Dataset Information

An open-source software tool for the generation of relaxation time maps in magnetic resonance imaging.

ABSTRACT:

Background

In magnetic resonance (MR) imaging, T1, T2 and T2* relaxation times represent characteristic tissue properties that can be quantified with the help of specific imaging strategies. While there are basic software tools for specific pulse sequences, until now there is no universal software program available to automate pixel-wise mapping of relaxation times from various types of images or MR systems. Such a software program would allow researchers to test and compare new imaging strategies and thus would significantly facilitate research in the area of quantitative tissue characterization.

Results

After defining requirements for a universal MR mapping tool, a software program named MRmap was created using a high-level graphics language. Additional features include a manual registration tool for source images with motion artifacts and a tabular DICOM viewer to examine pulse sequence parameters. MRmap was successfully tested on three different computer platforms with image data from three different MR system manufacturers and five different sorts of pulse sequences: multi-image inversion recovery T1; Look-Locker/TOMROP T1; modified Look-Locker (MOLLI) T1; single-echo T2/T2*; and multi-echo T2/T2*. Computing times varied between 2 and 113 seconds. Estimates of relaxation times compared favorably to those obtained from non-automated curve fitting. Completed maps were exported in DICOM format and could be read in standard software packages used for analysis of clinical and research MR data.

Conclusions

MRmap is a flexible cross-platform research tool that enables accurate mapping of relaxation times from various pulse sequences. The software allows researchers to optimize quantitative MR strategies in a manufacturer-independent fashion. The program and its source code were made available as open-source software on the internet.

SUBMITTER: Messroghli DR

PROVIDER: S-EPMC2919441 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

An open-source software tool for the generation of relaxation time maps in magnetic resonance imaging.

Messroghli Daniel R DR Rudolph Andre A Abdel-Aty Hassan H Wassmuth Ralf R Kühne Titus T Dietz Rainer R Schulz-Menger Jeanette J

BMC medical imaging 20100730

<h4>Background</h4>In magnetic resonance (MR) imaging, T1, T2 and T2* relaxation times represent characteristic tissue properties that can be quantified with the help of specific imaging strategies. While there are basic software tools for specific pulse sequences, until now there is no universal software program available to automate pixel-wise mapping of relaxation times from various types of images or MR systems. Such a software program would allow researchers to test and compare new imaging ...[more]

PMID: 20673350

Similar Datasets

Project description:In order that biological meaning may be derived and testable hypotheses may be built from proteomics experiments, assignments of proteins identified by mass spectrometry or other techniques must be supplemented with additional notation, such as information on known protein functions, protein-protein interactions, or biological pathway associations. Collecting, organizing, and interpreting this data often requires the input of experts in the biological field of study, in addition to the time-consuming search for and compilation of information from online protein databases. Furthermore, visualizing this bulk of information can be challenging due to the limited availability of easy-to-use and freely available tools for this process. In response to these constraints, we have undertaken the design of software to automate annotation and visualization of proteomics data in order to accelerate the pace of research. Here we present the Software Tool for Researching Annotations of Proteins (STRAP), a user-friendly, open-source C# application. STRAP automatically obtains gene ontology (GO) terms associated with proteins in a proteomics results ID list using the freely accessible UniProtKB and EBI GOA databases. Summarized in an easy-to-navigate tabular format, STRAP results include meta-information on the protein in addition to complementary GO terminology. Additionally, this information can be edited by the user so that in-house expertise on particular proteins may be integrated into the larger data set. STRAP provides a sortable tabular view for all terms, as well as graphical representations of GO-term association data in pie charts (biological process, cellular component, and molecular function) and bar charts (cross comparison of sample sets) to aid in the interpretation of large data sets and differential analyses experiments. Furthermore, proteins of interest may be exported as a unique FASTA-formatted file to allow for customizable re-searching of mass spectrometry data, and gene names corresponding to the proteins in the lists may be encoded in the Gaggle microformat for further characterization, including pathway analysis. STRAP, a tutorial, and the C# source code are freely available from http://cpctools.sourceforge.net.

Project description:BACKGROUND:MR-guided focused ultrasound or high-intensity focused ultrasound (MRgFUS/MRgHIFU) is a non-invasive therapeutic modality with many potential applications in areas such as cancer therapy, drug delivery, and blood-brain barrier opening. However, the large financial costs involved in developing preclinical MRgFUS systems represent a barrier to research groups interested in developing new techniques and applications. We aim to mitigate these challenges by detailing a validated, open-source preclinical MRgFUS system capable of delivering thermal and mechanical FUS in a quantifiable and repeatable manner under real-time MRI guidance. METHODS:A hardware and software package was developed that includes closed-loop feedback controlled thermometry code and CAD drawings for a therapy table designed for a preclinical MRI scanner. For thermal treatments, the modular software uses a proportional integral derivative controller to maintain a precise focal temperature rise in the target given input from MR phase images obtained concurrently. The software computes the required voltage output and transmits it to a FUS transducer that is embedded in the delivery table within the magnet bore. The delivery table holds the FUS transducer, a small animal and its monitoring equipment, and a transmit/receive RF coil. The transducer is coupled to the animal via a water bath and is translatable in two dimensions from outside the magnet. The transducer is driven by a waveform generator and amplifier controlled by real-time software in Matlab. MR acoustic radiation force imaging is also implemented to confirm the position of the focus for mechanical and thermal treatments. RESULTS:The system was validated in tissue-mimicking phantoms and in vivo during murine tumor hyperthermia treatments. Sonications were successfully controlled over a range of temperatures and thermal doses for up to 20 min with minimal temperature overshoot. MR thermometry was validated with an optical temperature probe, and focus visualization was achieved with acoustic radiation force imaging. CONCLUSIONS:We developed an MRgFUS platform for small-animal treatments that robustly delivers accurate, precise, and controllable sonications over extended time periods. This system is an open source and could increase the availability of low-cost small-animal systems to interdisciplinary researchers seeking to develop new MRgFUS applications and technology.

Project description:BackgroundPostmortem magnetic resonance imaging (MRI) has been used to investigate the cause of death, but due to time constraints, it is not widely applied to the heart. Therefore, MRI analysis of the heart after formalin fixation was previously performed. However, the changes in MRI signal values based on the fixation time of formalin were not investigated. The objective was to investigate changes over time in the T1- and T2-values of MRI signals in normal areas of hearts removed during autopsy, hearts subsequently fixed in formalin, and heart specimens sliced for the preparation of pathological specimens.MethodsThe study subjects were 21 autopsy cases in our hospital between May 26, 2019 and February 16, 2020 whose hearts were removed and scanned by MRI. The male:female ratio was 14:7, and their ages at death ranged from 9 to 92 years (mean age 65.0 ± 19.7 years). Postmortem (PM)-MRI was conducted with a 0.3-Tesla (0.3-T) scanner containing a permanent magnet. A 4-channel QD head coil was used as the receiver coil. Scans were performed immediately after removal, post-formalin fixation, and after slicing; 7 cases were scanned at all three time points.ResultsThe T1- and T2-values were calculated from the MRI signals of each sample organ at each scanning stage. Specimens were sliced from removed organs after formalin fixation, and the changes in T1- and T2-values over time were graphed to obtain an approximate curve. The median T1-values at each measurement time point tended to decrease from immediately after removal. The T2-values showed the same tendency to decrease, but this tendency was more pronounced for the T1-values.ConclusionMRI signal changes in images of heart specimens were investigated. Formalin fixation shortened both T1- and T2-values over time, and approximation formulae were derived to show these decreases over time. The shortening of T1- and T2-values can be understood as commensurate with the reduction in the water content (water molecules) of the formalin-fixed heart.

Project description:BackgroundElectronic medical records, including pathology reports, are often used for research purposes. Currently, there are few programs freely available to remove identifiers while leaving the remainder of the pathology report text intact. Our goal was to produce an open source, Health Insurance Portability and Accountability Act (HIPAA) compliant, deidentification tool tailored for pathology reports. We designed a three-step process for removing potential identifiers. The first step is to look for identifiers known to be associated with the patient, such as name, medical record number, pathology accession number, etc. Next, a series of pattern matches look for predictable patterns likely to represent identifying data; such as dates, accession numbers and addresses as well as patient, institution and physician names. Finally, individual words are compared with a database of proper names and geographic locations. Pathology reports from three institutions were used to design and test the algorithms. The software was improved iteratively on training sets until it exhibited good performance. 1800 new pathology reports were then processed. Each report was reviewed manually before and after deidentification to catalog all identifiers and note those that were not removed.Results1254 (69.7 %) of 1800 pathology reports contained identifiers in the body of the report. 3439 (98.3%) of 3499 unique identifiers in the test set were removed. Only 19 HIPAA-specified identifiers (mainly consult accession numbers and misspelled names) were missed. Of 41 non-HIPAA identifiers missed, the majority were partial institutional addresses and ages. Outside consultation case reports typically contain numerous identifiers and were the most challenging to deidentify comprehensively. There was variation in performance among reports from the three institutions, highlighting the need for site-specific customization, which is easily accomplished with our tool.ConclusionWe have demonstrated that it is possible to create an open-source deidentification program which performs well on free-text pathology reports.

Dataset Information

An open-source software tool for the generation of relaxation time maps in magnetic resonance imaging.

Background

Results

Conclusions

Publications

An open-source software tool for the generation of relaxation time maps in magnetic resonance imaging.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets