumor resection in the eloquent cortex. Although there have been improvements in image resolution and artifact correction, there are still limitations of this modality. In this review, we discuss clinical fMRI's applications, limitations and potential solutions. These limitations depend on the following parameters: foundations of fMRI, physiologic effects of the disease, distinctions between clinical and research fMRI, and the design of the fMRI study. We also compare fMRI to other brain mapping modalities which should be considered as alternatives or adjuncts when appropriate, and discuss intraoperative use and validation of fMRI. These concepts direct the clinical application of fMRI in neurosurgical patients.
Mizuki Nishino, Adrian G Sacher, Leena Gandhi, Zhao Chen, Esra Akbay, Andriy Fedorov, Carl F Westin, Hiroto Hatabu, Bruce E Johnson, Peter Hammerman, and Kwok-Kin Wong. 2017. “Co-clinical Quantitative Tumor Volume Imaging in ALK-rearranged NSCLC Treated with Crizotinib.” Eur J Radiol, 88, Pp. 15-20.Abstract
PURPOSE: To evaluate and compare the volumetric tumor burden changes during crizotinib therapy in mice and human cohorts with ALK-rearranged non-small-cell lung cancer (NSCLC). METHODS: Volumetric tumor burden was quantified on serial imaging studies in 8 bitransgenic mice with ALK-rearranged adenocarcinoma treated with crizotinib, and in 33 human subjects with ALK-rearranged NSCLC treated with crizotinib. The volumetric tumor burden changes and the time to maximal response were compared between mice and humans. RESULTS: The median tumor volume decrease (%) at the maximal response was -40.4% (range: -79.5%-+11.7%) in mice, and -72.9% (range: -100%-+72%) in humans (Wilcoxon p=0.03). The median time from the initiation of therapy to maximal response was 6 weeks in mice, and 15.7 weeks in humans. Overall volumetric response rate was 50% in mice and 97% in humans. Spider plots of tumor volume changes during therapy demonstrated durable responses in the human cohort, with a median time on therapy of 13.1 months. CONCLUSION: The present study described an initial attempt to evaluate quantitative tumor burden changes in co-clinical imaging studies of genomically-matched mice and human cohorts with ALK-rearranged NSCLC treated with crizotinib. Differences are noted in the degree of maximal volume response between the two cohorts in this well-established paradigm of targeted therapy, indicating a need for further studies to optimize co-clinical trial design and interpretation.
Sophia C Kamran, Matthias M Manuel, Linda P Cho, Antonio L Damato, Ehud J Schmidt, Clare Tempany, Robert A Cormack, and Akila N Viswanathan. 2017. “Comparison of Outcomes for MR-guided versus CT-guided High-dose-rate Interstitial Brachytherapy in Women with Locally Advanced Carcinoma of the Cervix.” Gynecol Oncol, 145, 2, Pp. 284-90.Abstract

OBJECTIVE: The purpose was to compare local control (LC), overall survival (OS) and dose to the organs at risk (OAR) in women with locally advanced cervical cancer treated with MR-guided versus CT-guided interstitial brachytherapy (BT). METHODS: 56 patients (29 MR, 27 CT) were treated with high-dose-rate (HDR) interstitial BT between 2005-2015. The MR patients had been prospectively enrolled on a Phase II clinical trial. Data were analyzed using Kaplan-Meier (K-M) and Cox proportional hazards statistical modeling in JMP® & R®. RESULTS: Median follow-up time was 19.7months (MR group) and 18.4months (CT group). There were no statistically significant differences in patient age at diagnosis, histology, percent with tumor size >4cm, grade, FIGO stage or lymph node involvement between the groups. Patients in the MR group had more lymphovascular involvement compared to patients in the CT group (p<0.01). When evaluating plans generated, there were no statistically significant differences in median cumulative dose to the high-risk clinical target volume or the OAR. 2-year K-M LC rates for MR-based and CT-based treatments were 96% and 87%, respectively (log-rank p=0.65). At 2years, OS was significantly better in the MR-guided cohort (84% vs. 56%, p=0.036). On multivariate analysis, squamous histology was associated with longer OS (HR 0.23, 95% CI 0.07-0.72) in a model with MR BT (HR 0.35, 95% CI 0.08-1.18). There was no difference in toxicities between CT and MR BT. CONCLUSION: In this population of locally advanced cervical-cancer patients, MR-guided HDR BT resulted in estimated 96% 2-year local control and excellent survival and toxicity rates.

Pages