Publications by Year: 2018

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.