Medical Image Processing, Analysis, And Visualization Research Articles

Development of Three-dimensional Rotator Cuff Tendon Magnetic Resonance Imaging System

Objectives:Three-dimensional magnetic resonance imaging (3D-MRI) system has already performed at cardiovascular surgery area, and it’s useful for improving the reliability of surgical planning and informed consent for patients. However, it is difficult to construct 3D-MRI of rotator cuff tendon because of existing similar intensity area for other tendon and muscular tissue. The purpose of this study is to construct 3D-MRI of rotator cuff tendon from 2D-MRI and compare the accuracy of rotator cuff tear characters between 2D-MRI and 3D-MRI. We hypothesized that 3D-MRI might be more accurate than 2D-MRI for classifying tear shape and size.Methods:An MRI scan was carried out on 28 rotator cuff tear patients 2 days before the surgery. Humerus, rotator cuff tear, and rotator cuff tendon were manually traced. 3D-MRI data was acquired using a medical image processing, analysis, and visualization (MIPAV). Four orthopedic surgeons, 2 residents (resident G) and 2 shoulder surgeons, more than 100 case shoulder surgery experiences (shoulder surgeon G) reviewed the MRI imaging for each patient independently. The five rotator cuff tear shapes used in this study: crescent, reverse L, L shaped, trapezoidal, and massive tear (Ellman and Gartsman Classification). The four rotator cuff tear sizes used in this study: small < 1cm, medium < 3cm, large < 5cm, and massive > 5cm (Cofield Classification). All images from the 2D and 3D-MRI were separated. In the first, the readers characterized the shape and size of the rotator cuff tendon tears by reviewing the 2D-MRI and blind to the intraoperative findings. Secondly, the 3D-MRI of each rotator cuff tear was reviewed, and shape and size of the tear were documented by each surgeon independently and blind to the intraoperative findings. These results were compared with the 2D-MRI imaging evaluation and intraoperative findings. (Figure 1)Results:The accuracy for tear shape using measurements on 2D-MRI was 64.3% for resident group and 78.6% for shoulder surgeon group. The accuracy for tear shape characterization using 3D-MRI was 85.7% for resident group and 92.9% for shoulder surgeon group. Moreover, the accuracy for tear size using measurements on 2D-MRI was 71.4% for resident group and 92.9% for shoulder surgeon group. The accuracy for tear size using 3D-MRI was 92.8% for resident group and 100% for shoulder surgeon group. The overall tear shape accuracy of 3D-MRI was 89.3%, significantly different from 2D-MRI accuracy (71.4%) (P=0.02). Furthermore, the overall tear size accuracy of 3D MRI was 96.4%, not significantly different from 2-D MRI accuracy (82.1%) (P=0.058). (Figure 2)Conclusion:In this study, 3D-MRI and 2D-MRI were significantly different in accuracy for diagnosing the shape of rotator cuff tears. Almost readers could easily review the shape and size of rotator cuff tear by 3D-MRI. This study has demonstrated that 3D-MRI of the rotator cuff tear improved the accuracy of characterizing the shape of a rotator cuff tear compare with the current 2D MRI-based techniques. The findings in this study suggest that 3D-MRI of the rotator cuff can work an important instrument for better categorizing and understanding rotator cuff tears, and those providing information that can be beneficial for pre-surgical planning. Further study and these data programing could automatically develop three-dimensional rotator cuff tendon magnetic resonance imaging system.

Dilated perivascular spaces and fatigue: is there a link? Magnetic resonance retrospective 3Tesla study.

Fatigue (F) is a common, inexplicable, and disabling symptom in multiple sclerosis (MS) patients. The purpose of this study was to evaluate a possible correlation between fatigue and morpho-volumetric features and site of dilated perivascular spaces (dPS), visible on 3T magnetic resonance (MR) in fatigued multiple sclerosis patients (FMS). We studied 82 relapsing remitting (RR) FMS patients and 43 HC, matched for age, sex, and education. F was assessed by the Fatigue Severity Scale (FSS). To evaluate a possible correlation between degree of F and characteristics of dPS, patients were divided in two groups: more (mFMS) (FSS ≥ 5; n = 30) and less fatigued (lFMS) (FSS ≥ 4; n = 52), compared to a matched healthy control (HC) subject group. The MR study was performed with 3T scanner by SpinEcho T1, Fast-SpinEcho DP-T2, FLAIR, and 3D FSPGR T1 sequences. dPS volumes were measured with Medical Image Processing Analysis and Visualization (MIPAV); Global Cerebral Atrophy (GCA), expressed as Brain Parenchymal Fraction (BPF), was assessed by FSL SIENAX. The t test showed significantly increased dPS number (p = 0.021) in FMS patients (mFMS p = 0.0024 and lFMS p = 0.033) compared to HC. Pearson correlation revealed a significant correlation between dPS number and FSS (r = 0.208 p = 0.051). Furthermore, the chi-squared test confirms the intragroup (HC, mFMS, lFMS) differences about dPS location (p = 0.01) and size (p = 0.0001). Our study confirms that PS in MS patients presents with different volumetric and site characteristics as compared to HC; moreover, F severity significantly correlates with dPS number, site, and size.

The effects of sequential treatments on hippocampal volumes in malignant glioma patients.

Malignant gliomas (MG) are very aggressive tumors. In an effort to improve the outcome, the patients receive multi-modal therapies such as surgery, radiation and chemotherapy (temozolomide followed in many cases by bevacizumab). The survivors are affected by multiple learning and memory deficits. Greater deterioration over time in hippocampal specific cognitive tasks was shown in patients receiving bevacizumab in addition to radiation and temozolomide for a longer period of time (RTOG 0825). The rate of hippocampal atrophy in patients treated with radiation and temozolomide followed by bevacizumab is not yet determined, and is the goal of the present study. We used the serial MRIs obtained as parts of standard clinical care in patients with MG. Measurements were done using the Medical Image Processing, Analysis and Visualization (MIPAV) software. The hippocampus in the contralateral hemisphere was manually traced and measured, to avoid morphological structure changes induced by the tumor, radiation fields or surgical markers. We determined a longitudinal progression of hippocampal atrophy-with the maximum volume loss (33.26 %) for the patients that were on treatment for 5years. There was no detectable hippocampal atrophy during the chemo-radiation followed by adjuvant temozolomide. A significant decrease in the absolute hippocampus volume was noted after 6months of continuous bevacizumab treatment (p < 0.05). The hippocampal volume loss progressed over the next 3years, and was higher than the one previously reported in Alzheimer disease patients. The hippocampal volume loss is minimal during the 1month after diagnosis, when the patients receive chemo-radiation and adjuvant temozolomide. However, prolonged treatment including bevacizumab is associated with a significant rate of hippocampal volume loss.

Integration of the Java Image Science Toolkit with E-Science Platform

Medical image analyses rely on diverse software packages assembled into a “pipeline”. The Java Image Science Toolkit (JIST) has served as a standalone plugin into the Medical Image Processing Analysis and Visualization (MIPAV). We addressed shortcomings that previously prevented deeper integration of JIST with other E-science platforms. First, we developed an interface for integrating externally compiled packages (similar to the interfaces in NiPy) such that the application can become a “draggable module” in the module tree. This allows for connection of inputs and outputs to other JIST modules while maintaining external processing and monitoring. Second, we develop an integration interface with the Neuroimaging Informatics Tools and Resources Clearinghouse Cloud Environment (NITRC-CE). User can launch and terminate pre-configured nodes to utilize computational resources of the Amazon cloud. Finally, we define a new external data source, which can connect to the eXtensible Neuroimaging Archive Toolkit (XNAT) to query and retrieve remote data using XNAT’s REST API. Specifically, we define dataflow for files that can readily be converted into volumes and collections of volumes to interface with any JIST module that expects volumetric image data as an input. Users now have the ability to run their pipelines from a well-defined external data source and no longer are required to already have data on the disk. With these upgrades we have extended JIST’s capabilities outside of complied java source code and enhanced capabilities to seamlessly interface with E-science platforms.

New Software for Multimodal Image Registration with Application in Cell Tracking Studies

Positron Emission Tomography (PET) is an essential tool for in vivo molecular imaging, due to its high sensitivity. Combined with the unparalleled soft tissue contrast of magnetic resonance imaging (MRI), PET-MRI allows for accurate quantification required for cell tracking studies. The purpose of this study is to develop a landmark based image registration program capable of fiducial registration, for application in a stem cell tracking study. PET and MR Images acquired with fiducial markers located in the field of view around the specimen were registered using our landmark based program, written in Matlab. The fiducial markers were also used to test the accuracy of our Matlab program, along with other automated image registration software including: automatic image registration (AIR), FMRIB's Linear Image Registration Tool (FLIRT), and Medical Image Processing, Analysis, and Visualization (MIPAV), all available online free for download. All of the programs were able to successfully register the images; however, our Matlab program provided the best registration accuracy, with a Fiducial Registration Error (FRE) of 1.36 mm. Our Matlab developed program is available free for download at www.sourceforge.net/p/matlabaffinereg.

VisN: Neuroimaging Toolkit for Medical Image Visualization

Event Abstract Back to Event VisN: Neuroimaging Toolkit for Medical Image Visualization Justin S. Senseney1, 2*, Terry Oakes1 and Gerard Riedy1 1 National Intrepid Center of Excellence, Research Department, United States 2 National Institutes of Health, Center for Information Technology, United States The National Intrepid Center of Excellence (NICoE) performs patient care and research of traumatic brain injury (TBI) and related psychological health conditions for United States armed forces. This research includes multi-modal neuroimaging acquisitions that are not available in current clinical medicine. VisN has been developed to apply existing open-source image processing libraries to demonstrate the potential of next-generation radiology to aid in the understanding of mental health. VisN extends the open-source Medical Image Processing, Analysis, and Visualization (MIPAV) program and uses the ImageJ program (Schneider, 2012) developed at the National Institutes of Health to provide a PACS-like system to radiologists that provides memory management and distributed database access functionality not yet found in clinically oriented software. The result is a familiar interface for the radiologist, as in Figure 1, while still allowing the application of emerging image processing tools. VisN can apply transformation matrices and non-linear volumetric transformation maps ad-hoc to the medical image fusion process. Once overlaid, VisN can define statistical parameters of interest for the visualization of functional activations. Tractography information is available using Camino (Cook, 2006). Finally, regions of interest can be selected and visualized on a volumetric basis, as shown in Figure 2. This provides a focus on real-time image processing tools that are manipulated through an integrated interface shown in Figure 3. VisN provides an open-source framework for emerging neuroimaging needs in radiology software. NICoE’s imaging protocol combines clinically useful structural images with images that do not have an immediate clinical application but may provide useful research information. Research images include functional, diffusion, spectroscopy, susceptibility, and perfusion MRI studies. Fluorodeoxyglucose (FDG) positron emission tomography (PET) images, broad electroencephalography data, and spatially localized magnetoencephalography (MEG) are also available. These multi-modal, spatially variable image sets make it difficult for commercially available image processing systems to provide the flexibility needed to conduct this research. VisN advances the state of accessible image processing to provide a viable platform for advanced neuroimaging-related software which can easily be translated into a radiology environment. Figure 1 Figure 2 Figure 3 References Cook, P A, Y Bai, K K Seunarine, M G Hall, G J Parker, and D C Alexander. 2006. “Camino : Open-Source Diffusion-MRI Reconstruction and Processing.” In 14th Scientific Meeting of the International Society for Magnetic Resonance in Medicine, 14:2759. Seattle, WA. Schneider, Caroline a, Wayne S Rasband, and Kevin W Eliceiri. 2012. “NIH Image to ImageJ: 25 Years of Image Analysis.” Nature Methods 9 (7) (June 28): 671–675. doi:10.1038/nmeth.2089. http://www.nature.com/doifinder/10.1038/nmeth.2089. Keywords: Traumatic brain injury (TBI), multi-modal neuroimaging, functional magnetic resonance imaging (fMRI), diffusion weighted imaging (DWI), Military Medicine Conference: Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013. Presentation Type: Poster Topic: Neuroimaging Citation: Senseney JS, Oakes T and Riedy G (2013). VisN: Neuroimaging Toolkit for Medical Image Visualization. Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. doi: 10.3389/conf.fninf.2013.09.00090 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 08 Apr 2013; Published Online: 11 Jul 2013. * Correspondence: Mr. Justin S Senseney, National Intrepid Center of Excellence, Research Department, Bethesda, MD, 20899, United States, justin.senseney@nih.gov Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Justin S Senseney Terry Oakes Gerard Riedy Google Justin S Senseney Terry Oakes Gerard Riedy Google Scholar Justin S Senseney Terry Oakes Gerard Riedy PubMed Justin S Senseney Terry Oakes Gerard Riedy Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Corpus callosum morphology in children who stutter

Multiple studies have reported both functional and neuroanatomical differences between adults who stutter and their normally fluent peers. However, the reasons for these differences remain unclear although some developmental data suggest that structural brain differences may be present in school-age children who stutter. In the present study, the corpus callosum of children with persistent stuttering, children who recovered from stuttering and typically developing children between 9 and 12 years of age was compared to test if the presence of aberrant callosal morphology is implicated in this disorder. The total corpus callosum midsagittal area and area of each subsection consisting of the rostrum, anterior midbody, posterior midbody and splenium were measured using MIPAV (Medical Image Processing, Analysis, and Visualization). Voxel-based morphometry (VBM) was also used to compare white matter volume. No differences were detected in the corpus callosum area or white matter volume between children with persistent stuttering, children who recovered from stuttering and typically developing children. These results agree with dichotic listening studies that indicate children who stutter show the typical right ear advantage. Therefore, the neural reorganization across the midline shown in adults who stutter may be the result of long-term adaptations to persistent stuttering.Learning outcomes: Educational objectives: After reading this article, the reader will be able to: (1) summarize research findings on corpus callosum development; and (2) discuss the characteristics of corpus callosum anatomy in stuttering.

Abstract 3629: Validation of the Prognostic Value of Quantitative Brain Diffusion-Weighted Imaging after Cardiac Arrest in a Multi-Center Study. Preliminary results

Background: Brain diffusion-weighted imaging MRI (DWI) holds promise as a prognostic tool in comatose post-cardiac arrest patients. In a preliminary study we found that more than 10% of whole brain volume with an ADC value of &lt;650x10 -6 mm 2 /sec between 49-108 hours after the arrest identified poor outcome patients (death or vegetative state at 30 days) with 100% specificity and 81% sensitivity. We aimed to validate this threshold in an external dataset. Methods: This is an ongoing multicenter observational study of DWI MRIs of comatose post-cardiac arrest patients obtained between 1 and 5 days (i.e. 25-120 hours) after the arrest. Patient data are systematically recorded on a web-based data entry form and include baseline characteristics, serial neurological examinations, neurophysiological testing, and date and cause of death if applicable. Poor outcome is defined as in hospital death. Assuming a 40% survival rate, and 100% specificity of DWI for poor outcome, 250 patients are needed to achieve a 0% false positive rate for poor outcome with a 95% confidence interval of 0-5%. MRI scans were processed using MIPAV (Medical Image Processing, Analysis and Visualization program). The brain was semi-automatically outlined on the b0 images using a levelset algorithm that analyzes the intensity and then produces the contours of the boundary. If necessary, contours were adjusted manually. The created brain masks were then applied to the corresponding ADC maps, and the ADC values of voxels within the brain were saved for further analysis. We used the ADC maps that we received from the participating centers. If only the b0 and b1000 images were available, we recreated the brain ADC maps using MIPAV. Results: Data from 48 patients from three centers (Northwestern, Mayo Clinic Jacksonville, Stanford) were included in this preliminary analysis: mean age was 57±16 years, 33% female, arrest time 23±23 minutes, 88% of patients were treated with hypothermia. The MRIs were obtained at 73±22 hours after the arrest. Twenty patients (42%) survived to hospital discharge. The median (IQR) percentage of brain volume with ADC&lt;650x10 -6 mm 2 /sec was 5% (3-6) in survivors and 30% (9-46) in non-survivors (P&lt;0.001). Brain volume with an ADC&lt;650x10 -6 mm 2 /sec &gt; 10% was 100% (95%CI 80-100) specific and 68% (95%CI 48-83) sensitive for in hospital death with a positive predictive value of 100% (79-100), and a negative predictive value of 69% (49-84). The risk ratio (RR) of death if &gt;10% of brain volume had an ADC&lt;650x10 -6 mm 2 /sec was 3.2 (95%CI 1.9-5.5). Conclusion: The preliminary results of this multicenter study demonstrate that early quantitative DWI MRI in comatose post-cardiac arrest patients holds great promise as a prognostic adjunct in these patients in the era of hypothermia. Centers who frequently obtain brain DWI MRI in comatose post-cardiac arrest patients are encouraged to participate.

Abstract 3326: Reliability Of Hematoma Volume Calculation At Local Sites In The Setting Of A Multicenter Intracerebral Hemorrhage Clinical Trial

Background: In the clinical trials evaluating new therapies for acute management of intracerebral hemorrhage (ICH), rapid hematoma volume (HV) measurements from computed tomographic (CT) scan using previously described ‘ABC/2’ method at study sites is frequently required for either determining eligibility or stratification. However, the accuracy of this method by multiple observers using variable visual display modes is not known. Method: We performed a post hoc analysis of a multicenter clinical trial that recruited ICH patients within 6 hours of symptom onset at ten sites in the United States. HV was measured by local site investigators to determine eligibility for enrollment (&lt;60 cc) into the trial using the ABC/2 method (manual method) on the initial CT scan. CT scan images were subsequently sent to the core imaging laboratory for blinded computer based analysis of HV using ‘Medical Image Processing, Analysis, and Visualization (MIPAV)’ software (computer method). Paired t-test was used to identify any differences between the manual and the computer methods of HV measurements. Spearman’s correlation was used to study the agreement between the two methods. Results: HV measurements by both the manual and the computer methods were available in 51 patients enrolled in the trial (mean±standard deviation [SD] age 62±15 years; 26 were women.) The mean±SD HV using as calculated by he computer method (14.6±15.3 mL) was not statistically different from the values calculated by the manual method (16.5±15 mL; p=0.6). Using Spearman’s correlation, there was a significant correlation between the two methods (rho 75%, p&lt;0.01). Manual calculation under estimated the HV by an average of 1.5 mL in the lower 50 percentile of computer-calculated HV (&lt;9.4 ml), while overestimated the HV in the upper 50 percentile by an average of 3.1 mL. None of these differences were statistically significant. Conclusion: Rapid measurements at study sites for either determining eligibility or stratification provides reliable HV measurements despite measurements by multiple observers using variable visual CT scan display modes.

Radio Frequency Ablation Registration, Segmentation, and Fusion Tool

The radio frequency ablation segmentation tool (RFAST) is a software application developed using the National Institutes of Health's medical image processing analysis and visualization (MIPAV) API for the specific purpose of assisting physicians in the planning of radio frequency ablation (RFA) procedures. The RFAST application sequentially leads the physician through the steps necessary to register, fuse, segment, visualize, and plan the RFA treatment. Three-dimensional volume visualization of the CT dataset with segmented three dimensional (3-D) surface models enables the physician to interactively position the ablation probe to simulate burns and to semimanually simulate sphere packing in an attempt to optimize probe placement. This paper describes software systems contained in RFAST to address the needs of clinicians in planning, evaluating, and simulating RFA treatments of malignant hepatic tissue.

Brain volume measurements in patients with human T-cell lymphotropic virus-1–associated tropical spastic paraparesis

Human T-cell lymphotropic virus (HTLV)-1 is associated with a chronic progressive neurologic disease known as HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) that affects 0.2% to 3% of HTLV-1-infected people. The authors aimed at exploring, in vivo, whether brain volume reduction occurs in patients with HAM/TSP through the use of magnetic resonance imaging (MRI). T1 pre/postcontrast spin echo-weighted images (WIs) and T2WIs of the brain were obtained in 19 HAM/TSP patients and 14 age-and sex-matched healthy volunteers. Both patients and healthy individuals were imaged at a 1.5-Tesla magnet by employing a conventional head coil. Focal T1 and T2 abnormalities were calculated and two measurements of brain parenchyma fraction (BPF) were obtained by using SIENAx (Structural Image Evaluation,using Normalisation, of Atrophy; University of Oxford, Oxford, UK) and MIPAV (Medical Image Processing, Analysis, and Visualization; National Institutes of Health, Bethesda, USA) from T1WIs. No significant differences in BPF were found between patients and healthy subjects when using either SIENAx or MIPAV. Analysis of individual patients detected that BPF was lower by 1 standard deviation (SD) relative to patients' average BPF in one patient. The authors conclude that reductions in BPF do not occur frequently in patients with HAM/TSP. However, the authors believe that one individual case of significant brain atrophy raises the question as to whether atrophy selectively targets the spinal cord of HAM/TSP patients or may involve the brain as well. A larger patient population analyzing regional brain volume changes could be helpful in determining whether brain atrophy is a marker of disease in patients with HAM/TSP.