Journal of Biomedical Graphics and Computing 

About: Journal of Biomedical Graphics and Computing is an academic journal. The journal publishes majorly in the area(s): Magnetic resonance imaging & Population. It has an ISSN identifier of 1925-4008. It is also open access. Over the lifetime, 139 publications have been published receiving 499 citations.

Brain MR image normalization in texture analysis of multiple sclerosis

Abstract: A problem that occurs in quantitative texture analysis of magnetic resonance imaging (MRI) is that there are intra-scan and inter-scan image intensity variations due to the MRI instrumentation. Therefore, image intensity normalization methods should be applied prior to further image analysis. The objective of this work was to investigate six previously described MRI intensity normalization methods and propose the most appropriate for the pre-processing of brain T2-weighted MR images acquired from 38 symptomatic untreated multiple sclerosis (MS) subjects. The following normalization methods were investigated: Contrast Stretch Normalization (CSN), Intensity Scaling (IS), Histogram Stretching (HS), Histogram Normalization (HN), Gaussian Kernel Normalization (GKN), and Histogram Equalization (HE). The main findings of this study can be summarized as follows: 1) Lesion texture features were affected differently by the normalization process for both the 0 and 6-12 months MRI scans. For example, for the features median and contrast there was significant difference between 0 and 6-12 months for the original MRI images but not for the HN normalized ones. On the other hand for the feature complexity there was no significant difference between 0 and 6-12 months for the original MRI images, but there was for the HN normalized images. 2) The statistical lesion feature analysis between the original and the normalized images showed that the HN method gave the highest number of significant features after normalization for both the 0 and 6-12 months MRI scans. 3) The HN normalization method gave the best performance compared to the other normalization methods with respect to the distance measures, structural similarity index, coefficient of variation, and correlation coefficient between the original and the normalized 0 and 6-12 months MRI scans. Thus, based on the above findings it is recommended that the simple HN normalization method could be used prior to quantitative texture analysis in the case study presented. The findings of this study provide evidence that texture features of MRI-detectable brain white matter lesions may have an additional potential role in the clinical evaluation of MRI images in MS. This is a prerequisite step in the assessment of texture features as surrogate markers of disease progression. However, a larger scale study is needed to establish the application in clinical practice and for computing shape parameters and texture features that may provide information for better and earlier differentiation between normal tissue and MS lesions.

BreastSimulator: A software platform for breast x-ray imaging research

Abstract: Objective: It is well established that computer based models of x-ray imaging systems are basic and very important tools for developing and evaluating new emerging x-ray imaging techniques, optimizing technical parameters, and performing feasibility studies prior to implementation in clinical practice. Such models are essential for the development and the establishment of new breast x-ray imaging modalities that aim to detect and better characterize breast lesions in their early stage. This work presents a complete software package, called BreastSimulator, dedicated for breast x-ray imaging research. Methods: The package consists of four modules used to create three-dimensional breast models in compressed and uncompressed state, simulate x-ray mammographic images and visualize the results of the simulations. The module that is used to generate breast models, Breast Modeling Module, consists of several sub-modules that are utilized to model the different breast components: external shape, glandular and adipose tissue, breast lesion, skin, pectoralis and lymphatics. The Compression Module is dedicated to simulate the mechanical compression of the breasts. Mammographic projection images are obtained with simulation of x-ray photon transport starting from the x-ray source, passing through the breast model and reaching the detector. This is accomplished in the Image Generation Module. Finally, the results of the simulations, i.e. breast models and mammographic images can be seen with the Visualization Module. Results: Here, we demonstrate the application of the software package in conventional and dual-energy mammography as well as compression studies, as examples to highlight basic functions and applications of Breast Simulator. The first study aimed to define the optimal pair of ‘low’ and ‘high’ monochromatic x-ray energies for dual-energy mammography. It involved the synthesis of 225 dual-energy images obtained from combinations of ‘low’ and ‘high’ energy images acquired in the energy range 14 to 28 keV. Images were generated from a medium sized dense breast model that contained one calcification. The study showed that 17/28 keV incident monoenergetic beams are optimal to obtain maximal calcification detectability for this breast. The second study demonstrated the effect of breast compression on the quality of the obtained mammograms. It included a breast model based on breast CT slices subjected to simulated compression and generation of mammographic images. Increased image quality is observed for mammograms obtained from breasts with reduced thickness. The characteristics of the x-ray beams that exit a small dense breast model were investigated in the third study. For two mammographic spectra used in mammography imaging, the mean energy of the transmitted x-rays and the mean exit angle of the scattered radiation increase as the incident x-ray energy increases. Conclusions: We believe that this tool and its functionalities will speed up the development, testing and optimization of new breast imaging modalities such as breast tomosynthesis, cone-beam CT and advanced two-dimensional techniques like dual-energy as well as specific parts of imaging chain, such as x-ray source, detector and acquisition geometry.

Integrating Google Glass into simulation-based training: experiences and future directions

Abstract: Background: Education experts are starting to explore the potential uses of wearable technology and augmented reality in simulation-based training. In this article, we summarize our experiences with using Google Glass in simulation-based training and discuss potential future directions with this advanced technology. Methods: Emergency medicine residents and medical students participated in a pilot study where each team captain was asked to wear Google Glass during 15 separate simulation-based training sessions. Video obtained from Google Glass was analyzed and utilized during debriefing sessions for the residents and medical students. Results: We were able to successfully integrate Google Glass into simulation-based training and debriefing. During the analysis of each recording, observations were noted about the events that transpired and this data was used to provide instructional feedback to the residents and medical students for self-reflection and appraisal. Post-exercise surveys were conducted after each simulation session and all participants noted that Google Glass did not interfere with their simulation experience. Google Glass enabled the observers to analyze the team captain’s primary visual focus during the entire simulation scenario and feedback was provided based on the data recorded. Conclusions: Wearable technologies such as Google Glass can be successfully integrated into simulation-based training exercises without disrupting the learners’ experience. Data obtained from this integration can be utilized to improve debriefing sessions and self-reflection. Future research is underway and required to evaluate other potential uses for wearable technology in simulation-based training.

Dynamic contrast-enhanced MRI in breast cancer: A comparison between distributed and compartmental tracer kinetic models

Abstract: Background/objectives: Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is widely used in tumor diagnosis, staging and assessment of therapy response for different types of tumors, thanks to its capability to provide important functional information about tissue microvasculature. Tracer kinetic models used for estimating microcirculatory parameters can be broadly categorized as conventional compartmental (CC) or distributed- parameter (DP) models. While DP models seem to be more realistic, CC models (in particular the Tofts and the Brix models) have been widely used in clinical investigations over the past two decades. However, to date there is no direct comparison of CC vs DP models on real breast DCE-MRI data; moreover, a direct comparison between Tofts and Brix models, has not yet been reported on real breast data. Therefore, the purpose of this study was two-fold: on the one hand we analyzed the performance, on real breast DCE-MRI data, of CC vs DP models in terms of goodness-of-fit metrics; on the other hand we compared Tofts and Brix models on the basis of real breast DCE-MRI data. Methods: Three models were compared: two CC models (the Tofts and the Brix models) and one DP model (the ATH model). We gathered data in two different scenarios: DCE-MRI with high temporal resolution obtained by means of a k -space under-sampling and data sharing method known as Time-resolved angiography With Stochastic Trajectories (TWIST) and DCE-MRI with low temporal resolution obtained by means of the Spoiled Gradient-Echo k -space scheme known as Fast Low Angle Shot (FLASH). The performances of the three models were evaluated by means of three goodness-of-fit metrics: the Residual Sum of Squares, the Bayesian Information Criterion and the Akaike Information Criterion on four breast DCE-MRI examinations. Results: Although not conclusive, the results of this study suggest that the ATH model can achieve better fit in comparison to the Tofts and Brix models for TWIST data; and that the Brix model can achieve better fit with respect to the Tofts model for FLASH data. Conclusion: Given the current typical settings of clinical breast DCE-MRI examinations, there seems not to be a clear advantage, in terms of goodness-of-fit, of ATH with respect to Tofts and Brix models; moreover, at lower temporal resolution the Brix model can achieve better fit than the Tofts model.

Functional MRI demonstrates pain perception in hand osteoarthritis has features of central pain processing

Abstract: Background: Hand osteoarthritis (HOA) is typified by pain and reduced function. We hypothesised that people with HOA have enhanced sensitivity and activation of peripheral nociceptors in the hand, thereby potentiating chronic pain. In our study we aimed to assess if central sensitisation mediates pain perception in osteoarthritis of the hand. Methods: Participants with proximal and distal interphalangeal joint (PIP/DIP) HOA and non-OA controls were recruited. Clinical pain scores using the visual analogue scale (VAS) were recorded before and after performing a painful hand task. Central pain processing was evaluated with functional brain neuroimaging (fMRI) using a finger flexion-extension (FFE) task performed over 3 minutes. Data was analysed with FMRIB software (www.fmrib.ox.ac.uk/fsl). Group mean activation of functional MRI signal between hand osteoarthritis and control non-arthritic participants was compared. Results: Our group of hand OA participants reported high pain levels compared with non-arthritic controls as demonstrated by the mean VAS in hand OA participants of 59.31± 8.19 mm compared to 4.00 ± 1.89 mm in controls ( p < 0.0001), despite all participants reporting analgesic use. Functional MRI analysis showed increased activation in the thalamus, cingulate, frontal and somatosensory cortex in the hand OA group but not in controls (thresholded at p < 0.05). Regions of activation were mapped to Brodmann areas 3, 4, 6, 9, 13, 22, 24 and 44. Activated regions found in our study are recognised higher brain pain processing centres implicated in central sensitisation. Conclusions: People with hand osteoarthritis demonstrated features of central sensitisation that was evident after a finger flexion-extension task using functional MRI. Functional MRI is a useful biomarker in detecting pain in hand osteoarthritis and could be used in future hand osteoarthritis pain studies to evaluate pain modulation strategies.