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Chen H, Ding V, Zhu G, Jiang B, Li Y, Boothroyd D, Rezaii P, Bet AM, Paulino A, Weber A, Glushakova OY, Hayes RL, Wintermark M. Association between Blood and CT Imaging Biomarkers in a Cohort of Mild Traumatic Brain Injury Patients. J Neurotrauma 2022; 39:1329-1338. [PMID: 35546284 DOI: 10.1089/neu.2021.0390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE To analyze the relationships between traumatic brain injury (TBI) on CT imaging and blood concentration of glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase-L1 (UCH-L1), and S100B. METHODS This prospective cohort study involved 644 TBI patients referred to Stanford Hospital's Emergency Department between November 2015 and April 2017. Plasma and serum samples of 462 patients were analyzed for levels of GFAP, UCH-L1 and S100B. Glial neuronal ratio (GNR) was calculated as the ratio between GFAP and UCH-L1 concentrations. Admission head CT scans were reviewed for TBI imaging common data elements, and performance of biomarkers for identifying TBI was assessed via area under the receiver operating characteristic curve (ROC). We also dichotomized biomarkers at established thresholds and estimated standard measures of classification accuracy. We assessed the ability of GFAP, UCH-L1 and GNR to discriminate small and large/diffuse lesions based on CT imaging using an ROC analysis. RESULTS In our cohort of mostly mild TBI patients, GFAP was significantly more accurate in detecting all types of acute brain injuries than UCH-L1 in terms of area under the curves (AUC) values (P<0.001), and also compared to S100B (P<0.001). UCH-L1 and S100B had similar performance (comparable AUC values, P=0.342). Sensitivity exceeded 0.8 for each biomarker across all different types of TBI injuries, and no significant differences were observed by type of injury. Significant differences of GFAP and GNR distinguished between small lesions and large/diffuse lesions in all injuries (P=0.004, P=0.007). CONCLUSIONS GFAP, UCH-L1, and S100B show high sensitivity and negative predictive values for all types of TBI lesions on head CT. A combination of negative blood biomarkers (GFAP and UCH-L1) in a patient suspected of TBI may be used to safely obviate the need for a head CT scan. GFAP is a promising indicator to discriminate between small and large/diffuse TBI lesions.
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Affiliation(s)
- Hui Chen
- Stanford University School of Medicine, 10624, Radiology, Stanford, California, United States;
| | - Victoria Ding
- Stanford University, 6429, Department of Medicine, Quantitative Sciences Unit, Stanford, California, United States;
| | - Guangming Zhu
- Stanford University School of Medicine, 10624, Radiology, Stanford, California, United States;
| | - Bin Jiang
- Stanford University Department of Radiology, 209657, Radiology, Neuroradiology Division, 300 Pasteur Dr., Stanford, California, United States, 94305;
| | - Ying Li
- Stanford University School of Medicine, 10624, Stanford, California, United States;
| | - Derek Boothroyd
- Stanford University, 6429, Department of Medicine, Stanford, California, United States;
| | - Paymon Rezaii
- Stanford University, Department of Radiology, 300 Pasteur Drive, Room S047, Stanford, California, United States, 94305;
| | - Anthony Marco Bet
- Stanford University, Department of Neurosurgery, 300 Pasteur Dr, Stanford, California, United States, 94305;
| | - Amy Paulino
- Banyan Biomarkers Inc San Diego, 506046, 16470 W Bernardo Drive, Suite 100, San Diego, California, United States, 92127;
| | - Art Weber
- Banyan Biomarkers Inc San Diego, 506046, Clinical Affairs, 16470 West Bernardo Drive, Suite 100, San Diego, California, United States, 92127;
| | - Olena Y Glushakova
- Virginia Commonwealth University, Department of Neurosurgery, PO Box 980631, Richmond, Virginia, United States, 23298-0631;
| | - Ronald L Hayes
- Banyan Biomarkers, Inc., Director, Center of Innovative Research, 12085 Research Dr., Alachua, Florida, United States, 32615.,United States;
| | - Max Wintermark
- Stanford University Department of Radiology, 209657, Radiology, Neuroradiology Division, 300 Pasteur Dr, Grant Building S047, Stanford, California, United States, 94305-5105;
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Jin MC, Ho AL, Feng AY, Medress ZA, Pendharkar AV, Rezaii P, Ratliff JK, Desai AM. Prediction of Discharge Status and Readmissions after Resection of Intradural Spinal Tumors. Neurospine 2022; 19:133-145. [PMID: 35378587 PMCID: PMC8987552 DOI: 10.14245/ns.2143244.622] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 02/07/2022] [Indexed: 11/19/2022] Open
Abstract
Objective Intradural spinal tumors are uncommon and while associations between clinical characteristics and surgical outcomes have been explored, there remains a paucity of literature unifying diverse predictors into an integrated risk model. To predict postresection outcomes for patients with spinal tumors.
Methods IBM MarketScan Claims Database was queried for adult patients receiving surgery for intradural tumors between 2007 and 2016. Primary outcomes-of-interest were nonhome discharge and 90-day postdischarge readmissions. Secondary outcomes included hospitalization duration and postoperative complications. Risk modeling was developed using a regularized logistic regression framework (LASSO, least absolute shrinkage and selection operator) and validated in a withheld subset.
Results A total of 5,060 adult patients were included. Most surgeries utilized a posterior approach (n=5,023, 99.3%) and tumors were most commonly found in the thoracic region (n=1,941, 38.4%), followed by the lumbar (n=1,781, 35.2%) and cervical (n=1,294, 25.6%) regions. Compared to models using only tumor-specific or patient-specific features, our integrated models demonstrated better discrimination (area under the curve [AUC] [nonhome discharge] = 0.786; AUC [90-day readmissions] = 0.693) and accuracy (Brier score [nonhome discharge] = 0.155; Brier score [90-day readmissions] = 0.093). Compared to those predicted to be lowest risk, patients predicted to be highest-risk for nonhome discharge required continued care 16.3 times more frequently (64.5% vs. 3.9%). Similarly, patients predicted to be at highest risk for postdischarge readmissions were readmitted 7.3 times as often as those predicted to be at lowest risk (32.6% vs. 4.4%).
Conclusion Using a diverse set of clinical characteristics spanning tumor-, patient-, and hospitalization-derived data, we developed and validated risk models integrating diverse clinical data for predicting nonhome discharge and postdischarge readmissions.
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Affiliation(s)
- Michael C. Jin
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Allen L. Ho
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Austin Y. Feng
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Zachary A. Medress
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Arjun V. Pendharkar
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Paymon Rezaii
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - John K. Ratliff
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Atman M. Desai
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
- Corresponding Author Atman M. Desai https://orcid.org/0000-0001-8387-3808 Department of Neurosurgery, Stanford University, Director of Neurosurgical Spine Oncology, 213 Quarry Road, 4th Fl MC 5958, Palo Alto, CA 94304, USA
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Deo DR, Rezaii P, Hochberg LR, M Okamura A, Shenoy KV, Henderson JM. Effects of Peripheral Haptic Feedback on Intracortical Brain-Computer Interface Control and Associated Sensory Responses in Motor Cortex. IEEE Trans Haptics 2021; 14:762-775. [PMID: 33844633 PMCID: PMC8745032 DOI: 10.1109/toh.2021.3072615] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Intracortical brain-computer interfaces (iBCIs) provide people with paralysis a means to control devices with signals decoded from brain activity. Despite recent impressive advances, these devices still cannot approach able-bodied levels of control. To achieve naturalistic control and improved performance of neural prostheses, iBCIs will likely need to include proprioceptive feedback. With the goal of providing proprioceptive feedback via mechanical haptic stimulation, we aim to understand how haptic stimulation affects motor cortical neurons and ultimately, iBCI control. We provided skin shear haptic stimulation as a substitute for proprioception to the back of the neck of a person with tetraplegia. The neck location was determined via assessment of touch sensitivity using a monofilament test kit. The participant was able to correctly report skin shear at the back of the neck in 8 unique directions with 65% accuracy. We found motor cortical units that exhibited sensory responses to shear stimuli, some of which were strongly tuned to the stimuli and well modeled by cosine-shaped functions. In this article, we also demonstrated online iBCI cursor control with continuous skin-shear feedback driven by decoded command signals. Cursor control performance increased slightly but significantly when the participant was given haptic feedback, compared to the purely visual feedback condition.
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Gendreau JL, Kim LH, Prins PN, D’Souza M, Rezaii P, Pendharkar AV, Sussman ES, Ho AL, Desai AM. Outcomes After Cervical Disc Arthroplasty Versus Stand-Alone Anterior Cervical Discectomy and Fusion: A Meta-Analysis. Global Spine J 2020; 10:1046-1056. [PMID: 32875831 PMCID: PMC7645085 DOI: 10.1177/2192568219888448] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
STUDY DESIGN Systemic review and meta-analysis. OBJECTIVES To review and compare surgical outcomes for patients undergoing stand-alone anterior cervical discectomy and fusion (ACDF) versus cervical disc arthroplasty (CDA) for the treatment of cervical spine disease. METHODS A systematic search was performed on PubMed, Medline, and the Cochrane Library. Comparative trials measuring outcomes of patients undergoing CDA and stand-alone ACDF for degenerative spine disease in the last 10 years were selected for inclusion. After data extraction and quality assessment, statistical analysis was performed with R software metafor package. The random-effects model was used if there was heterogeneity between studies; otherwise, the fixed-effects model was used. RESULTS In total, 12 studies including 859 patients were selected for inclusion in the meta-analysis. Patients undergoing stand-alone ACDF had a statistically significant increase in postoperative segmental angles (mean difference 0.85° [95% confidence interval = 0.35° to 1.35°], P = .0008). Patients undergoing CDA had a decreased rate of developing adjacent segmental degeneration (risk ratio = 0.56 [95% confidence interval = -0.06 to 1.18], P = .0745). Neck Disability Index, Japanese Orthopedic Association score, Visual Analogue Scale of the arm and neck, as well as postoperative cervical angles were similar between the 2 treatments. CONCLUSIONS When compared with CDA, stand-alone ACDF offers similar clinical outcomes for patients and leads to increased postoperative segmental angles. We encourage further blinded randomized trials to compare rates of adjacent segmental degeneration and other postoperative outcomes between these 2 treatments options.
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Mills BD, Goubran M, Parivash SN, Dennis EL, Rezaii P, Akers C, Bian W, Mitchell LA, Boldt B, Douglas D, Sami S, Mouchawar N, Wilson EW, DiGiacomo P, Parekh M, Do H, Lopez J, Rosenberg J, Camarillo D, Grant G, Wintermark M, Zeineh M. Longitudinal alteration of cortical thickness and volume in high-impact sports. Neuroimage 2020; 217:116864. [DOI: 10.1016/j.neuroimage.2020.116864] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 04/16/2020] [Accepted: 04/17/2020] [Indexed: 01/08/2023] Open
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Stavisky SD, Willett FR, Wilson GH, Murphy BA, Rezaii P, Avansino DT, Memberg WD, Miller JP, Kirsch RF, Hochberg LR, Ajiboye AB, Druckmann S, Shenoy KV, Henderson JM. Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis. eLife 2019; 8:e46015. [PMID: 31820736 PMCID: PMC6954053 DOI: 10.7554/elife.46015] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 11/14/2019] [Indexed: 01/20/2023] Open
Abstract
Speaking is a sensorimotor behavior whose neural basis is difficult to study with single neuron resolution due to the scarcity of human intracortical measurements. We used electrode arrays to record from the motor cortex 'hand knob' in two people with tetraplegia, an area not previously implicated in speech. Neurons modulated during speaking and during non-speaking movements of the tongue, lips, and jaw. This challenges whether the conventional model of a 'motor homunculus' division by major body regions extends to the single-neuron scale. Spoken words and syllables could be decoded from single trials, demonstrating the potential of intracortical recordings for brain-computer interfaces to restore speech. Two neural population dynamics features previously reported for arm movements were also present during speaking: a component that was mostly invariant across initiating different words, followed by rotatory dynamics during speaking. This suggests that common neural dynamical motifs may underlie movement of arm and speech articulators.
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Affiliation(s)
- Sergey D Stavisky
- Department of NeurosurgeryStanford UniversityStanfordUnited States
- Department of Electrical EngineeringStanford UniversityStanfordUnited States
| | - Francis R Willett
- Department of NeurosurgeryStanford UniversityStanfordUnited States
- Department of Electrical EngineeringStanford UniversityStanfordUnited States
| | - Guy H Wilson
- Neurosciences ProgramStanford UniversityStanfordUnited States
| | - Brian A Murphy
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandUnited States
- FES Center, Rehab R&D ServiceLouis Stokes Cleveland Department of Veterans Affairs Medical CenterClevelandUnited States
| | - Paymon Rezaii
- Department of NeurosurgeryStanford UniversityStanfordUnited States
| | | | - William D Memberg
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandUnited States
- FES Center, Rehab R&D ServiceLouis Stokes Cleveland Department of Veterans Affairs Medical CenterClevelandUnited States
| | - Jonathan P Miller
- FES Center, Rehab R&D ServiceLouis Stokes Cleveland Department of Veterans Affairs Medical CenterClevelandUnited States
- Department of NeurosurgeryUniversity Hospitals Cleveland Medical CenterClevelandUnited States
| | - Robert F Kirsch
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandUnited States
- FES Center, Rehab R&D ServiceLouis Stokes Cleveland Department of Veterans Affairs Medical CenterClevelandUnited States
| | - Leigh R Hochberg
- VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D ServiceProvidence VA Medical CenterProvidenceUnited States
- Center for Neurotechnology and Neurorecovery, Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonUnited States
- School of Engineering and Robert J. & Nandy D. Carney Institute for Brain ScienceBrown UniversityProvidenceUnited States
| | - A Bolu Ajiboye
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandUnited States
- FES Center, Rehab R&D ServiceLouis Stokes Cleveland Department of Veterans Affairs Medical CenterClevelandUnited States
| | - Shaul Druckmann
- Department of NeurobiologyStanford UniversityStanfordUnited States
| | - Krishna V Shenoy
- Department of Electrical EngineeringStanford UniversityStanfordUnited States
- Department of NeurobiologyStanford UniversityStanfordUnited States
- Department of BioengineeringStanford UniversityStanfordUnited States
- Howard Hughes Medical Institute, Stanford UniversityStanfordUnited States
- Wu Tsai Neurosciences InstituteStanford UniversityStanfordUnited States
- Bio-X ProgramStanford UniversityStanfordUnited States
| | - Jaimie M Henderson
- Department of NeurosurgeryStanford UniversityStanfordUnited States
- Wu Tsai Neurosciences InstituteStanford UniversityStanfordUnited States
- Bio-X ProgramStanford UniversityStanfordUnited States
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7
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Parivash SN, Goubran M, Mills BD, Rezaii P, Thaler C, Wolman D, Bian W, Mitchell LA, Boldt B, Douglas D, Wilson EW, Choi J, Xie L, Yushkevich PA, DiGiacomo P, Wongsripuemtet J, Parekh M, Fiehler J, Do H, Lopez J, Rosenberg J, Camarillo D, Grant G, Wintermark M, Zeineh M. Longitudinal Changes in Hippocampal Subfield Volume Associated with Collegiate Football. J Neurotrauma 2019; 36:2762-2773. [PMID: 31044639 PMCID: PMC7872005 DOI: 10.1089/neu.2018.6357] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Collegiate football athletes are subject to repeated traumatic brain injuriesthat may cause brain injury. The hippocampus is composed of several distinct subfields with possible differential susceptibility to injury. The aim of this study is to determine whether there are longitudinal changes in hippocampal subfield volume in collegiate football. A prospective cohort study was conducted over a 5-year period tracking 63 football and 34 volleyball male collegiate athletes. Athletes underwent high-resolution structural magnetic resonance imaging, and automated segmentation provided hippocampal subfield volumes. At baseline, football (n = 59) athletes demonstrated a smaller subiculum volume than volleyball (n = 32) athletes (-67.77 mm3; p = 0.012). A regression analysis performed within football athletes similarly demonstrated a smaller subiculum volume among those at increased concussion risk based on athlete position (p = 0.001). For the longitudinal analysis, a linear mixed-effects model assessed the interaction between sport and time, revealing a significant decrease in cornu ammonis area 1 (CA1) volume in football (n = 36) athletes without an in-study concussion compared to volleyball (n = 23) athletes (volume difference per year = -35.22 mm3; p = 0.005). This decrease in CA1 volume over time was significant when football athletes were examined in isolation from volleyball athletes (p = 0.011). Thus, this prospective, longitudinal study showed a decrease in CA1 volume over time in football athletes, in addition to baseline differences that were identified in the downstream subiculum. Hippocampal changes may be important to study in high-contact sports.
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Affiliation(s)
| | - Maged Goubran
- Department of Radiology, Stanford University, Stanford, California
| | - Brian D. Mills
- Department of Radiology, Stanford University, Stanford, California
| | - Paymon Rezaii
- Department of Neurosurgery, Stanford University, Stanford, California
| | - Christian Thaler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Dylan Wolman
- Department of Radiology, Stanford University, Stanford, California
| | - Wei Bian
- Department of Radiology, Stanford University, Stanford, California
| | - Lex A. Mitchell
- Department of Radiology, Uniformed Services University of the Health Sciences, Bethesda, Maryland
- Department of Radiology, Tripler Army Medical Center, Honolulu, Hawaii
| | - Brian Boldt
- Department of Radiology, Uniformed Services University of the Health Sciences, Bethesda, Maryland
- Department of Radiology, Madigan Army Medical Center, Tacoma, Washington
| | - David Douglas
- Department of Radiology, Stanford University, Stanford, California
| | - Eugene W. Wilson
- Department of Radiology, Stanford University, Stanford, California
| | - Jay Choi
- Department of Radiology, Stanford University, Stanford, California
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Phil DiGiacomo
- Department of Radiology, Stanford University, Stanford, California
| | | | - Mansi Parekh
- Department of Radiology, Stanford University, Stanford, California
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Huy Do
- Department of Radiology, Stanford University, Stanford, California
| | - Jaime Lopez
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, California
| | | | - David Camarillo
- Department of Bioengineering, Stanford University, Stanford, California
| | - Gerald Grant
- Department of Neurosurgery, Stanford University, Stanford, California
| | - Max Wintermark
- Department of Radiology, Stanford University, Stanford, California
| | - Michael Zeineh
- Department of Radiology, Stanford University, Stanford, California
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Stavisky SD, Rezaii P, Willett FR, Hochberg LR, Shenoy KV, Henderson JM. Decoding Speech from Intracortical Multielectrode Arrays in Dorsal "Arm/Hand Areas" of Human Motor Cortex. Annu Int Conf IEEE Eng Med Biol Soc 2019; 2018:93-97. [PMID: 30440349 DOI: 10.1109/embc.2018.8512199] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Neural prostheses are being developed to restore speech to people with neurological injury or disease. A key design consideration is where and how to access neural correlates of intended speech. Most prior work has examined cortical field potentials at a coarse resolution using electroencephalography (EEG) or medium resolution using electrocorticography (ECoG). The few studies of speech with single-neuron resolution recorded from ventral areas known to be part of the speech network. Here, we recorded from two 96- electrode arrays chronically implanted into the 'hand knob' area of motor cortex while a person with tetraplegia spoke. Despite being located in an area previously demonstrated to modulate during attempted arm movements, many electrodes' neuronal firing rates responded to speech production. In offline analyses, we could classify which of 9 phonemes (plus silence) was spoken with 81% single-trial accuracy using a combination of spike rate and local field potential (LFP) power. This suggests that high-fidelity speech prostheses may be possible using large-scale intracortical recordings in motor cortical areas involved in controlling speech articulators.
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Willett FR, Young DR, Murphy BA, Memberg WD, Blabe CH, Pandarinath C, Stavisky SD, Rezaii P, Saab J, Walter BL, Sweet JA, Miller JP, Henderson JM, Shenoy KV, Simeral JD, Jarosiewicz B, Hochberg LR, Kirsch RF, Bolu Ajiboye A. Principled BCI Decoder Design and Parameter Selection Using a Feedback Control Model. Sci Rep 2019; 9:8881. [PMID: 31222030 PMCID: PMC6586941 DOI: 10.1038/s41598-019-44166-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Accepted: 03/04/2019] [Indexed: 02/01/2023] Open
Abstract
Decoders optimized offline to reconstruct intended movements from neural recordings sometimes fail to achieve optimal performance online when they are used in closed-loop as part of an intracortical brain-computer interface (iBCI). This is because typical decoder calibration routines do not model the emergent interactions between the decoder, the user, and the task parameters (e.g. target size). Here, we investigated the feasibility of simulating online performance to better guide decoder parameter selection and design. Three participants in the BrainGate2 pilot clinical trial controlled a computer cursor using a linear velocity decoder under different gain (speed scaling) and temporal smoothing parameters and acquired targets with different radii and distances. We show that a user-specific iBCI feedback control model can predict how performance changes under these different decoder and task parameters in held-out data. We also used the model to optimize a nonlinear speed scaling function for the decoder. When used online with two participants, it increased the dynamic range of decoded speeds and decreased the time taken to acquire targets (compared to an optimized standard decoder). These results suggest that it is feasible to simulate iBCI performance accurately enough to be useful for quantitative decoder optimization and design.
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Affiliation(s)
- Francis R Willett
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA. .,Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland, Ohio, USA. .,Department of Neurosurgery, Stanford University, Stanford, California, USA. .,Department of Electrical Engineering, Stanford University, Stanford, California, USA.
| | - Daniel R Young
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.,Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland, Ohio, USA
| | - Brian A Murphy
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.,Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland, Ohio, USA
| | - William D Memberg
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.,Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland, Ohio, USA
| | - Christine H Blabe
- Department of Neurosurgery, Stanford University, Stanford, California, USA
| | - Chethan Pandarinath
- Department of Neurosurgery, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Sergey D Stavisky
- Department of Neurosurgery, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Paymon Rezaii
- Department of Neurosurgery, Stanford University, Stanford, California, USA
| | - Jad Saab
- School of Engineering, Brown University, Providence, RI, USA.,Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs Medical Center, Providence, RI, USA
| | - Benjamin L Walter
- Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland, Ohio, USA.,Department of Neurology, University Hospitals Case Medical Center, Cleveland, Ohio, USA
| | - Jennifer A Sweet
- Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland, Ohio, USA.,Department of Neurosurgery, University Hospitals Case Medical Center, Cleveland, Ohio, USA
| | - Jonathan P Miller
- Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland, Ohio, USA.,Department of Neurosurgery, University Hospitals Case Medical Center, Cleveland, Ohio, USA
| | - Jaimie M Henderson
- Department of Neurosurgery, Stanford University, Stanford, California, USA.,Stanford Neurosciences Institute, Stanford University, Stanford, 94305, California, USA
| | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, California, USA.,Stanford Neurosciences Institute, Stanford University, Stanford, 94305, California, USA.,Department of Bioengineering, Stanford University, Stanford, California, 94305, USA.,Department of Neurobiology, Stanford University, Stanford, California, 94305, USA.,Howard Hughes Medical Institute, Stanford University, Stanford, California, 94305, USA.,Neurosciences Program, Stanford University, Stanford, California, 94305, USA.,Bio-X Program, Stanford University, Stanford, California, 94305, USA
| | - John D Simeral
- School of Engineering, Brown University, Providence, RI, USA.,Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs Medical Center, Providence, RI, USA.,Carney Institute for Brain Science, Brown University, Providence, Rhode Island, USA.,Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Beata Jarosiewicz
- Department of Neurosurgery, Stanford University, Stanford, California, USA
| | - Leigh R Hochberg
- School of Engineering, Brown University, Providence, RI, USA.,Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs Medical Center, Providence, RI, USA.,Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Robert F Kirsch
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.,Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland, Ohio, USA
| | - A Bolu Ajiboye
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.,Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland, Ohio, USA
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10
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Young D, Willett F, Memberg WD, Murphy B, Rezaii P, Walter B, Sweet J, Miller J, Shenoy KV, Hochberg LR, Kirsch RF, Ajiboye AB. Closed-loop cortical control of virtual reach and posture using Cartesian and joint velocity commands. J Neural Eng 2018; 16:026011. [PMID: 30523839 DOI: 10.1088/1741-2552/aaf606] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
OBJECTIVE Brain-computer interfaces (BCIs) are a promising technology for the restoration of function to people with paralysis, especially for controlling coordinated reaching. Typical BCI studies decode Cartesian endpoint velocities as commands, but human arm movements might be better controlled in a joint-based coordinate frame, which may match underlying movement encoding in the motor cortex. A better understanding of BCI controlled reaching by people with paralysis may lead to performance improvements in brain-controlled assistive devices. APPROACH Two intracortical BCI participants in the BrainGate2 pilot clinical trial performed a visual 3D endpoint virtual reality reaching task using two decoders: Cartesian and joint velocity. Task performance metrics (i.e. success rate and path efficiency) and single feature and population tuning were compared across the two decoder conditions. The participants also demonstrated the first BCI control of a fourth dimension of reaching, the arm's swivel angle, in a 4D posture matching task. MAIN RESULTS Both users achieved significantly higher success rates using Cartesian velocity control, and joint controlled trajectories were more variable and significantly more curved. Neural tuning analyses showed that most single feature activity was best described by a Cartesian kinematic encoding model, and population analyses revealed only slight differences in aggregate activity between the decoder conditions. Simulations of a BCI user reproduced trajectory features seen during closed-loop joint control when assuming only Cartesian-tuned features passed through a joint decoder. With minimal training, both participants controlled the virtual arm's swivel angle to complete a 4D posture matching task, and achieved significantly higher success using a Cartesian + swivel velocity decoder compared to a joint velocity decoder. SIGNIFICANCE These results suggest that Cartesian velocity command interfaces may provide better BCI control of arm movements than other kinematic variables, even in 4D posture tasks with swivel angle targets.
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Affiliation(s)
- D Young
- Case Western Reserve University, Cleveland, OH, United States of America. Department of VA Medical Center, FES Center of Excellence, Rehabilitation R&D Service, Louis Stokes Cleveland, Cleveland, OH, United States of America
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Iv M, Samghabadi P, Holdsworth S, Gentles A, Rezaii P, Harsh G, Li G, Thomas R, Moseley M, Daldrup-Link HE, Vogel H, Wintermark M, Cheshier S, Yeom KW. Quantification of Macrophages in High-Grade Gliomas by Using Ferumoxytol-enhanced MRI: A Pilot Study. Radiology 2018; 290:198-206. [PMID: 30398435 DOI: 10.1148/radiol.2018181204] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Purpose To investigate ferumoxytol-enhanced MRI as a noninvasive imaging biomarker of macrophages in adults with high-grade gliomas. Materials and Methods In this prospective study, adults with high-grade gliomas were enrolled between July 2015 and July 2017. Each participant was administered intravenous ferumoxytol (5 mg/kg) and underwent 3.0-T MRI 24 hours later. Two sites in each tumor were selected for intraoperative sampling on the basis of the degree of ferumoxytol-induced signal change. Susceptibility and the relaxation rates R2* (1/T2*) and R2 (1/T2) were obtained by region-of-interest analysis by using the respective postprocessed maps. Each sample was stained with Prussian blue, CD68, CD163, and glial fibrillary acidic protein. Pearson correlation and linear mixed models were performed to assess the relationship between imaging measurements and number of 400× magnification high-power fields with iron-containing macrophages. Results Ten adults (four male participants [mean age, 65 years ± 9 {standard deviation}; age range, 57-74 years] and six female participants [mean age, 53 years ± 12 years; age range, 32-65 years]; mean age of all participants, 58 years ± 12 [age range, 32-74 years]) with high-grade gliomas were included. Significant positive correlations were found between susceptibility, R2*, and R2' and the number of high-power fields with CD163-positive (r range, 0.64-0.71; P < .01) and CD68-positive (r range, 0.55-0.57; P value range, .01-.02) iron-containing macrophages. No significant correlation was found between R2 and CD163-positive (r = 0.33; P = .16) and CD68-positive (r = 0.24; P = .32) iron-containing macrophages. Similar significance results were obtained with linear mixed models. At histopathologic analysis, iron particles were found only in macrophages; none was found in glial fibrillary acidic protein-positive tumor cells. Conclusion MRI measurements of susceptibility, R2*, and R2' (R2* - R2) obtained after ferumoxytol administration correlate with iron-containing macrophage concentration, and this shows their potential as quantitative imaging markers of macrophages in malignant gliomas. © RSNA, 2018 Online supplemental material is available for this article.
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Affiliation(s)
- Michael Iv
- From the Departments of Radiology (M.I., P.R., H.E.D.L., M.W., K.W.Y.) and Pathology (P.S., H.V.), Stanford University Medical Center, 300 Pasteur Dr, Grant Building, Room S031E, Stanford, CA 94305; Richard M. Lucas Center for Imaging (S.H., M.M.) and Departments of Medicine (Biomedical Informatics Research) (A.G.), Neurosurgery (G.H., G.L., S.C.), and Neurology (Neuro-Oncology) (R.T.), Stanford University, Stanford, Calif
| | - Peyman Samghabadi
- From the Departments of Radiology (M.I., P.R., H.E.D.L., M.W., K.W.Y.) and Pathology (P.S., H.V.), Stanford University Medical Center, 300 Pasteur Dr, Grant Building, Room S031E, Stanford, CA 94305; Richard M. Lucas Center for Imaging (S.H., M.M.) and Departments of Medicine (Biomedical Informatics Research) (A.G.), Neurosurgery (G.H., G.L., S.C.), and Neurology (Neuro-Oncology) (R.T.), Stanford University, Stanford, Calif
| | - Samantha Holdsworth
- From the Departments of Radiology (M.I., P.R., H.E.D.L., M.W., K.W.Y.) and Pathology (P.S., H.V.), Stanford University Medical Center, 300 Pasteur Dr, Grant Building, Room S031E, Stanford, CA 94305; Richard M. Lucas Center for Imaging (S.H., M.M.) and Departments of Medicine (Biomedical Informatics Research) (A.G.), Neurosurgery (G.H., G.L., S.C.), and Neurology (Neuro-Oncology) (R.T.), Stanford University, Stanford, Calif
| | - Andrew Gentles
- From the Departments of Radiology (M.I., P.R., H.E.D.L., M.W., K.W.Y.) and Pathology (P.S., H.V.), Stanford University Medical Center, 300 Pasteur Dr, Grant Building, Room S031E, Stanford, CA 94305; Richard M. Lucas Center for Imaging (S.H., M.M.) and Departments of Medicine (Biomedical Informatics Research) (A.G.), Neurosurgery (G.H., G.L., S.C.), and Neurology (Neuro-Oncology) (R.T.), Stanford University, Stanford, Calif
| | - Paymon Rezaii
- From the Departments of Radiology (M.I., P.R., H.E.D.L., M.W., K.W.Y.) and Pathology (P.S., H.V.), Stanford University Medical Center, 300 Pasteur Dr, Grant Building, Room S031E, Stanford, CA 94305; Richard M. Lucas Center for Imaging (S.H., M.M.) and Departments of Medicine (Biomedical Informatics Research) (A.G.), Neurosurgery (G.H., G.L., S.C.), and Neurology (Neuro-Oncology) (R.T.), Stanford University, Stanford, Calif
| | - Griffith Harsh
- From the Departments of Radiology (M.I., P.R., H.E.D.L., M.W., K.W.Y.) and Pathology (P.S., H.V.), Stanford University Medical Center, 300 Pasteur Dr, Grant Building, Room S031E, Stanford, CA 94305; Richard M. Lucas Center for Imaging (S.H., M.M.) and Departments of Medicine (Biomedical Informatics Research) (A.G.), Neurosurgery (G.H., G.L., S.C.), and Neurology (Neuro-Oncology) (R.T.), Stanford University, Stanford, Calif
| | - Gordon Li
- From the Departments of Radiology (M.I., P.R., H.E.D.L., M.W., K.W.Y.) and Pathology (P.S., H.V.), Stanford University Medical Center, 300 Pasteur Dr, Grant Building, Room S031E, Stanford, CA 94305; Richard M. Lucas Center for Imaging (S.H., M.M.) and Departments of Medicine (Biomedical Informatics Research) (A.G.), Neurosurgery (G.H., G.L., S.C.), and Neurology (Neuro-Oncology) (R.T.), Stanford University, Stanford, Calif
| | - Reena Thomas
- From the Departments of Radiology (M.I., P.R., H.E.D.L., M.W., K.W.Y.) and Pathology (P.S., H.V.), Stanford University Medical Center, 300 Pasteur Dr, Grant Building, Room S031E, Stanford, CA 94305; Richard M. Lucas Center for Imaging (S.H., M.M.) and Departments of Medicine (Biomedical Informatics Research) (A.G.), Neurosurgery (G.H., G.L., S.C.), and Neurology (Neuro-Oncology) (R.T.), Stanford University, Stanford, Calif
| | - Michael Moseley
- From the Departments of Radiology (M.I., P.R., H.E.D.L., M.W., K.W.Y.) and Pathology (P.S., H.V.), Stanford University Medical Center, 300 Pasteur Dr, Grant Building, Room S031E, Stanford, CA 94305; Richard M. Lucas Center for Imaging (S.H., M.M.) and Departments of Medicine (Biomedical Informatics Research) (A.G.), Neurosurgery (G.H., G.L., S.C.), and Neurology (Neuro-Oncology) (R.T.), Stanford University, Stanford, Calif
| | - Heike E Daldrup-Link
- From the Departments of Radiology (M.I., P.R., H.E.D.L., M.W., K.W.Y.) and Pathology (P.S., H.V.), Stanford University Medical Center, 300 Pasteur Dr, Grant Building, Room S031E, Stanford, CA 94305; Richard M. Lucas Center for Imaging (S.H., M.M.) and Departments of Medicine (Biomedical Informatics Research) (A.G.), Neurosurgery (G.H., G.L., S.C.), and Neurology (Neuro-Oncology) (R.T.), Stanford University, Stanford, Calif
| | - Hannes Vogel
- From the Departments of Radiology (M.I., P.R., H.E.D.L., M.W., K.W.Y.) and Pathology (P.S., H.V.), Stanford University Medical Center, 300 Pasteur Dr, Grant Building, Room S031E, Stanford, CA 94305; Richard M. Lucas Center for Imaging (S.H., M.M.) and Departments of Medicine (Biomedical Informatics Research) (A.G.), Neurosurgery (G.H., G.L., S.C.), and Neurology (Neuro-Oncology) (R.T.), Stanford University, Stanford, Calif
| | - Max Wintermark
- From the Departments of Radiology (M.I., P.R., H.E.D.L., M.W., K.W.Y.) and Pathology (P.S., H.V.), Stanford University Medical Center, 300 Pasteur Dr, Grant Building, Room S031E, Stanford, CA 94305; Richard M. Lucas Center for Imaging (S.H., M.M.) and Departments of Medicine (Biomedical Informatics Research) (A.G.), Neurosurgery (G.H., G.L., S.C.), and Neurology (Neuro-Oncology) (R.T.), Stanford University, Stanford, Calif
| | - Samuel Cheshier
- From the Departments of Radiology (M.I., P.R., H.E.D.L., M.W., K.W.Y.) and Pathology (P.S., H.V.), Stanford University Medical Center, 300 Pasteur Dr, Grant Building, Room S031E, Stanford, CA 94305; Richard M. Lucas Center for Imaging (S.H., M.M.) and Departments of Medicine (Biomedical Informatics Research) (A.G.), Neurosurgery (G.H., G.L., S.C.), and Neurology (Neuro-Oncology) (R.T.), Stanford University, Stanford, Calif
| | - Kristen W Yeom
- From the Departments of Radiology (M.I., P.R., H.E.D.L., M.W., K.W.Y.) and Pathology (P.S., H.V.), Stanford University Medical Center, 300 Pasteur Dr, Grant Building, Room S031E, Stanford, CA 94305; Richard M. Lucas Center for Imaging (S.H., M.M.) and Departments of Medicine (Biomedical Informatics Research) (A.G.), Neurosurgery (G.H., G.L., S.C.), and Neurology (Neuro-Oncology) (R.T.), Stanford University, Stanford, Calif
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Abstract
Surgical treatment may be indicated for select patients with cervical disc disease, whether it is cervical disc herniation or spondylosis due to degenerative changes, acute cervical injury due to trauma, or other underlying cervical pathology. Currently, there are various surgical techniques, including anterior, posterior, or combined approaches, in addition to new interventions being utilized in practice. Ideally, the surgical approach should be selected in consideration of each patient’s clinical presentation, imaging findings, and overall medical comorbidities on an individual basis. But the unique advantages and disadvantages of each surgical technique often complicate the therapy choice in managing cervical disc diseases. Although anterior cervical discectomy and fusion (ACDF) is the most widely accepted procedure performed for both single and multi-level cervical disc diseases, there are multiple modifications to this technique. Surgeons have access to different types of plates, screws, and cages and can adopt newer advances in the field such as stand-alone and minimally invasive techniques when indicated. In short, no consensus exists in terms of a single approach that is preferred for all patients. This article aims to review the standard of care for management of cervical disc disease with a focus on the surgical techniques and, in particular, the anterior approach, exploring the various surgical options within this technique.
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Affiliation(s)
- Lily H Kim
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | - Marissa D'Souza
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | - Allen L Ho
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | | | - Eric S Sussman
- Neurosurgery, Stanford University School of Medicine, West Orange, USA
| | - Paymon Rezaii
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
| | - Atman Desai
- Neurosurgery, Stanford University School of Medicine, Stanford, USA
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Yoon BC, Saad AF, Rezaii P, Wintermark M, Zaharchuk G, Iv M. Evaluation of Thick-Slab Overlapping MIP Images of Contrast-Enhanced 3D T1-Weighted CUBE for Detection of Intracranial Metastases: A Pilot Study for Comparison of Lesion Detection, Interpretation Time, and Sensitivity with Nonoverlapping CUBE MIP, CUBE, and Inversion-Recovery-Prepared Fast-Spoiled Gradient Recalled Brain Volume. AJNR Am J Neuroradiol 2018; 39:1635-1642. [PMID: 30093483 DOI: 10.3174/ajnr.a5747] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2017] [Accepted: 06/16/2018] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Early and accurate identification of cerebral metastases is important for prognostication and treatment planning although this process is often time consuming and labor intensive, especially with the hundreds of images associated with 3D volumetric imaging. This study aimed to evaluate the benefits of thick-slab overlapping MIPs constructed from contrast-enhanced T1-weighted CUBE (overlapping CUBE MIP) for the detection of brain metastases in comparison with traditional CUBE and inversion-recovery prepared fast-spoiled gradient recalled brain volume (IR-FSPGR-BRAVO) and nonoverlapping CUBE MIP. MATERIALS AND METHODS A retrospective review of 48 patients with cerebral metastases was performed at our institution from June 2016 to October 2017. Brain MRIs, which were acquired on multiple 3T scanners, included gadolinium-enhanced T1-weighted IR-FSPGR-BRAVO and CUBE, with subsequent generation of nonoverlapping CUBE MIP and overlapping CUBE MIP. Two blinded radiologists identified the total number and location of metastases on each image type. The Cohen κ was used to determine interrater agreement. Sensitivity, interpretation time, and lesion contrast-to-noise ratio were assessed. RESULTS Interrater agreement for identification of metastases was fair-to-moderate for all image types (κ = 0.222-0.598). The total number of metastases identified was not significantly different across the image types. Interpretation time for CUBE MIPs was significantly shorter than for CUBE and IR-FSPGR-BRAVO, saving at least 50 seconds per case on average (P < .001). The mean lesion contrast-to-noise ratio for both CUBE MIPs was higher than for IR-FSPGR-BRAVO. The mean contrast-to-noise ratio for small lesions (<4 mm) was lower for nonoverlapping CUBE MIP (1.55) than for overlapping CUBE MIP (2.35). For both readers, the sensitivity for lesion detection was high for all image types but highest for overlapping CUBE MIP and CUBE (0.93-0.97). CONCLUSIONS This study suggests that the use of overlapping CUBE MIP or nonoverlapping CUBE MIP for the detection of brain metastases can reduce interpretation time without sacrificing sensitivity, though the contrast-to-noise ratio of lesions is highest for overlapping CUBE MIP.
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Affiliation(s)
- B C Yoon
- From the Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University, Stanford, California
| | - A F Saad
- From the Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University, Stanford, California
| | - P Rezaii
- From the Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University, Stanford, California
| | - M Wintermark
- From the Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University, Stanford, California
| | - G Zaharchuk
- From the Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University, Stanford, California
| | - M Iv
- From the Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University, Stanford, California.
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