KEYWORDS: Head, Magnetic resonance imaging, Picture Archiving and Communication System, Error analysis, Skull, Radiology, Brain, Gold, Image resolution, Medicine
In addition to length and weight, maximum head circumference is a standard measurement obtained when evaluating pediatric patients, especially in the first two years of life. This study describes the technique to accurately and anatomically determine the head circumference from MRI studies within the picture archiving and communications system and adds the numerical value to the final radiology report as a standard application. Correlation between the automatic and manual measurements was within 2% of error. An accurate automatic determination of head's circumference from MRI images is of definite clinical usefulness.
The Picture Administration and Communications System (PACS) was designed to replace the old film archiving system in hospitals in order to store and move varying medical image modalities. Using the standard Internet transport protocol, PACS creators designed a robust digital signaling platform to optimize media use, availability, and confidentiality. Nowadays PACS has become ubiquitous in medical facilities but lacks imaging analytical capabilities. A myriad of initiatives have been launched in the hope of achieving this goal, but current solutions face issues with security and ease-of-use that have precluded their widespread adoption.
Here, we present a PACS-based image processing tool that safeguards patient confidentiality, is user-friendly and is easy to implement. The final product is platform-independent, has a small degree of intrusiveness and is well suited to clinical and research work flows.
The characterization of tumors after being imaged is currently a qualitative process performed by skilled professionals. If we can aid their diagnosis by identifying quantifiable features associated with tumor classification, we may avoid invasive procedures such as biopsies and enhance efficiency. The aim of this paper is to describe the 3D EdgeRunner Pipeline which characterizes the shape of a tumor. Shape analysis is relevant as malignant tumors tend to be more lobular and benign ones tare generally more symmetrical. The method described considers the distance from each point on the edge of the tumor to the centre of a synthetically created field of view. The method then determines coordinates where the measured distances are rapidly changing (peaks) using a second derivative found by five point differentiation. The list of coordinates considered to be peaks can then be used as statistical data to compare tumors quantitatively. We have found this process effectively captures the peaks on a selection of kidney tumors.
KEYWORDS: Radiology, Kidney, 3D metrology, 3D modeling, 3D image processing, Image segmentation, Tumors, Image processing, Natural surfaces, 3D imaging standards
A conventional radiology report primarily consists of a large amount of unstructured text, and lacks clear, concise, consistent and content-rich information. Hence, an area of unmet clinical need consists of developing better ways to communicate radiology findings and information specific to each patient. Here, we design a new workflow and reporting system that combines and integrates advances in engineering technology with those from the medical sciences, the Multidimensional Interactive Radiology Report and Analysis (MIRRA). Until recently, clinical standards have primarily relied on 2D images for the purpose of measurement, but with the advent of 3D processing, many of the manually measured metrics can be automated, leading to better reproducibility and less subjective measurement placement. Hence, we make use this newly available 3D processing in our workflow. Our pipeline is used here to standardize the labeling, tracking, and quantifying of metrics for renal masses.
Tensor-Based Morphometry (TBM) is an increasingly popular method for group analysis of brain MRI data. The main steps in the analysis consist of a nonlinear registration to align each individual scan to a common space, and a subsequent statistical analysis to determine morphometric differences, or difference in fiber structure between groups. Recently, we implemented the Statistically-Assisted Fluid Registration Algorithm or SAFIRA,1 which is designed for tracking morphometric differences among populations. To this end, SAFIRA allows the inclusion of statistical priors extracted from the populations being studied as regularizers in the registration. This flexibility and degree of sophistication limit the tool to expert use, even more so considering that SAFIRA was initially implemented in command line mode. Here, we introduce a new, intuitive, easy to use, Matlab-based graphical user interface for SAFIRA’s multivariate TBM. The interface also generates different choices for the TBM statistics, including both the traditional univariate statistics on the Jacobian matrix, and comparison of the full deformation tensors.2 This software will be freely disseminated to the neuroimaging research community.
Magnetic Resonance Imaging (MRI) has been gaining popularity in the clinic in recent years as a safe in-vivo imaging technique. As a result, large troves of data are being gathered and stored daily that may be used as clinical training sets in hospitals. While numerous machine learning (ML) algorithms have been implemented for Alzheimer’s disease classification, their outputs are usually difficult to interpret in the clinical setting. Here, we propose a simple method of rapid diagnostic classification for the clinic using Support Vector Machines (SVM)1 and easy to obtain geometrical measurements that, together with a cortical and sub-cortical brain parcellation, create a robust framework capable of automatic diagnosis with high accuracy. On a significantly large imaging dataset consisting of over 800 subjects taken from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, classification-success indexes of up to 99.2% are reached with a single measurement.
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