Frisken S, Luo M, Juvekar P, Bunevicius A, Machado I, Unadkat P, Bertotti MM, Toews M, Wells WM, Miga MI, et al. A Comparison of Thin-Plate Spline Deformation and Finite Element Modeling to Compensate for Brain Shift during Tumor Resection. Int J Comput Assist Radiol Surg. 2020;15 (1) :75-85.Abstract
PURPOSE: Brain shift during tumor resection can progressively invalidate the accuracy of neuronavigation systems and affect neurosurgeons' ability to achieve optimal resections. This paper compares two methods that have been presented in the literature to compensate for brain shift: a thin-plate spline deformation model and a finite element method (FEM). For this comparison, both methods are driven by identical sparse data. Specifically, both methods are driven by displacements between automatically detected and matched feature points from intraoperative 3D ultrasound (iUS). Both methods have been shown to be fast enough for intraoperative brain shift correction (Machado et al. in Int J Comput Assist Radiol Surg 13(10):1525-1538, 2018; Luo et al. in J Med Imaging (Bellingham) 4(3):035003, 2017). However, the spline method requires no preprocessing and ignores physical properties of the brain while the FEM method requires significant preprocessing and incorporates patient-specific physical and geometric constraints. The goal of this work was to explore the relative merits of these methods on recent clinical data. METHODS: Data acquired during 19 sequential tumor resections in Brigham and Women's Hospital's Advanced Multi-modal Image-Guided Operating Suite between December 2017 and October 2018 were considered for this retrospective study. Of these, 15 cases and a total of 24 iUS to iUS image pairs met inclusion requirements. Automatic feature detection (Machado et al. in Int J Comput Assist Radiol Surg 13(10):1525-1538, 2018) was used to detect and match features in each pair of iUS images. Displacements between matched features were then used to drive both the spline model and the FEM method to compensate for brain shift between image acquisitions. The accuracies of the resultant deformation models were measured by comparing the displacements of manually identified landmarks before and after deformation. RESULTS: The mean initial subcortical registration error between preoperative MRI and the first iUS image averaged 5.3 ± 0.75 mm. The mean subcortical brain shift, measured using displacements between manually identified landmarks in pairs of iUS images, was 2.5 ± 1.3 mm. Our results showed that FEM was able to reduce subcortical registration error by a small but statistically significant amount (from 2.46 to 2.02 mm). A large variability in the results of the spline method prevented us from demonstrating either a statistically significant reduction in subcortical registration error after applying the spline method or a statistically significant difference between the results of the two methods. CONCLUSIONS: In this study, we observed less subcortical brain shift than has previously been reported in the literature (Frisken et al., in: Miller (ed) Biomechanics of the brain, Springer, Cham, 2019). This may be due to the fact that we separated out the initial misregistration between preoperative MRI and the first iUS image from our brain shift measurements or it may be due to modern neurosurgical practices designed to reduce brain shift, including reduced craniotomy sizes and better control of intracranial pressure with the use of mannitol and other medications. It appears that the FEM method and its use of geometric and biomechanical constraints provided more consistent brain shift correction and better correction farther from the driving feature displacements than the simple spline model. The spline-based method was simpler and tended to give better results for small deformations. However, large variability in the spline results and relatively small brain shift prevented this study from demonstrating a statistically significant difference between the results of the two methods.
Herz C, MacNeil K, Behringer PA, Tokuda J, Mehrtash A, Mousavi P, Kikinis R, Fennessy FM, Tempany CM, Tuncali K, et al. Open Source Platform for Transperineal In-Bore MRI-Guided Targeted Prostate Biopsy. IEEE Trans Biomed Eng. 2020;67 (2) :565-76.Abstract
OBJECTIVE: Accurate biopsy sampling of the suspected lesions is critical for the diagnosis and clinical management of prostate cancer. Transperineal in-bore MRI-guided prostate biopsy (tpMRgBx) is a targeted biopsy technique that was shown to be safe, efficient, and accurate. Our goal was to develop an open source software platform to support evaluation, refinement, and translation of this biopsy approach. METHODS: We developed SliceTracker, a 3D Slicer extension to support tpMRgBx. We followed modular design of the implementation to enable customization of the interface and interchange of image segmentation and registration components to assess their effect on the processing time, precision, and accuracy of the biopsy needle placement. The platform and supporting documentation were developed to enable the use of software by an operator with minimal technical training to facilitate translation. Retrospective evaluation studied registration accuracy, effect of the prostate segmentation approach, and re-identification time of biopsy targets. Prospective evaluation focused on the total procedure time and biopsy targeting error (BTE). RESULTS: Evaluation utilized data from 73 retrospective and ten prospective tpMRgBx cases. Mean landmark registration error for retrospective evaluation was 1.88 ± 2.63 mm, and was not sensitive to the approach used for prostate gland segmentation. Prospectively, we observed target re-identification time of 4.60 ± 2.40 min and BTE of 2.40 ± 0.98 mm. CONCLUSION: SliceTracker is modular and extensible open source platform for supporting image processing aspects of the tpMRgBx procedure. It has been successfully utilized to support clinical research procedures at our site.
Gao Y, Takagi K, Kato T, Shono N, Hata N. Continuum Robot With Follow-the-Leader Motion for Endoscopic Third Ventriculostomy and Tumor Biopsy. IEEE Trans Biomed Eng. 2020;67 (2) :379-90.Abstract
BACKGROUND: In a combined endoscopic third ventriculostomy (ETV) and endoscopic tumor biopsy (ETB) procedure, an optimal tool trajectory is mandatory to minimize trauma to surrounding cerebral tissue. OBJECTIVE: This paper presents wire-driven multi-section robot with push-pull wire. The robot is tested to attain follow-the-leader (FTL) motion to place surgical instruments through narrow passages while minimizing the trauma to tissues. METHODS: A wire-driven continuum robot with six sub-sections was developed and its kinematic model was proposed to achieve FTL motion. An accuracy test to assess the robot's ability to attain FTL motion along a set of elementary curved trajectory was performed. We also used hydrocephalus ventricular model created from human subject data to generate five ETV/ETB trajectories and conducted a study assessing the accuracy of the FTL motion along these clinically desirable trajectories. RESULTS: In the test with elementary curved paths, the maximal deviation of the robot was increased from 0.47 mm at 30 turn to 1.78 mm at 180 in a simple C-shaped curve. S-shaped FTL motion had lesser deviation ranging from 0.16 to 0.18 mm. In the phantom study, the greatest tip deviation was 1.45 mm, and the greatest path deviation was 1.23 mm. CONCLUSION: We present the application of a continuum robot with FTL motion to perform a combined ETV/ETB procedure. The validation study using human subject data indicated that the accuracy of FTL motion is relatively high. The study indicated that FTL motion may be useful tool for combined ETV and ETB.
Wachinger C, Toews M, Langs G, Wells W, Golland P. Keypoint Transfer for Fast Whole-Body Segmentation. IEEE Trans Med Imaging. 2020;39 (2) :273-82.Abstract
We introduce an approach for image segmentation based on sparse correspondences between keypoints in testing and training images. Keypoints represent automatically identified distinctive image locations, where each keypoint correspondence suggests a transformation between images. We use these correspondences to transfer label maps of entire organs from the training images to the test image. The keypoint transfer algorithm includes three steps: (i) keypoint matching, (ii) voting-based keypoint labeling, and (iii) keypoint-based probabilistic transfer of organ segmentations. We report segmentation results for abdominal organs in whole-body CT and MRI, as well as in contrast-enhanced CT and MRI. Our method offers a speed-up of about three orders of magnitude in comparison to common multi-atlas segmentation, while achieving an accuracy that compares favorably. Moreover, keypoint transfer does not require the registration to an atlas or a training phase. Finally, the method allows for the segmentation of scans with highly variable field-of-view.
Zhang F, Hoffmann N, Cetin Karayumak S, Rathi Y, Golby AJ, O'Donnell LJ. Deep White Matter Analysis: Fast, Consistent Tractography Segmentation Across Populations and dMRI Acquisitions. Med Image Comput Comput Assist Interv. 2019;11766 :599-608.Abstract
We present a deep learning tractography segmentation method that allows fast and consistent white matter fiber tract identification across healthy and disease populations and across multiple diffusion MRI (dMRI) acquisitions. We create a large-scale training tractography dataset of 1 million labeled fiber samples (54 anatomical tracts are included). To discriminate between fibers from different tracts, we propose a novel 2D multi-channel feature descriptor (FiberMap) that encodes spatial coordinates of points along each fiber. We learn a CNN tract classification model based on FiberMap and obtain a high tract classification accuracy of 90.99%. The method is evaluated on a test dataset of 374 dMRI scans from three independently acquired populations across health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). We perform comparisons with two state-of-the-art white matter tract segmentation methods. Experimental results show that our method obtains a highly consistent segmentation result, where over 99% of the fiber tracts are successfully detected across all subjects under study, most importantly, including patients with space occupying brain tumors. The proposed method leverages deep learning techniques and provides a much faster and more efficient tool for large data analysis than methods using traditional machine learning techniques.
Fan D, Chaudhari NN, Rostowsky KA, Calvillo M, Lee SK, Chowdhury NF, Zhang F, O'Donnell LJ, Irimia A. Post-Traumatic Cerebral Microhemorrhages and their Effects Upon White Matter Connectivity in the Aging Human Brain. Conf Proc IEEE Eng Med Biol Soc. 2019;2019 :198-203.Abstract
Cerebral microbleeds (CMBs), a common manifestation of mild traumatic brain injury (mTBI), have been sporadically implicated in the neurocognitive deficits of mTBI victims but their clinical significance has not been established adequately. Here we investigate the longitudinal effects of post-mTBI CMBs upon the fractional anisotropy (FA) of white matter (WM) in 21 older mTBI patients across the first ~6 months post-injury. CMBs were segmented automatically from susceptibility-weighted imaging (SWI) by leveraging the intensity gradient properties of SWI to identify CMB-related hypointensities using gradient-based edge detection. A detailed diffusion magnetic resonance imaging (dMRI) atlas of WM was used to segment and cluster tractography streamlines whose prototypes were then identified. The correlation coefficient was calculated between (A) FA values at vertices along streamline prototypes and (B) topological (along-streamline) distances between these vertices and the nearest CMB. Across subjects, the CMB identification approach achieved a sensitivity of 97.1% ± 4.7% and a precision of 72.4% ± 11.0% across subjects. The correlation coefficient was found to be negative and, additionally, statistically significant for 12.3% ± 3.5% of WM clusters (p <; 0.05, corrected), whose FA was found to decrease, on average, by 11.8% ± 5.3% across the first 6 months post-injury. These results suggest that CMBs can be associated with deleterious effects upon peri-lesional WM and highlight the vulnerability of older mTBI patients to neurovascular injury.
Kuijf HJ, Casamitjana A, Collins LD, Dadar M, Georgiou A, Ghafoorian M, Jin D, Khademi A, Knight J, Li H, et al. Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge. IEEE Trans Med Imaging. 2019;38 (11) :2556-68.Abstract
Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. The automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their methods on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge. Sixty T1 + FLAIR images from three MR scanners were released with the manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. The segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: 1) Dice similarity coefficient; 2) modified Hausdorff distance (95th percentile); 3) absolute log-transformed volume difference; 4) sensitivity for detecting individual lesions; and 5) F1-score for individual lesions. In addition, the methods were ranked on their inter-scanner robustness; 20 participants submitted their methods for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all the methods generalize to unseen scanners. The challenge remains open for future submissions and provides a public platform for method evaluation.
Panda A, OʼConnor G, Lo WC, Jiang Y, Margevicius S, Schluchter M, Ponsky LE, Gulani V. Targeted Biopsy Validation of Peripheral Zone Prostate Cancer Characterization With Magnetic Resonance Fingerprinting and Diffusion Mapping. Invest Radiol. 2019;54 (8) :485-93.Abstract
OBJECTIVE: This study aims for targeted biopsy validation of magnetic resonance fingerprinting (MRF) and diffusion mapping for characterizing peripheral zone (PZ) prostate cancer and noncancers. MATERIALS AND METHODS: One hundred four PZ lesions in 85 patients who underwent magnetic resonance imaging were retrospectively analyzed with apparent diffusion coefficient (ADC) mapping, MRF, and targeted biopsy (cognitive or in-gantry). A radiologist blinded to pathology drew regions of interest on targeted lesions and visually normal peripheral zone on MRF and ADC maps. Mean T1, T2, and ADC were analyzed using linear mixed models. Generalized estimating equations logistic regression analyses were used to evaluate T1 and T2 relaxometry combined with ADC in differentiating pathologic groups. RESULTS: Targeted biopsy revealed 63 cancers (low-grade cancer/Gleason score 6 = 10, clinically significant cancer/Gleason score ≥7 = 53), 15 prostatitis, and 26 negative biopsies. Prostate cancer T1, T2, and ADC (mean ± SD, 1660 ± 270 milliseconds, 56 ± 20 milliseconds, 0.70 × 10 ± 0.24 × 10 mm/s) were significantly lower than prostatitis (mean ± SD, 1730 ± 350 milliseconds, 77 ± 36 milliseconds, 1.00 × 10 ± 0.30 × 10 mm/s) and negative biopsies (mean ± SD, 1810 ± 250 milliseconds, 71 ± 37 milliseconds, 1.00 × 10 ± 0.33 × 10 mm/s). For cancer versus prostatitis, ADC was sensitive and T2 specific with comparable area under curve (AUC; (AUCT2 = 0.71, AUCADC = 0.79, difference between AUCs not significant P = 0.37). T1 + ADC (AUCT1 + ADC = 0.83) provided the best separation between cancer and negative biopsies. Low-grade cancer T2 and ADC (mean ± SD, 75 ± 29 milliseconds, 0.96 × 10 ± 0.34 × 10 mm/s) were significantly higher than clinically significant cancers (mean ± SD, 52 ± 16 milliseconds, 0.65 ± 0.18 × 10 mm/s), and T2 + ADC (AUCT2 + ADC = 0.91) provided the best separation. CONCLUSIONS: T1 and T2 relaxometry combined with ADC mapping may be useful for quantitative characterization of prostate cancer grades and differentiating cancer from noncancers for PZ lesions seen on T2-weighted images.
Wang J, Wells WM, Golland P, Zhang M. Registration Uncertainty Quantification via Low-dimensional Characterization of Geometric Deformations. Magn Reson Imaging. 2019;64 :122-31.Abstract
This paper presents an efficient approach to quantifying image registration uncertainty based on a low-dimensional representation of geometric deformations. In contrast to previous methods, we develop a Bayesian diffeomorphic registration framework in a bandlimited space, rather than a high-dimensional image space. We show that a dense posterior distribution on deformation fields can be fully characterized by much fewer parameters, which dramatically reduces the computational complexity of model inferences. To further avoid heavy computation loads introduced by random sampling algorithms, we approximate a marginal posterior by using Laplace's method at the optimal solution of log-posterior distribution. Experimental results on both 2D synthetic data and real 3D brain magnetic resonance imaging (MRI) scans demonstrate that our method is significantly faster than the state-of-the-art diffeomorphic registration uncertainty quantification algorithms, while producing comparable results.
Lee TC, Guenette JP, Moses ZB, Chi JH. MRI-Guided Cryoablation of Epidural Malignancies in the Spinal Canal Resulting in Neural Decompression and Regrowth of Bone. AJR Am J Roentgenol. 2019;212 (1) :205-8.Abstract
OBJECTIVE: The purpose of this article is to describe the use of MRI to safely monitor cryoablation for the treatment of spinal epidural malignancies. CONCLUSION: Use of MRI guidance to monitor percutaneous cryoablation allows ablation margins more distinct than those allowed by heat-based ablation modalities. MRI-guided cryoablation is a feasible option for treating epidural tumors involving the spinal canal, resulting in successful decompression of the tumor away from the spinal cord with regrowth of previously eroded bone around the spinal canal.
Basu SS, McMinn MH, Giménez-Cassina Lopéz B, Regan MS, Randall EC, Clark AR, Cox CR, Agar NYR. Metal Oxide Laser Ionization Mass Spectrometry Imaging (MOLI MSI) Using Cerium(IV) Oxide. Anal Chem. 2019;91 (10) :6800-7.Abstract
Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is a powerful technique for spatially resolved metabolomics. A variation on MALDI, termed metal oxide laser ionization (MOLI), capitalizes on the unique property of cerium(IV) oxide (CeO) to induce laser-catalyzed fatty acyl cleavage from lipids and has been utilized for bacterial identification. In this study, we present the development and utilization of CeO as an MSI catalyst. The method was developed using a MALDI TOF instrument in negative ion mode, equipped with a high frequency laser. Instrument parameters for MOLI MS fatty acid catalysis with CeO were optimized with phospholipid standards and fatty acid catalysis was confirmed using lipid extracts from reference bacterial strains, and sample preparation was optimized using mouse brain tissue. MOLI MSI was applied to the imaging of normal mouse brain revealing differentiable fatty acyl pools in myelinated and nonmyelinated regions. Similarly, MOLI MSI showed distinct fatty acyl composition in tumor regions of a patient derived xenograft mouse model of glioblastoma. To assess the potential of MOLI MSI to detect pathogens directly from tissue, a pseudoinfection model was prepared by spotting Escherichia coli lipid extracts on mouse brain tissue sections and imaged by MOLI MSI. The spotted regions were molecularly resolved from the supporting mouse brain tissue by the diagnostic odd-chained fatty acids and reflected control bacterial MOLI MS signatures. We describe MOLI MSI for the first time and highlight its potential for spatially resolved fatty acyl analysis, characterization of fatty acyl composition in tumors, and its potential for pathogen detection directly from tissue.
Panda A, Obmann VC, Lo W-C, Margevicius S, Jiang Y, Schluchter M, Patel IJ, Nakamoto D, Badve C, Griswold MA, et al. MR Fingerprinting and ADC Mapping for Characterization of Lesions in the Transition Zone of the Prostate Gland. Radiology. 2019;292 (3) :685-94.Abstract
BackgroundPreliminary studies have shown that MR fingerprinting-based relaxometry combined with apparent diffusion coefficient (ADC) mapping can be used to differentiate normal peripheral zone from prostate cancer and prostatitis. The utility of relaxometry and ADC mapping for the transition zone (TZ) is unknown.PurposeTo evaluate the utility of MR fingerprinting combined with ADC mapping for characterizing TZ lesions.Materials and MethodsTZ lesions that were suspicious for cancer in men who underwent MRI with T2-weighted imaging and ADC mapping ( values, 50-1400 sec/mm), MR fingerprinting with steady-state free precession, and targeted biopsy (60 in-gantry and 15 cognitive targeting) between September 2014 and August 2018 in a single university hospital were retrospectively analyzed. Two radiologists blinded to Prostate Imaging Reporting and Data System (PI-RADS) scores and pathologic diagnosis drew regions of interest on cancer-suspicious lesions and contralateral visually normal TZs (NTZs) on MR fingerprinting and ADC maps. Linear mixed models compared two-reader means of T1, T2, and ADC. Generalized estimating equations logistic regression analysis was used to evaluate both MR fingerprinting and ADC in differentiating NTZ, cancers and noncancers, clinically significant (Gleason score ≥ 7) cancers from clinically insignificant lesions (noncancers and Gleason 6 cancers), and characterizing PI-RADS version 2 category 3 lesions.ResultsIn 67 men (mean age, 66 years ± 8 [standard deviation]) with 75 lesions, targeted biopsy revealed 37 cancers (six PI-RADS category 3 cancers and 31 PI-RADS category 4 or 5 cancers) and 38 noncancers (31 PI-RADS category 3 lesions and seven PI-RADS category 4 or 5 lesions). The T1, T2, and ADC of NTZ (1800 msec ± 150, 65 msec ± 22, and [1.13 ± 0.19] × 10 mm/sec, respectively) were higher than those in cancers (1450 msec ± 110, 36 msec ± 11, and [0.57 ± 0.13] × 10 mm/sec, respectively; < .001 for all). The T1, T2, and ADC in cancers were lower than those in noncancers (1620 msec ± 120, 47 msec ± 16, and [0.82 ± 0.13] × 10 mm/sec, respectively; = .001 for T1 and ADC and = .03 for T2). The area under the receiver operating characteristic curve (AUC) for T1 plus ADC was 0.94 for separation. T1 and ADC in clinically significant cancers (1440 msec ± 140 and [0.58 ± 0.14] × 10 mm/sec, respectively) were lower than those in clinically insignificant lesions (1580 msec ± 120 and [0.75 ± 0.17] × 10 mm/sec, respectively; = .001 for all). The AUC for T1 plus ADC was 0.81 for separation. Within PI-RADS category 3 lesions, T1 and ADC of cancers (1430 msec ± 220 and [0.60 ± 0.17] × 10 mm/sec, respectively) were lower than those of noncancers (1630 msec ± 120 and [0.81 ± 0.13] × 10 mm/sec, respectively; = .006 for T1 and = .004 for ADC). The AUC for T1 was 0.79 for differentiating category 3 lesions.ConclusionMR fingerprinting-based relaxometry combined with apparent diffusion coefficient mapping may improve transition zone lesion characterization.© RSNA, 2019
Zhou H, Zhang T, Jagadeesan J. Re-weighting and 1-Point RANSAC-Based P nP Solution to Handle Outliers. IEEE Trans Pattern Anal Mach Intell. 2019;41 (12) :3022-33.Abstract
The ability to handle outliers is essential for performing the perspective- n-point (P nP) approach in practical applications, but conventional RANSAC+P3P or P4P methods have high time complexities. We propose a fast P nP solution named R1PP nP to handle outliers by utilizing a soft re-weighting mechanism and the 1-point RANSAC scheme. We first present a P nP algorithm, which serves as the core of R1PP nP, for solving the P nP problem in outlier-free situations. The core algorithm is an optimal process minimizing an objective function conducted with a random control point. Then, to reduce the impact of outliers, we propose a reprojection error-based re-weighting method and integrate it into the core algorithm. Finally, we employ the 1-point RANSAC scheme to try different control points. Experiments with synthetic and real-world data demonstrate that R1PP nP is faster than RANSAC+P3P or P4P methods especially when the percentage of outliers is large, and is accurate. Besides, comparisons with outlier-free synthetic data show that R1PP nP is among the most accurate and fast P nP solutions, which usually serve as the final refinement step of RANSAC+P3P or P4P. Compared with REPP nP, which is the state-of-the-art P nP algorithm with an explicit outliers-handling mechanism, R1PP nP is slower but does not suffer from the percentage of outliers limitation as REPP nP.
Guenette JP, Ben-Shlomo N, Jayender J, Seethamraju RT, Kimbrell V, Tran N-A, Huang RY, Kim CJ, Kass JI, Corrales CE, et al. MR Imaging of the Extracranial Facial Nerve with the CISS Sequence. AJNR Am J Neuroradiol. 2019;40 (11) :1954-9.Abstract
BACKGROUND AND PURPOSE: MR imaging is not routinely used to image the extracranial facial nerve. The purpose of this study was to determine the extent to which this nerve can be visualized with a CISS sequence and to determine the feasibility of using that sequence for locating the nerve relative to tumor. MATERIALS AND METHODS: Thirty-two facial nerves in 16 healthy subjects and 4 facial nerves in 4 subjects with parotid gland tumors were imaged with an axial CISS sequence protocol that included 0.8-mm isotropic voxels on a 3T MR imaging system with a 64-channel head/neck coil. Four observers independently segmented the 32 healthy subject nerves. Segmentations were compared by calculating average Hausdorff distance values and Dice similarity coefficients. RESULTS: The primary bifurcation of the extracranial facial nerve into the superior temporofacial and inferior cervicofacial trunks was visible on all 128 segmentations. The mean of the average Hausdorff distances was 1.2 mm (range, 0.3-4.6 mm). Dice coefficients ranged from 0.40 to 0.82. The relative position of the facial nerve to the tumor could be inferred in all 4 tumor cases. CONCLUSIONS: The facial nerve can be seen on CISS images from the stylomastoid foramen to the temporofacial and cervicofacial trunks, proximal to the parotid plexus. Use of a CISS protocol is feasible in the clinical setting to determine the location of the facial nerve relative to tumor.
Luo J, Sedghi A, Popuri K, Cobzas D, Zhang M, Preiswerk F, Toews M, Golby A, Sugiyama M, Wells WIIIM, et al. On the Applicability of Registration Uncertainty, in MICCAI 2019. Vol LNCS 11765. Shenzhen, China: Springer ; 2019 :410-9.Abstract
Estimating the uncertainty in (probabilistic) image registration enables, e.g., surgeons to assess the operative risk based on the trustworthiness of the registered image data. If surgeons receive inaccurately calculated registration uncertainty and misplace unwarranted confidence in the alignment solutions, severe consequences may result. For probabilistic image registration (PIR), the predominant way to quantify the registration uncertainty is using summary statistics of the distribution of transformation parameters. The majority of existing research focuses on trying out different summary statistics as well as means to exploit them. Distinctively, in this paper, we study two rarely examined topics: (1) whether those summary statistics of the transformation distribution most informatively represent the registration uncertainty; (2) Does utilizing the registration uncertainty always be beneficial. We show that there are two types of uncertainties: the transformation uncertainty, Ut, and label uncertainty Ul. The conventional way of using Ut to quantify Ul is inappropriate and can be misleading. By a real data experiment, we also share a potentially critical finding that making use of the registration uncertainty may not always be an improvement.
Luo MICCAI 2019
Miller K, Joldes GR, Bourantas G, Warfield SK, Hyde DE, Kikinis R, Wittek A. Biomechanical Modeling and Computer Simulation of the Brain during Neurosurgery. Int J Numer Method Biomed Eng. 2019;35 (10) :e3250.Abstract
Computational biomechanics of the brain for neurosurgery is an emerging area of research recently gaining in importance and practical applications. This review paper presents the contributions of the Intelligent Systems for Medicine Laboratory and its collaborators to this field, discussing the modeling approaches adopted and the methods developed for obtaining the numerical solutions. We adopt a physics-based modeling approach and describe the brain deformation in mechanical terms (such as displacements, strains, and stresses), which can be computed using a biomechanical model, by solving a continuum mechanics problem. We present our modeling approaches related to geometry creation, boundary conditions, loading, and material properties. From the point of view of solution methods, we advocate the use of fully nonlinear modeling approaches, capable of capturing very large deformations and nonlinear material behavior. We discuss finite element and meshless domain discretization, the use of the total Lagrangian formulation of continuum mechanics, and explicit time integration for solving both time-accurate and steady-state problems. We present the methods developed for handling contacts and for warping 3D medical images using the results of our simulations. We present two examples to showcase these methods: brain shift estimation for image registration and brain deformation computation for neuronavigation in epilepsy treatment.
Fan G, Liu H, Wu Z, Li Y, Feng C, Wang D, Luo J, Wells WM, He S. Deep Learning-Based Automatic Segmentation of Lumbosacral Nerves on CT for Spinal Intervention: A Translational Study. AJNR Am J Neuroradiol. 2019;40 (6) :1074-81.Abstract
BACKGROUND AND PURPOSE: 3D reconstruction of a targeted area ("safe" triangle and Kambin triangle) may benefit the viability assessment of transforaminal epidural steroid injection, especially at the L5/S1 level. However, manual segmentation of lumbosacral nerves for 3D reconstruction is time-consuming. The aim of this study was to investigate the feasibility of deep learning-based segmentation of lumbosacral nerves on CT and the reconstruction of the safe triangle and Kambin triangle. MATERIALS AND METHODS: A total of 50 cases of spinal CT were manually labeled for lumbosacral nerves and bones using Slicer 4.8. The ratio of training/validation/testing was 32:8:10. A 3D U-Net was adopted to build the model SPINECT for automatic segmentations of lumbosacral structures. The Dice score, pixel accuracy, and Intersection over Union were computed to assess the segmentation performance of SPINECT. The areas of Kambin and safe triangles were measured to validate the 3D reconstruction. RESULTS: The results revealed successful segmentation of lumbosacral bone and nerve on CT. The average pixel accuracy for bone was 0.940, and for nerve, 0.918. The average Intersection over Union for bone was 0.897 and for nerve, 0.827. The Dice score for bone was 0.945, and for nerve, it was 0.905. There were no significant differences in the quantified Kambin triangle or safe triangle between manually segmented images and automatically segmented images ( > .05). CONCLUSIONS: Deep learning-based automatic segmentation of lumbosacral structures (nerves and bone) on routine CT is feasible, and SPINECT-based 3D reconstruction of safe and Kambin triangles is also validated.
Lemaire J-J, De Salles A, Coll G, El Ouadih Y, Chaix R, Coste J, Durif F, Makris N, Kikinis R. MRI Atlas of the Human Deep Brain. Front Neurol. 2019;10 :851.Abstract
Mastering detailed anatomy of the human deep brain in clinical neurosciences is challenging. Although numerous pioneering works have gathered a large dataset of structural and topographic information, it is still difficult to transfer this knowledge into practice, even with advanced magnetic resonance imaging techniques. Thus, classical histological atlases continue to be used to identify structures for stereotactic targeting in functional neurosurgery. Physicians mainly use these atlases as a template co-registered with the patient's brain. However, it is possible to directly identify stereotactic targets on MRI scans, enabling personalized targeting. In order to help clinicians directly identify deep brain structures relevant to present and future medical applications, we built a volumetric MRI atlas of the deep brain (MDBA) on a large scale (infra millimetric). Twelve hypothalamic, 39 subthalamic, 36 telencephalic, and 32 thalamic structures were identified, contoured, and labeled. Nineteen coronal, 18 axial, and 15 sagittal MRI plates were created. Although primarily designed for direct labeling, the anatomic space was also subdivided in twelfths of AC-PC distance, leading to proportional scaling in the coronal, axial, and sagittal planes. This extensive work is now available to clinicians and neuroscientists, offering another representation of the human deep brain ([] [hal-02116633]). The atlas may also be used by computer scientists who are interested in deciphering the topography of this complex region.
Patel NA, Li G, Shang W, Wartenberg M, Heffter T, Burdette EC, Iordachita I, Tokuda J, Hata N, Tempany CM, et al. System Integration and Preliminary Clinical Evaluation of a Robotic System for MRI-Guided Transperineal Prostate Biopsy. J Med Robot Res. 2019;4 (2).Abstract
This paper presents the development, preclinical evaluation, and preliminary clinical study of a robotic system for targeted transperineal prostate biopsy under direct interventional magnetic resonance imaging (MRI) guidance. The clinically integrated robotic system is developed based on a modular design approach, comprised of surgical navigation application, robot control software, MRI robot controller hardware, and robotic needle placement manipulator. The system provides enabling technologies for MRI-guided procedures. It can be easily transported and setup for supporting the clinical workflow of interventional procedures, and the system is readily extensible and reconfigurable to other clinical applications. Preclinical evaluation of the system is performed with phantom studies in a 3 Tesla MRI scanner, rehearsing the proposed clinical workflow, and demonstrating an in-plane targeting error of 1.5mm. The robotic system has been approved by the institutional review board (IRB) for clinical trials. A preliminary clinical study is conducted with the patient consent, demonstrating the targeting errors at two biopsy target sites to be 4.0 and 3.7, which is sufficient to target a clinically significant tumor foci. First-in-human trials to evaluate the system's effectiveness and accuracy for MR image-guide prostate biopsy are underway.
Machado I, Toews M, George E, Unadkat P, Essayed W, Luo J, Teodoro P, Carvalho H, Martins J, Golland P, et al. Deformable MRI-Ultrasound Registration using Correlation-based Attribute Matching for Brain Shift Correction: Accuracy and Generality in Multi-site Data. Neuroimage. 2019;202 :116094.Abstract
Intraoperative tissue deformation, known as brain shift, decreases the benefit of using preoperative images to guide neurosurgery. Non-rigid registration of preoperative magnetic resonance (MR) to intraoperative ultrasound (US) has been proposed as a means to compensate for brain shift. We focus on the initial registration from MR to predurotomy US. We present a method that builds on previous work to address the need for accuracy and generality of MR-iUS registration algorithms in multi-site clinical data. To improve accuracy of registration, we use high-dimensional texture attributes instead of image intensities and propose to replace the standard difference-based attribute matching with correlation-based attribute matching. We also present a strategy that deals explicitly with the large field-of-view mismatch between MR and iUS images. We optimize key parameters across independent MR-iUS brain tumor datasets acquired at three different institutions, with a total of 43 tumor patients and 758 corresponding landmarks to validate the registration algorithm. Despite differences in imaging protocols, patient demographics and landmark distributions, our algorithm was able to reduce landmark errors prior to registration in three data sets (5.37 ± 4.27, 4.18 ± 1.97 and 6.18 ± 3.38 mm, respectively) to a consistently low level (2.28 ± 0.71, 2.08 ± 0.37 and 2.24 ± 0.78 mm, respectively). Our algorithm is compared to 15 other algorithms that have been previously tested on MR-iUS registration and it is competitive with the state-of-the-art on multiple datasets. We show that our algorithm has one of the lowest errors in all datasets (accuracy), and this is achieved while sticking to a fixed set of parameters for multi-site data (generality). In contrast, other algorithms/tools of similar performance need per-dataset parameter tuning (high accuracy but lower generality), and those that stick to fixed parameters have larger errors or inconsistent performance (generality but not the top accuracy). We further characterized landmark errors according to brain regions and tumor types, a topic so far missing in the literature. We found that landmark errors were higher in high-grade than low-grade glioma patients, and higher in tumor regions than in other brain regions.