Publications

2019
Stojanovski S, Felsky D, Viviano JD, Shahab S, Bangali R, Burton CL, Devenyi GA, O'Donnell LJ, Szatmari P, Chakravarty MM, et al. Polygenic Risk and Neural Substrates of Attention-Deficit/Hyperactivity Disorder Symptoms in Youths With a History of Mild Traumatic Brain Injury. Biol Psychiatry. 2019;85 (5) :408-16.Abstract
BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a major sequela of traumatic brain injury (TBI) in youths. The objective of this study was to examine whether ADHD symptoms are differentially associated with genetic risk and brain structure in youths with and without a history of TBI. METHODS: Medical history, ADHD symptoms, genetic data, and neuroimaging data were obtained from a community sample of youths. ADHD symptom severity was compared between those with and without TBI (TBI n = 418, no TBI n = 3193). The relationship of TBI history, genetic vulnerability, brain structure, and ADHD symptoms was examined by assessing 1) ADHD polygenic score (discovery sample ADHD n = 19,099, control sample n = 34,194), 2) basal ganglia volumes, and 3) fractional anisotropy in the corpus callosum and corona radiata. RESULTS: Youths with TBI reported greater ADHD symptom severity compared with those without TBI. Polygenic score was positively associated with ADHD symptoms in youths without TBI but not in youths with TBI. The negative association between the caudate volume and ADHD symptoms was not moderated by a history of TBI. However, the relationship between ADHD symptoms and structure of the genu of the corpus callosum was negative in youths with TBI and positive in youths without TBI. CONCLUSIONS: The identification of distinct ADHD etiology in youths with TBI provides neurobiological insight into the clinical heterogeneity in the disorder. Results indicate that genetic predisposition to ADHD does not increase the risk for ADHD symptoms associated with TBI. ADHD symptoms associated with TBI may be a result of a mechanical insult rather than neurodevelopmental factors.
Unadkat P, Fumagalli L, Rigolo L, Vangel MG, Young GS, Huang R, Mukundan S, Golby A, Tie Y. Functional MRI Task Comparison for Language Mapping in Neurosurgical Patients. J Neuroimaging. 2019.Abstract
BACKGROUND AND PURPOSE: Language task-based functional MRI (fMRI) is increasingly used for presurgical planning in patients with brain lesions. Different paradigms elicit activations of different components of the language network. The aim of this study is to optimize fMRI clinical usage by comparing the effectiveness of three language tasks for language lateralization and localization in a large group of patients with brain lesions. METHODS: We analyzed fMRI data from a sequential retrospective cohort of 51 patients with brain lesions who underwent presurgical fMRI language mapping. We compared the effectiveness of three language tasks (Antonym Generation, Sentence Completion (SC), and Auditory Naming) for lateralizing language function and for activating cortex within patient-specific regions-of-interest representing eloquent language areas, and assessed the degree of spatial overlap of the areas of activation elicited by each task. RESULTS: The tasks were similarly effective for lateralizing language within the anterior language areas. The SC task produced higher laterality indices within the posterior language areas and had a significantly higher agreement with the clinical report. Dice coefficients between the task pairs were in the range of .351-.458, confirming substantial variation in the components of the language network activated by each task. CONCLUSIONS: SC task consistently produced large activations within the dominant hemisphere and was more effective for lateralizing language within the posterior language areas. The low degree of spatial overlap among the tasks strongly supports the practice of using a battery of tasks to help the surgeon to avoid eloquent language areas.
Yengul SS, Barbone PE, Madore B. Dispersion in Tissue-Mimicking Gels Measured with Shear Wave Elastography and Torsional Vibration Rheometry. Ultrasound Med Biol. 2019;45 (2) :586-604.Abstract
Dispersion, or the frequency dependence of mechanical parameters, is a primary confounding factor in elastography comparisons. We present a study of dispersion in tissue-mimicking gels over a wide frequency band using a combination of ultrasound shear wave elastography (SWE), and a novel torsional vibration rheometry which allows independent mechanical measurement of SWE samples. Frequency-dependent complex shear modulus was measured in homogeneous gelatin hydrogels of two different bloom strengths while controlling for confounding factors such as temperature, water content and material aging. Furthermore, both techniques measured the same physical samples, thereby eliminating possible variation caused by batch-to-batch gel variation, sample geometry differences and boundary artifacts. The wide-band measurement, from 1 to 1800 Hz, captured a 30%-50% increase in the storage modulus and a nearly linear increase with frequency of the loss modulus. The magnitude of the variation suggests that accounting for dispersion is essential for meaningful comparisons between SWE implementations.
Cheng C-C, Preiswerk F, Hoge WS, Kuo T-H, Madore B. Multipathway Multi-echo (MPME) Imaging: All Main MR Parameters Mapped Based on a Single 3D Scan. Magn Reson Med. 2019;81 (3) :1699-1713.Abstract
PURPOSE: Quantitative parameter maps, as opposed to qualitative grayscale images, may represent the future of diagnostic MRI. A new quantitative MRI method is introduced here that requires a single 3D acquisition, allowing good spatial coverage to be achieved in relatively short scan times. METHODS: A multipathway multi-echo sequence was developed, and at least 3 pathways with 2 TEs were needed to generate T , T , T , B , and B maps. The method required the central k-space region to be sampled twice, with the same sequence but with 2 very different nominal flip angle settings. Consequently, scan time was only slightly longer than that of a single scan. The multipathway multi-echo data were reconstructed into parameter maps, for phantom as well as brain acquisitions, in 5 healthy volunteers at 3 T. Spatial resolution, matrix size, and FOV were 1.2 × 1.0 × 1.2 mm , 160 × 192 × 160, and 19.2 × 19.2 × 19.2 cm (whole brain), acquired in 11.5 minutes with minimal acceleration. Validation was performed against T , T , and T maps calculated from gradient-echo and spin-echo data. RESULTS: In Bland-Altman plots, bias and limits of agreement for T and T results in vivo and in phantom were -2.9/±125.5 ms (T in vivo), -4.8/±20.8 ms (T in vivo), -1.5/±18.1 ms (T in phantom), and -5.3/±7.4 ms (T in phantom), for regions of interest including given brain structures or phantom compartments. Due to relatively high noise levels, the current implementation of the approach may prove more useful for region of interest-based as opposed to pixel-based interpretation. CONCLUSIONS: We proposed a novel approach to quantitatively map MR parameters based on a multipathway multi-echo acquisition.
deSouza NM, Tempany CM. A Risk-based Approach to Identifying Oligometastatic Disease on Imaging. Int J Cancer. 2019;144 (3) :422-30.Abstract
Recognition of <3 metastases in <2 organs, particularly in cancers with a known predisposition to oligometastatic disease (OMD) (colorectal, prostate, renal, sarcoma and lung), offers the opportunity to focally treat the lesions identified and confers a survival advantage. The reliability with which OMD is identified depends on the sensitivity of the imaging technique used for detection and may be predicted from phenotypic and genetic factors of the primary tumour, which determine metastatic risk. Whole-body or organ-specific imaging to identify oligometastases requires optimization to achieve maximal sensitivity. Metastatic lesions at multiple locations may require a variety of imaging modalities for best visualisation because the optimal image contrast is determined by tumour biology. Newer imaging techniques used for this purpose require validation. Additionally, rationalisation of imaging strategies is needed, particularly with regard to timing of imaging and follow-up studies. This article reviews the current evidence for the use of imaging for recognising OMD and proposes a risk-based roadmap for identifying patients with true OMD, or at risk of metastatic disease likely to be OM.
Alessandrino F, Taghipour M, Hassanzadeh E, Ziaei A, Vangel M, Fedorov A, Tempany CM, Fennessy FM. Predictive Role of PI-RADSv2 and ADC Parameters in Differentiating Gleason Pattern 3 + 4 and 4 + 3 Prostate Cancer. Abdom Radiol (NY). 2019;44 (1) :279-85.Abstract
PURPOSE: To compare the predictive roles of qualitative (PI-RADSv2) and quantitative assessment (ADC metrics), in differentiating Gleason pattern (GP) 3 + 4 from the more aggressive GP 4 + 3 prostate cancer (PCa) using radical prostatectomy (RP) specimen as the reference standard. METHODS: We retrospectively identified treatment-naïve peripheral (PZ) and transitional zone (TZ) Gleason Score 7 PCa patients who underwent multiparametric 3T prostate MRI (DWI with b value of 0,1400 and where unavailable, 0,500) and subsequent RP from 2011 to 2015. For each lesion identified on MRI, a PI-RADSv2 score was assigned by a radiologist blinded to pathology data. A PI-RADSv2 score ≤ 3 was defined as "low risk," a PI-RADSv2 score ≥ 4 as "high risk" for clinically significant PCa. Mean tumor ADC (ADC), ADC of adjacent normal tissue (ADC), and ADC (ADC/ADC) were calculated. Stepwise regression analysis using tumor location, ADC and ADC, b value, low vs. high PI-RADSv2 score was performed to differentiate GP 3 + 4 from 4 + 3. RESULTS: 119 out of 645 cases initially identified met eligibility requirements. 76 lesions were GP 3 + 4, 43 were 4 + 3. ADC was significantly different between the two GP groups (p = 0.001). PI-RADSv2 score ("low" vs. "high") was not significantly different between the two GP groups (p = 0.17). Regression analysis selected ADC (p = 0.03) and ADC (p = 0.0007) as best predictors to differentiate GP 4 + 3 from 3 + 4. Estimated sensitivity, specificity, and accuracy of the predictive model in differentiating GP 4 + 3 from 3 + 4 were 37, 82, and 66%, respectively. CONCLUSIONS: ADC metrics could differentiate GP 3 + 4 from 4 + 3 PCa with high specificity and moderate accuracy while PI-RADSv2, did not differentiate between these patterns.
2018
Ciris PA, Chiou J-yuan G, Glazer DI, Chao T-C, Tempany-Afdhal CM, Madore B, Maier SE. Accelerated Segmented Diffusion-Weighted Prostate Imaging for Higher Resolution, Higher Geometric Fidelity, and Multi-b Perfusion Estimation. Invest Radiol. 2018.Abstract
PURPOSE: The aim of this study was to improve the geometric fidelity and spatial resolution of multi-b diffusion-weighted magnetic resonance imaging of the prostate. MATERIALS AND METHODS: An accelerated segmented diffusion imaging sequence was developed and evaluated in 25 patients undergoing multiparametric magnetic resonance imaging examinations of the prostate. A reduced field of view was acquired using an endorectal coil. The number of sampled diffusion weightings, or b-factors, was increased to allow estimation of tissue perfusion based on the intravoxel incoherent motion (IVIM) model. Apparent diffusion coefficients measured with the proposed segmented method were compared with those obtained with conventional single-shot echo-planar imaging (EPI). RESULTS: Compared with single-shot EPI, the segmented method resulted in faster acquisition with 2-fold improvement in spatial resolution and a greater than 3-fold improvement in geometric fidelity. Apparent diffusion coefficient values measured with the novel sequence demonstrated excellent agreement with those obtained from the conventional scan (R = 0.91 for bmax = 500 s/mm and R = 0.89 for bmax = 1400 s/mm). The IVIM perfusion fraction was 4.0% ± 2.7% for normal peripheral zone, 6.6% ± 3.6% for normal transition zone, and 4.4% ± 2.9% for suspected tumor lesions. CONCLUSIONS: The proposed accelerated segmented prostate diffusion imaging sequence achieved improvements in both spatial resolution and geometric fidelity, along with concurrent quantification of IVIM perfusion.
Herrmann MD, Clunie DA, Fedorov A, Doyle SW, Pieper S, Klepeis V, Le LP, Mutter GL, Milstone DS, Schultz TJ, et al. Implementing the DICOM Standard for Digital Pathology. J Pathol Inform. 2018;9 :37.Abstract
Background: Digital Imaging and Communications in Medicine (DICOM) is the standard for the representation, storage, and communication of medical images and related information. A DICOM file format and communication protocol for pathology have been defined; however, adoption by vendors and in the field is pending. Here, we implemented the essential aspects of the standard and assessed its capabilities and limitations in a multisite, multivendor healthcare network. Methods: We selected relevant DICOM attributes, developed a program that extracts pixel data and pixel-related metadata, integrated patient and specimen-related metadata, populated and encoded DICOM attributes, and stored DICOM files. We generated the files using image data from four vendor-specific image file formats and clinical metadata from two departments with different laboratory information systems. We validated the generated DICOM files using recognized DICOM validation tools and measured encoding, storage, and access efficiency for three image compression methods. Finally, we evaluated storing, querying, and retrieving data over the web using existing DICOM archive software. Results: Whole slide image data can be encoded together with relevant patient and specimen-related metadata as DICOM objects. These objects can be accessed efficiently from files or through RESTful web services using existing software implementations. Performance measurements show that the choice of image compression method has a major impact on data access efficiency. For lossy compression, JPEG achieves the fastest compression/decompression rates. For lossless compression, JPEG-LS significantly outperforms JPEG 2000 with respect to data encoding and decoding speed. Conclusion: Implementation of DICOM allows efficient access to image data as well as associated metadata. By leveraging a wealth of existing infrastructure solutions, the use of DICOM facilitates enterprise integration and data exchange for digital pathology.
Fedorov A, Schwier M, Clunie D, Herz C, Pieper S, Kikinis R, Tempany C, Fennessy F. An Annotated Test-retest Collection of Prostate Multiparametric MRI. Sci Data. 2018;5 :180281.Abstract
Multiparametric Magnetic Resonance Imaging (mpMRI) is widely used for characterizing prostate cancer. Standard of care use of mpMRI in clinic relies on visual interpretation of the images by an expert. mpMRI is also increasingly used as a quantitative imaging biomarker of the disease. Little is known about repeatability of such quantitative measurements, and no test-retest datasets have been available publicly to support investigation of the technical characteristics of the MRI-based quantification in the prostate. Here we present an mpMRI dataset consisting of baseline and repeat prostate MRI exams for 15 subjects, manually annotated to define regions corresponding to lesions and anatomical structures, and accompanied by region-based measurements. This dataset aims to support further investigation of the repeatability of mpMRI-derived quantitative prostate measurements, study of the robustness and reliability of the automated analysis approaches, and to support development and validation of new image analysis techniques. The manuscript can also serve as an example of the use of DICOM for standardized encoding of the image annotation and quantification results.
Randall EC, Emdal KB, Laramy JK, Kim M, Roos A, Calligaris D, Regan MS, Gupta SK, Mladek AC, Carlson BL, et al. Integrated Mapping of Pharmacokinetics and Pharmacodynamics in a Patient-derived Xenograft Model of Glioblastoma. Nat Commun. 2018;9 (1) :4904.Abstract
Therapeutic options for the treatment of glioblastoma remain inadequate despite concerted research efforts in drug development. Therapeutic failure can result from poor permeability of the blood-brain barrier, heterogeneous drug distribution, and development of resistance. Elucidation of relationships among such parameters could enable the development of predictive models of drug response in patients and inform drug development. Complementary analyses were applied to a glioblastoma patient-derived xenograft model in order to quantitatively map distribution and resulting cellular response to the EGFR inhibitor erlotinib. Mass spectrometry images of erlotinib were registered to histology and magnetic resonance images in order to correlate drug distribution with tumor characteristics. Phosphoproteomics and immunohistochemistry were used to assess protein signaling in response to drug, and integrated with transcriptional response using mRNA sequencing. This comprehensive dataset provides simultaneous insight into pharmacokinetics and pharmacodynamics and indicates that erlotinib delivery to intracranial tumors is insufficient to inhibit EGFR tyrosine kinase signaling.
Peled S, Vangel M, Kikinis R, Tempany CM, Fennessy FM, Fedorov A. Selection of Fitting Model and Arterial Input Function for Repeatability in Dynamic Contrast-Enhanced Prostate MRI. Acad Radiol. 2018.Abstract
RATIONALE AND OBJECTIVES: Analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging is notable for the variability of calculated parameters. The purpose of this study was to evaluate the level of measurement variability and error/variability due to modeling in DCE magnetic resonance imaging parameters. MATERIALS AND METHODS: Two prostate DCE scans were performed on 11 treatment-naïve patients with suspected or confirmed prostate peripheral zone cancer within an interval of less than two weeks. Tumor-suspicious and normal-appearing regions of interest (ROI) in the prostate peripheral zone were segmented. Different Tofts-Kety based models and different arterial input functions, with and without bolus arrival time (BAT) correction, were used to extract pharmacokinetic parameters. The percent repeatability coefficient (%RC) of fitted model parameters K, v, and k was calculated. Paired t-tests comparing parameters in tumor-suspicious ROIs and in normal-appearing tissue evaluated each parameter's sensitivity to pathology. RESULTS: Although goodness-of-fit criteria favored the four-parameter extended Tofts-Kety model with the BAT correction included, the simplest two-parameter Tofts-Kety model overall yielded the best repeatability scores. The best %RC in the tumor-suspicious ROI was 63% for k, 28% for v and 83% for K . The best p values for discrimination between tissues were p <10 for k and K, and p = 0.11 for v. Addition of the BAT correction to the models did not improve repeatability. CONCLUSION: The parameter k, using an arterial input functions directly measured from blood signals, was more repeatable than K. Both K and k values were highly discriminatory between healthy and diseased tissues in all cases. The parameter v had high repeatability but could not distinguish the two tissue types.
Mehrtash A, Ghafoorian M, Pernelle G, Ziaei A, Heslinga FG, Tuncali K, Fedorov A, Kikinis R, Tempany CM, Wells WM, et al. Automatic Needle Segmentation and Localization in MRI with 3D Convolutional Neural Networks: Application to MRI-targeted Prostate Biopsy. IEEE Trans Med Imaging. 2018.Abstract
Image-guidance improves tissue sampling during biopsy by allowing the physician to visualize the tip and trajectory of the biopsy needle relative to the target in MRI, CT, ultrasound, or other relevant imagery. This paper reports a system for fast automatic needle tip and trajectory localization and visualization in MRI that has been developed and tested in the context of an active clinical research program in prostate biopsy. To the best of our knowledge, this is the first reported system for this clinical application, and also the first reported system that leverages deep neural networks for segmentation and localization of needles in MRI across biomedical applications. Needle tip and trajectory were annotated on 583 T2-weighted intra-procedural MRI scans acquired after needle insertion for 71 patients who underwent transperenial MRI-targeted biopsy procedure at our institution. The images were divided into two independent training-validation and test sets at the patient level. A deep 3-dimensional fully convolutional neural network model was developed, trained and deployed on these samples. The accuracy of the proposed method, as tested on previously unseen data, was 2.80 mm average in needle tip detection, and 0.98° in needle trajectory angle. An observer study was designed in which independent annotations by a second observer, blinded to the original observer, were compared to the output of the proposed method. The resultant error was comparable to the measured inter-observer concordance, reinforcing the clinical acceptability of the proposed method. The proposed system has the potential for deployment in clinical routine.
Taghipour M, Ziaei A, Alessandrino F, Hassanzadeh E, Harisinghani M, Vangel M, Tempany CM, Fennessy FM. Investigating the Role of DCE-MRI, over T2 and DWI, in accurate PI-RADS v2 Assessment of Clinically Significant Peripheral Zone Prostate Lesions as Defined at Radical Prostatectomy. Abdom Radiol (NY). 2018.Abstract
PURPOSE: PI-RADS v2 dictates that dynamic contrast-enhanced (DCE) imaging be used to further classify peripheral zone (PZ) cases that receive a diffusion-weighted imaging equivocal score of three (DWI3), a positive DCE resulting in an increase in overall assessment score to a four, indicative of clinically significant prostate cancer (csPCa). However, the accuracy of DCE in predicting csPCa in DWI3 PZ cases is unknown. This study sought to determine the frequency with which DCE changes the PI-RADS v2 DWI3 assessment category, and to determine the overall accuracy of DCE-MRI in equivocal PZ DWI3 lesions. MATERIALS AND METHODS: This is a retrospective study of patients with pathologically proven PCa who underwent prostate mpMRI at 3T and subsequent radical prostatectomy. PI-RADS v2 assessment categories were determined by a radiologist, aware of a diagnosis of PCa, but blinded to final pathology. csPCa was defined as a Gleason score ≥ 7 or extra prostatic extension at pathology review. Performance characteristics and diagnostic accuracy of DCE in assigning a csPCa assessment in PZ lesions were calculated. RESULTS: A total of 271 men with mean age of 59 ± 6 years mean PSA 6.7 ng/mL were included. csPCa was found in 212/271 (78.2%) cases at pathology, 209 of which were localized in the PZ. DCE was necessary to further classify (45/209) of patients who received a score of DWI3. DCE was positive in 29/45 cases, increasing the final PI-RADS v2 assessment category to a category 4, with 16/45 having a negative DCE. When compared with final pathology, DCE was correct in increasing the assessment category in 68.9% ± 7% (31/45) of DWI3 cases. CONCLUSION: DCE increases the accuracy of detection of csPCa in the majority of PZ lesions that receive an equivocal PI-RADS v2 assessment category using DWI.
Moreira P, Patel N, Wartenberg M, Li G, Tuncali K, Heffter T, Burdette EC, Iordachita I, Fischer GS, Hata N, et al. Evaluation of Robot-assisted MRI-guided Prostate Biopsy: Needle Path Analysis during Clinical Trials. Phys Med Biol. 2018;63 (20) :20NT02.Abstract
PURPOSE: While the interaction between a needle and the surrounding tissue is known to cause a significant targeting error in prostate biopsy leading to false-negative results, few studies have demonstrated how it impacts in the actual procedure. We performed a pilot study on robot-assisted MRI-guided prostate biopsy with an emphasis on the in-depth analysis of the needle-tissue interaction in-vivo. Methods: The data were acquired during in-bore transperineal prostate biopsies in patients using a 4 degrees-of-freedom (DoF) MRI-compatible robot. The anatomical structures in the pelvic area and the needle path were reconstructed from MR images, and quantitatively analyzed. We analyzed each structure individually and also proposed a mathematical model to investigate the influence of those structures in the targeting error using the mixed-model regression. Results: The median targeting error in 188 insertions (27 patients) was 6.3mm. Both the individual anatomical structure analysis and the mixed-model analysis showed that the deviation resulted from the contact between the needle and the skin as the main source of error. On contrary, needle bending inside the tissue (expressed as needle curvature) did not vary among insertions with targeting errors above and below the average. The analysis indicated that insertions crossing the bulbospongiosus presented a targeting error lower than the average. The mixed-model analysis demonstrated that the distance between the needle guide and the patient skin, the deviation at the entry point, and the path length inside the pelvic diaphragm had a statistically significant contribution to the targeting error (p<0.05). Conclusions: Our results indicate that the errors associated with the elastic contact between the needle and the skin were more prominent than the needle bending along the insertion. Our findings will help to improve the preoperative planning of transperineal prostate biopsies.
Lasso A, Nam HH, Dinh PV, Pinter C, Fillion-Robin J-C, Pieper S, Jhaveri S, Vimort J-B, Martin K, Asselin M, et al. Interaction with Volume-Rendered Three-Dimensional Echocardiographic Images in Virtual Reality. J Am Soc Echocardiogr. 2018;31 (10) :1158-60.
Basu SS, Randall EC, Regan MS, Lopez BGC, Clark AR, Schmitt ND, Agar JN, Dillon DA, Agar NYR. In Vitro Liquid Extraction Surface Analysis Mass Spectrometry (ivLESA-MS) for Direct Metabolic Analysis of Adherent Cells in Culture. Anal Chem. 2018;90 (8) :4987-91.Abstract
Conventional metabolomic methods include extensive sample preparation steps and long analytical run times, increasing the likelihood of processing artifacts and limiting high throughput applications. We present here in vitro liquid extraction surface analysis mass spectrometry (ivLESA-MS), a variation on LESA-MS, performed directly on adherent cells grown in 96-well cell culture plates. To accomplish this, culture medium was aspirated immediately prior to analysis, and metabolites were extracted using LESA from the cell monolayer surface, followed by nano-electrospray ionization and MS analysis in negative ion mode. We applied this platform to characterize and compare lipidomic profiles of multiple breast cancer cell lines growing in culture (MCF-7, ZR-75-1, MDA-MB-453, and MDA-MB-231) and revealed distinct and reproducible lipidomic signatures between the cell lines. Additionally, we demonstrated time-dependent processing artifacts, underscoring the importance of immediate analysis. ivLESA-MS represents a rapid in vitro metabolomic method, which precludes the need for quenching, cell harvesting, sample preparation, and chromatography, significantly shortening preparation and analysis time while minimizing processing artifacts. This method could be further adapted to test drugs in vitro in a high throughput manner.
van Beek EJR, Kuhl C, Anzai Y, Desmond P, Ehman RL, Gong Q, Gold G, Gulani V, Hall-Craggs M, Leiner T, et al. Value of MRI in Medicine: More Than Just Another Test?. J Magn Reson Imaging. 2018.Abstract
There is increasing scrutiny from healthcare organizations towards the utility and associated costs of imaging. MRI has traditionally been used as a high-end modality, and although shown extremely important for many types of clinical scenarios, it has been suggested as too expensive by some. This editorial will try and explain how value should be addressed and gives some insights and practical examples of how value of MRI can be increased. It requires a global effort to increase accessibility, value for money, and impact on patient management. We hope this editorial sheds some light and gives some indications of where the field may wish to address some of its research to proactively demonstrate the value of MRI. LEVEL OF EVIDENCE: 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018.
Beek JMRI 2018
Guenette JP, Seethamraju RT, Jayender J, Corrales CE, Lee TC. MR Imaging of the Facial Nerve through the Temporal Bone at 3T with a Noncontrast Ultrashort Echo Time Sequence. AJNR Am J Neuroradiol. 2018;39 (10) :1903-6.Abstract
The pointwise encoding time reduction with radial acquisition (PETRA) ultrashort echo time MR imaging sequence at 3T enables visualization of the facial nerve from the brain stem, through the temporal bone, to the stylomastoid foramen without intravenous contrast. Use of the PETRA sequence, or other ultrashort echo time sequences, should be considered in the MR imaging evaluation of certain skull base tumors and perhaps other facial nerve and temporal bone pathologies.
Luo J, Frisken S, Machado I, Zhang M, Pieper S, Golland P, Toews M, Unadkat P, Sedghi A, Zhou H, et al. Using the Variogram for Vector Outlier Screening: Application to Feature-based Image Registration. Int J Comput Assist Radiol Surg. 2018;13 (12) :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.
Zhang F, Wu Y, Norton I, Rigolo L, Rathi Y, Makris N, O'Donnell LJ. An Anatomically Curated Fiber Clustering White Matter Atlas for Consistent White Matter Tract Parcellation across the Lifespan. Neuroimage. 2018;179 :429-47.Abstract
This work presents an anatomically curated white matter atlas to enable consistent white matter tract parcellation across different populations. Leveraging a well-established computational pipeline for fiber clustering, we create a tract-based white matter atlas including information from 100 subjects. A novel anatomical annotation method is proposed that leverages population-based brain anatomical information and expert neuroanatomical knowledge to annotate and categorize the fiber clusters. A total of 256 white matter structures are annotated in the proposed atlas, which provides one of the most comprehensive tract-based white matter atlases covering the entire brain to date. These structures are composed of 58 deep white matter tracts including major long range association and projection tracts, commissural tracts, and tracts related to the brainstem and cerebellar connections, plus 198 short and medium range superficial fiber clusters organized into 16 categories according to the brain lobes they connect. Potential false positive connections are annotated in the atlas to enable their exclusion from analysis or visualization. In addition, the proposed atlas allows for a whole brain white matter parcellation into 800 fiber clusters to enable whole brain connectivity analyses. The atlas and related computational tools are open-source and publicly available. We evaluate the proposed atlas using a testing dataset of 584 diffusion MRI scans from multiple independently acquired populations, across genders, the lifespan (1 day-82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). Experimental results show successful white matter parcellation across subjects from different populations acquired on multiple scanners, irrespective of age, gender or disease indications. Over 99% of the fiber tracts annotated in the atlas were detected in all subjects on average. One advantage in terms of robustness is that the tract-based pipeline does not require any cortical or subcortical segmentations, which can have limited success in young children and patients with brain tumors or other structural lesions. We believe this is the first demonstration of consistent automated white matter tract parcellation across the full lifespan from birth to advanced age.

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