Institute of Neurology, University College London

Using jMRUI for training at IoN-UCL Advance Neuroimaging MSc

We are delighted to report that the jMRUI software will be used for training purposes at the Advanced Neuroimaging MSc Programme of the Institute of Neurology (IoN) and the National Hospital for Neurology and Neurosurgery of the University College London (UCL).

UCL Institute of Neurology and the National Hospital for Neurology and Neurosurgery
UCL Institute of Neurology (left) and the National Hospital for Neurology and Neurosurgery (right) in Queen Square, London (Source: Wikipedia).

The MSc in Advanced Neuroimaging is a multidisciplinary programme which aims to give students a strong working knowledge of neuroanatomy and an in-depth understanding of standard and advanced neuroimaging techniques for image acquisition, processing and analysis in the diagnosis, treatment and study of a full range of neurological diseases. During their time at Queen Square, students will have the opportunity to contribute to world-leading research and have access to cutting edge neuroimaging facilities.

You are welcome to follow their initiative, but keep in mind that some rules must be fulfilled if you want to use and distribute the jMRUI software for free.

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Improved import plug-ins of GE and Siemens data-sets

GE and Siemens format update

Updated plug-ins for loading GE and Siemens data sets are now available. If you experienced any problems with any of those two data types, please download and update the plug-ins in your jMRUI installation.

Installation

  1. Before proceeding please make a copy of GE and Siemens plug-ins in any external directory
  2. Copy the plug-ins to “<jmrui_root>\plugins” folder; in case of the ge.jar file replace the original one by the new one. Note: you must not leave the copy of the original ge.jar file in the  “<jmrui_root>\plugins” folder together with the new one.

Download

Download GE_Siemens_Update-1.zip (25.4 KB)

Troubleshooting

If the problem with Siemens Dicom persists, please load the data file using “File/Open As” and select “Siemens DICOM”.

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jmrui2xml plugin

The jMRUI2XML plugin is reported in BMC Bioinformatics

From raw data to data-analysis for magnetic resonance spectroscopy – the missing link: jMRUI2XML.
Victor Mocioiu, Sandra Ortega-Martorell, Iván Olier, Michal Jablonski, Jana Starcukova, Paulo Lisboa, Carles Arús and Margarida Julià-Sapé.
BMC Bioinformatics
2015, 16:378
DOI: 10.1186/s12859-015-0796-5

Abstract

Background

Magnetic resonance spectroscopy provides metabolic information about living tissues in a non-invasive way. However, there are only few multi-centre clinical studies, mostly performed on a single scanner model or data format, as there is no flexible way of documenting and exchanging processed magnetic resonance spectroscopy data in digital format. This is because the DICOM standard for spectroscopy deals with unprocessed data.

The paper proposes a plugin tool developed for jMRUI, namely jMRUI2XML, to tackle the latter limitation, given that jMRUI has evolved into a plugin platform allowing for implementation of novel features.

Results

jMRUI2XML is a Java solution that facilitates common preprocessing of magnetic resonance spectroscopy data across multiple scanners. Its main characteristics are: 1) it automates magnetic resonance spectroscopy preprocessing, and 2) it can be a platform for outputting exchangeable magnetic resonance spectroscopy data. The plugin works with any kind of data that can be opened by jMRUI and outputs in extensible markup language format. Data processing templates can be generated and saved for later use.

The output format opens the way for easy data sharing- due to the documentation of the preprocessing parameters and the intrinsic anonymization – for example for performing pattern recognition analysis on multicentre/multi-manufacturer magnetic resonance spectroscopy data.

Conclusions

jMRUI2XML provides a self-contained and self-descriptive format accounting for the most relevant information needed for exchanging magnetic resonance spectroscopy data in digital form, as well as for automating its processing. This allows for tracking the procedures the data has undergone, which makes the proposed tool especially useful when performing pattern recognition analysis. Moreover, this work constitutes a first proposal for a minimum amount of information that should accompany any magnetic resonance processed spectrum, towards the goal of achieving better transferability of magnetic resonance spectroscopy studies.

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Anton Bielousov - Toronto skyline at night - DSC_2134.

jMRUI at the ISMRM conference in Toronto

ISMRM 23rd Annual Meeting Logo

We would like to invite you to the Philips Healthcare booth at the ISMRM 23rd Annual Meeting in Toronto, where we will demonstrate the new jMRUI version, as well as a new clinical MRS and MRSI viewer which should be available in the near future.

The booth is located at the 800 Level of the South Building of the Metro Toronto Convention Centre, just past the entrance, in front of you on the left side (see below).

Philips booth location at Toronto Convention Center
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jMRUI 5.2

jMRUI 5.2 released

New version of jMRUI 5.2 is available.

New plug-ins

  • jMRUI2XML – extends the functionality of jMRUI by automating to a certain degree spectral preprocessing along with some algorithms that were not previously present in jMRUI. Purpose: data preprocessing for classification. Downloadable as a separate plug-in from http://gabrmn.uab.es/?q=jmrui2xml
  • SpectraClassifier – is built for designing and implementing Magnetic Resonance Spectroscopy (MRS)-based classifiers. The main goal of SC is to allow users with minimum background knowledge of multivariate statistics to perform a fully automated pattern recognition analysis. Downloadable as a separate plug-in from http://gabrmn.uab.es/?q=sc
  • InterpretDSS –   allows radiologists, medical physicists, biochemists or, generally speaking, any person with a minimum knowledge of what an MR spectrum is, to enter their own raw data, acquired at 1.5 T, and to analyze them. The system is expected to help in the categorization of MR Spectra from abnormal brain masses.  Downloadable as a separate plug-in from http://gabrmn.uab.es/?q=dss
  • Monte Carlo modeling  (for quantification results), available in Linux.

Improvements

QUEST / AQSES

  • Visualization of a basis set overlaid over a spectrum (Shift-TAB mode) has been improved:
    1. A metabolite that has been shifted interactively can be correctly shifted again into a new position.
    2. Multiple metabolites can be displayed at the same time over the quantified spectrum.
    3. Zoom is kept when a new metabolite is selected.
    4. When zoomed, analysed and metabolite spectra can be scrolled and the invisible part of the spectrum can be brought to the window with the same frequency scaling (by dragging the spectrum with mouse and SHIFT key pressed).
  • The frequency axes of the basis set and of the spectrum to be fitted are automatically aligned if both the basis set and the spectrum have been calibrated (a basis set is calibrated in NMRScopeB automatically).
  • The metabolite list is saved with parameters such as reference frequency, transmitter frequency and SW (which can be loaded to 1D mode as regular set of spectra).
  • Metabolites stored in the list (.ml format) can be additionally  processed in a 1D window and saved in “.ml” format from the 1D window.
  • S/N added to the result protocol.
  • Normalization is now an action, not a permanent change to the basis set.

QUEST / AQSES / AMARES

  • Quantitation results can be saved as a set of estimated metabolites in “.mrui” format and also passed to the 1D window, and so each individual estimated component (metabolite) can be additionally analysed.

AMARES

  • The database can be saved as a text file, the binary format is described.
  • Results: the Gaussian linewidth is exported correctly into the text file.
  • Results: the linewidths and their standard deviations in the results txt file are positive numbers.
  • Prior knowledge: the linewidth for the Gaussian-shaped model peak is corrected.
  • Peak picking works correctly after opening AMARES for the second time during one fitting session.

 NMRScopeB

  • Code implemented in Python.
  • Runs also in GNU/Linux.
  • A new protocol for the SPECIAL sequence.
  • Simulated FIDs can be integrated and/or multiplied by a user defined function (for simulation of VOI selection, inhomogeneous excitation, chemical-shift effect).
  • A new calibration constant was added  into a protocol; simulated FIDs  are divided by this constant in order to facilitate mixing simulated and measured signals in basis sets;  normalization is unnecessary in QUEST for data simulated with NMRScopeB.
  • Improved graphical visualization of sequences, possibility of export in vector graphics formats.
  • New interface:  multiple metabolites can be selected and simulated together (similar user comfort as in NMRScope).
  • Simulated metabolites can be saved in a metabolite list and loaded directly in QUEST/AQSES as a new basis set (no need to create manually a list of metabolites from individually simulated metabolites).
  • All information such as reference frequency, transmitter frequency and SW is saved in the metabolite list.
  • Metabolites stored in the list (.ml format) can be additionally apodized (for T2* effect) and saved in .ml format in 1D window (outside NMRScopeB).
  • NMRScopeB can be used in a batch mode.
  • More instances of NMRScopeB can be used at a time.
  • Simulated signals can be stored in vector graphics formats, e.g. Windows Metafiles, SVG, etc. for export to document.
  • Protocols are saved with all corresponding files.

Data formats

  • Data formats are recognized automatically, the data format (vendor) does not have to be selected by the user (Open button).
  • Possibility to load more spectra from different directories into 1D mode at the same time for Bruker data format.
  • MRSI Siemens “.rda” data are loaded with information about its orientation, and metabolite images are overlaid over an anatomical image in the correct spatial position.
  • jMRUI v3 format can be loaded.

 Preprocessing

  • Group delay and digital-filter transient correction (automated for the Bruker data format).
  • “ER Filter” fixed  for  multiple spectra.
  • Apodization filter width is defined as the full linewidth at half its maximum, not by damping factors.
  • Phase correction was significantly speeded-up for multiple spectra.
  • HLSVD – the Cancel button cancels the dialogue without performing HLSVD.
  • New possibility to average selected signals.

Signal simulation

  • Noise can be simulated even without signal (it is defined by its effective value, not as percentage of the signal amplitude).
  • The simulated signal can have parameter alpha and beta both set to 0 (for the simulation of a constant signal).

General improvements

  • Graphs of spectra/signals and quantification results can be stored in vector graphics formats (Print/Export, button Save to HTML);  Windows Metafile, SVG and other formats are saved together with HTML.
  • Zoomed spectra/signals can be scrolled together with the frequency/time axis (by dragging the spectrum with mouse and SHIFT key pressed), hidden parts are brought to display without changing the zoom.
  • The precision of the cursor position display in ppm/Hz can be set in Options (define the number of decimal digits), display of either the nearest sample or an interpolated value can be selected.
  • The automated FID/ECHO identification, based on signal maximum position, can be switched off (in Options).
  • CSI of non-square spatial matrix size can be loaded.
  • Further small improvements and bugs fixed.
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New jMRUI website screenshot

Welcome to the new jMRUI website!

It has been a long while since we deployed the jMRUI website and after years of ageing it was intensely pleading for a thorough update. The work began under the FAST European Project, but it has finally come to light with the current TRANSACT European Project.

The new website runs on WordPress, a free and open source blogging and content-management software running on PHP and MySQL. According to Wikipedia, WordPress is used by more than 23.3% of the top 10 million websites as of January 2015, and it is the most popular blogging system in use on the Web at more than 60 million websites.

It is our hope that this new website will foster the development of a virtual community that will work towards spreading the use of MR spectroscopy in the pre-clinical and clinical worlds.

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