Pvmatlab Manual

Manual

User Manual:

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pvmatlab

MATLAB package for handling ParaVision data

Bruker BioSpin MRI GmbH

D-76275 Ettlingen, Germany

Table of Contents

1 Introduction................................................................................................................3

2 Requirements and Installation...................................................................................3

2.1 System requirements.........................................................................................3

2.2 Installation..........................................................................................................4

3 Quick start..................................................................................................................5

4 Overview....................................................................................................................7

4.1 Data objects........................................................................................................7

4.2 File structure.......................................................................................................7

5 Data objects...............................................................................................................9

5.1 Common properties and methods......................................................................9

5.2 Raw data object................................................................................................10

5.3 Frame data object............................................................................................10

5.4 Cartesian k-space data object..........................................................................11

5.5 Image data object.............................................................................................12

6 Core data handling functions...................................................................................13

6.1 readBrukerParamFile.......................................................................................13

6.2 readBrukerRaw................................................................................................13

6.3 readBruker2dseq..............................................................................................14

6.4 convertRawToFrame........................................................................................15

6.5 convertFrameToCKData...................................................................................15

7 Warnings and Error messages................................................................................16

8 Feedback.................................................................................................................16

9 Appendix..................................................................................................................17

9.1 Some important parameters.............................................................................17

9.2 Required parameters........................................................................................18

10 Legal matters.........................................................................................................19

1  Introduction
The pvmatlab package is a collection of tools for Bruker users who wish to use 
MATLAB for processing ParaVision raw data, for performing basic image 
reconstruction of Cartesian k-space data and for importing/exporting ParaVision 
images. 
To get started, it is recommend to read at least section 2.2 (Installation) and to work 
through chapter 3 (Quick start), which addresses the most common functions of 
pvmatlab using a test data set included in the package.
Chapters 4 to 6 are dedicated to a somewhat more detailed description of the 
structure of pvmatlab, its functions and conventions. Some common warnings and 
error messages and what to do about them are listed in chapter 7.
A word of caution: pvmatlab is distributed 'as is' without any warranty and without any 
claim of being bug-free or complete. The output produced by pvmatlab should always 
be checked thoroughly, otherwise the results based on it might be wrong or 
meaningless.
Bruker does not provide customer support for pvmatlab like for a regular product and 
does not service the package regularly. However, if you notice bugs or malfunctions 
you may get a fix and also help other users of pvmatlab by notifying Bruker software 
support (see chapter 8).
The pvmatlab package is provided for non-commercial use only, at your Bruker client 
site, and may not be modified or redistributed to other institutions. Requests for 
pvmatlab can be made via email at: mri-software-support@bruker.de. See also 
chapter 10.
2  Requirements and Installation
2.1  System requirements
As pvmatlab is a collection of MATLAB functions, the minimum system requirements 
are determined by MATLAB (see http://www.mathworks.de/support/sysreq/). MATLAB 
is available for Windows, Linux and Mac platforms, therefore pvmatlab can be run on 
all these platforms, however, it has only been tested on Linux. 
A basic MATLAB installation, release R2008a or later, is required. Earlier releases do 
not support the object oriented code used in pvmatlab. No additional MATLAB 
toolboxes are needed. However, the core functions of pvmatlab could be used with 
earlier MATLAB releases or with Octave 3.6 or later, see chapter 6.
ParaVision data sets generated by versions 4.0, 5.0, 5.1, 5.1_Icon and 6.0 have been 
tested, but data acquired with different versions of ParaVision may work, too.
If large data sets need to be processed, additional memory requirements can be 
estimated as follows: For each double precision complex value of raw data, 16 bytes 
of memory are needed. For example, a 3D raw data set with a matrix size of 
256 x 256 x 256, acquired with a four-channel receive array and four repetitions uses 
3

4 GB of memory. Reducing the data precision to “single” (see chapter 5), will bring

the additional memory requirement down to 2 GB. During the processing, copies of

the data need to be made, so the recommended additional RAM size is two times the

required memory for the biggest raw data set, at the very least.

If the memory requirements exceed the available RAM, MATLAB will use swap space

on the file system which will slow down data processing considerably. Exceeding the

swap capacity will cause MATLAB to stop with an “out of memory” error.

2.2 Installation

1. Extract the archive pvmatlab.tar.gz into a folder of your choice. For example

copy pvmatlab.tar.gz to the MATLAB working folder /home/nmrsu/matlab,

and run

[nmrsu@host matlab]$ tar xvzf pvmatlab.tar.gz

By extracting the archive, the subfolder pvtools will be created. This path will

be referred to as “the pvmatlab folder” in the following, for this example it reads

/home/nmrsu/matlab/pvtools.

2. Add the pvmatlab folder including its subfolders to the MATLAB path:

For adding the paths permanently, use the MATLAB command

>> pathtool

Select “Add with Subfolders”, choose the pvmatlab folder and then select

“Save”.

For adding the pvmatlab folder only temporarily, run

>> addBrukerPaths

or include the command inside MATLAB scripts that use pvmatlab functions.

3. For information about the installed version of pvmatlab use

>> bruker_version

This completes the installation of pvmatlab. The file pvmatlab.tar.gz can be deleted or

moved to a different location.

3 Quick start

In this chapter, the main functions are presented as one would typically use them.

With the MATLAB script BrukerExample.m provided in the pvmatlab folder, the

steps of this quick start guide can be reproduced in MATLAB. A sample data set of a

kiwi is used in these examples, which is also contained in the pvmatlab folder.

1. Launch MATLAB and change into the pvmatlab folder

2. In the Command Window run

>> edit BrukerExample

MATLAB will open the example script.

3. Initialization of paths

Evaluate the initialization cell with <Ctrl+Enter>. This code sets the paths to

the pvmatlab functions and to the test data set:

addBrukerPaths;

baseDir = fileparts(mfilename('fullpath'));

pathTestData = fullfile(baseDir,'TestData/2/pdata/1');

4. Importing raw data

The next set of commands demonstrates how raw data are imported into a raw

data object (for more details concerning objects see chapter 5):

rawObj = RawDataObject(pathTestData);

numSlices = rawObj.Acqp.NI;

plot(real(rawObj.data{1}(1,:,319)));

The raw data of experiment number 2 can now be accessed (in this example

by plotting echo number 319) as well as the associated method and

acquisition parameters (in this example the number of images, Acqp.NI)

5. Preparing image reconstruction

Before the raw data can be reconstructed, they need to be sorted into k-space

correctly. This is done by creating and initializing a CKDataObject. Also, the

reco parameters need to be read from disk:

kdataObj = CKDataObject(pathTestData);

kdataObj = kdataObj.readReco;

6. Reconstructing images from Cartesian k-space

The k-space object provides a reconstruction method .reco that operates on

the raw data it has been initialized with and creates an image object as output.

Both objects provide a viewer to visualize the k-space data and the image,

respectively:

kdataObj.viewer;

imageObj = kdataObj.reco('all', 'image');

imageObj.viewer;

7. Importing / Exporting ParaVision images

The image object can also be used to import images created by the

ParaVision reconstruction pipeline:

imageObj = ImageDataObject(pathTestData);

imageObj.viewer;

For exporting an image, the path must be specified and visualization

parameters need to be generated. Then the writeImage routine can be called:

imageObj = imageObj.setDataPath('imagewrite', 'export/2/pdata/1');

imageObj = imageObj.genExportVisu('genmode','auto');

imageObj.writeImage;

pvmatlab will then generate a subfolder in the export folder, named

<subjectID>_<studyID> followed by the specified expno/pdata/procno. In this

case the image (2dseq) and visu parameters (visu_pars) are exported to

export/Kiwi_pvmatlab/2/pdata/1/

4 Overview

This chapter contains a brief overview of how the pvmatlab package is organized and

how it interacts with the ParaVision data structure. Effectively, pvmatlab translates the

ParaVision data stored in the file system into MATLAB objects and vice versa.

4.1 Data objects

The MATLAB components handling the raw data and image data are implemented as

objects. Apart from the actual data, an object can contain additional information about

the data (acquisition parameters, method parameters, etc.). In addition, the object

contains matlab code (“methods”) working with its data, e. g., an import routine and a

data viewer.

CKDataObj

.data

.viewer

.Acqp.NI 5

There are three types of raw data objects, “RawDataObject”, “FrameDataObject” and

“CKDataObject” and one image object, “ImageObject”.

4.2 File structure

ParaVision stores all data associated with an MR experiment in a file system tree.

Each study (with raw data, image data, parameters) is stored in a separate folder (for

example /opt/PV5.1/data/nmrsu/nmr/TestData) and can be copied to any

other location for processing with MATLAB.

There are binary files containing the actual raw or image data, and parameter files in

text format, containing all necessary parameters to interpret the binary data. For a

detailed description of all parameters please refer to the ParaVision manual. A small

selection of parameters most relevant to pvmatlab is given in section 9.1.

size = [256 128 1 1 1 5]

0.0241 + 0.0066i -0.0533 + 0.0210i -0.0258 + 0.0496i

0.0929 + 0.0402i 0.1404 - 0.0049i 0.1222 + 0.0111i

-0.0172 + 0.0620i 0.0002 + 0.0641i -0.0099 + 0.0540i

0.0287 + 0.0527i 0.0809 + 0.0650i 0.1082 + 0.0442i

0.0005 + 0.0112i 0.0370 + 0.0387i 0.0131 + 0.0120i

-0.0094 - 0.0077i 0.0041 + 0.0525i 0.0498 + 0.0789i

-0.0125 + 0.0035i 0.0394 + 0.0220i 0.0536 + 0.0174i

0.0233 - 0.0335i -0.0029 - 0.0074i 0.0022 + 0.0129i

-0.0160 + 0.0175i 0.0302 + 0.0137i 0.0713 – 0.0156i

...

The files that are relevant for pvmatlab can be found in the following locations in the

ParaVision data tree:

TestData/

↳subject

↳acqp

fid

rawdata.job0, .job1, ...

method

pdata/

↳1/

↳2dseq

reco

visu_pars

study folder

subject parameters

folder for each experiment (expno)

acquisition parameters

acquired raw data

method parameters

folder for processed data

folder for processed data set (procno)

reconstructed image

reconstruction parameters

visualization parameters

text

binary

text

binary

text

The following sketch shows how the data objects and files depend on each other:

job0...nfid

Acquisition Data

RawDataObject FrameDataObject CKDataObject

methodacqp

Acquisition Parameters

Import:

FrameDataObject(RawDataObject)

Import:

CKDataObject(FrameDataObject)

RawDataObject(path, Arguments) FrameDataObject(path, Arguments) CKDataObject(path, Arguments)

Empty:

RawDataObject()

Empty:

FrameDataObject()

Empty:

CKDataObject()

For example, a FrameDataObject can be created from an fid file and an acqp

parameter file, and a CKDataObject can then be created from this FrameDataObject

by calling

frameObj = FrameDataObject(pathTestData);

kdataObj = CKDataObject(frameObj);

5 Data objects

Usually, the data objects can be used as described in chapter 3. In that case, they

automatically take care of setting paths, reading the parameter files and then loading

the data. This chapter briefly describes the variables and routines of the data objects

in case you would like to control this process manually.

5.1 Common properties and methods

The data objects “RawDataObject”, “FrameDataObject”, “CKDataObject” and

“ImageObject” have common fields (properties), and matlab code (methods) to

contain and load data and parameters. These are

property method responsible for

Filespath setDataPath Paths to parameter and data files

Acqp readAcqp Acquisition parameters

Method readMethod Method parameters

Visu readVisu Visualization parameters

Reco readReco Reconstruction parameters

Subject readSubject Subject parameters

HeaderInformation - -

- setPrecision Internal representation of data

data specific, see sections 5.2-5.5 Actual data

For example, after creation of an object anyObj, the function

myObj = myObj.setDataPath('auto','/home/nmrsu/TestData/2/pdata/1');

will set the paths to all parameter files automatically. With the command

myObj = myObj.setDataPath('acqp','/home/nmrsu/myacqp');

only the acqp path is set. The property Filespath.path_to_acqp then contains

'/home/nmrsu/myacqp', the other paths are empty. A subsequent call of readAcqp

myObj = myObj.readAcqp;

will result in “myacqp” to be read to myObj.Acqp instead of the acqp file created by

ParaVision in the expno folder. Other parameter structs in myObj remain unchanged.

All objects also provide a function for setting the precision of the data. In case

memory is a limitation, consider setting the precision to 'single' (8 bytes per complex

data point instead of 16 bytes) on initializing. Default is 'double'.

myObj = RawDataObject(pathTestData, 'dataPrecision', 'single' );

To reduce the precision of an existing object, use:

myObj = myObj.setPrecision('single');

5.2 Raw data object

The RawDataObject represents the original raw data as an unsorted sequence of

acquired scans of length ScanSize. The data from an fid or job file is stored in a

matrix with dimensions

Raw data sizes

(numReceiveChannels, ScanSize, numScansPerChannel)

These matrices are stored in the data property in a 1D Cell-Array.

The following code creates a raw data object, sets the paths to the parameter files

and to the fid file, reads the acquisition parameters and calls the specific method

“readRaw” to load the raw data:

rawObj = RawDataObject;

rawObj = rawObj.setDataPath('auto','/home/nmrsu/TestData/2');

rawObj = rawObj.readAcqp;

rawObj = rawObj.readRaw;

The fid data matrix is now located in myRawObj.data{1}.

For a minimum set of acqp parameters required for loading raw data see section 9.2.

5.3 Frame data object

The FrameDataObject represents the raw data as a set of frames, i. e., the data are

put into an imaging context. For converting data from a RawDataObject into frames,

more acquisition parameters are required, namely the ACQ_dim, ACQ_phase_factor,

and ACQ_obj_order. Based on these parameters, the scans are sorted into separate

objects (e. g. slices), and reordered in case the acquisition was interleaved. The

resulting data matrix reflects this structure and the number of repetitions:

Frame data sizes

(ScanSize, numScansPerFrame, numReceiveChannels, numObjects, numRepetitions)

A FrameDataObject can be generated from a RawDataObject

frameObj = FrameDataObject(rawObj);

or directly from disk

frameObj = FrameDataObject('/home/nmrsu/TestData/2');

5.4 Cartesian k-space data object

The CKDataObject represents the raw data in Cartesian k-space according to the

encoding scheme used in the acquisition method. For converting data from a

FrameDataObject into k-space, method parameters are required, namely

'PVM_Matrix', 'PVM_EncSteps1', and, for 3D, 'PVM_EncSteps2'. Currently, the

methods RARE, FLASH, MSME and FISP are supported. The sizes of the data

matrix are:

Cartesian k-space sizes

(dim1, dim2, dim3, dim4, numReceiveChannels, numObjects, numRepetitions)

A CKDataObject can be created directly from disk as described in chapter 3 or from a

FrameDataObject

kdataObj = CKDataObject(frameObj);

The CKDataObject provides a viewer method (.viewer) to display the k-space data in

a MATLAB figure, and a .reco method to perform basic Fourier reconstruction. The

reconstruction requires a set of parameters in the Reco property.

Usage of .reco method

outObj = kdataObj.reco(recopart,

['image'], ['RECO_Parametername', ParameterValue], ...);

Input (optional in square brackets)

recopart A string or cell array of strings of reconstruction

steps to perform.

'all' = {'quadrature', 'phase_rotate', 'zero_filling',

'FT', 'sumOfSquares', 'transposition'}

'image' If given, outObj is of type ImageObject (see

section 5.5)

'RECO_Parametername',

ParameterValue

Additional reconstruction parameters

Output

outObj A CKDataObject, in which the specified recoparts have

been performed on the data. If the argument 'image' is

provided, outObj is an ImageObject

Example

kdataObj = kdataObj.setDataPath('reco','TestData/2/pdata/1/reco');

kdataObj = kdataObj.readReco;

imageObj = kdataObj.reco('all','image');

5.5 Image data object

The ImageObject contains a set of reconstructed images in its “data” property, which

can be created by reconstructing k-space data of a CKDataObject (see section 5.4),

or by loading images reconstructed by ParaVision (see chapter 3). In the latter case,

the data representation as frame groups is read from the visu_pars parameter file.

The sizes of the image matrix in the data property are:

Image sizes

(dim1, dim2, dim3, dim4, FrameGroupDim1, FrameGroupDim2, ...)

The ImageObject provides a viewer method (.viewer) to display the images in a

MATLAB figure, and a .writeImage method to write the image data into the

ParaVision files 2dseq and visu_pars.

For writing images to the disk, a path needs to be set by calling .setDataPath

imageObj = imageObj.setDataPath('imagewrite', 'export/2/pdata/1');

Note: The target folder will not be the exact path specified to setDataPath, but a

folder “<subjectID>_<studyID>” based on the subject and study parameters will be

created above the expno folder. For example, with subjectID “Kiwi” and studyID

“pvmatlab”, the target folder is export/Kiwi_pvmatlab/2/pdata/1

In addition, a valid set of visu parameters in the exportVisu property of the ImageObject

is required, which can be generated via

imageObj = imageObj.genExportVisu('genmode',genmode,

['TemplatePath',path]);

Input (optional in square brackets)

genmode Parameter source for generating the exportVisu struct.

Options:'Visu', 'Template', 'Subject', 'auto', default is 'auto'.

'TemplatePath',

path

Path to a visu template file. Default is expno/visu_pars

The exportVisu parameters are based on the structure of data and on all available

parameter structs (genmode 'auto') or on the parameter source specified in genmode.

Alternatively, the Visu parameters can be set manually using .setExportVisu

imageObj = imageObj.setExportVisu(parameterName,parameterValue);

To clear all or one specific parameter, use

imageObj = imageObj.setExportVisu('all','clear');

imageObj = imageObj.setExportVisu(parameterName,'clear');

With the path and visu parameters set, the image data can be exported by calling

imageObj.writeImage;

6 Core data handling functions

This chapter lists the core functions of pvmatlab that are used by the methods of the

data objects. They can also be used on their own and do not require object support,

therefore these functions also work with MATLAB releases earlier than R2008a and

possibly also with Octave.

6.1 readBrukerParamFile

Reads Bruker JCAMP parameter files (subject, acqp, method, reco, visu_pars) into a

struct.

Usage

paramStruct = readBrukerParamFile(filename);

Input

filename Full path and name of the parameter file

Output

paramStruct Structure containing parameter variables. The parameter

names are derived from the JCAMP tags.

Example

Acqp = readBrukerParamFile('TestData/2/acqp');

6.2 readBrukerRaw

Reads Bruker raw data (fid or rawdata.job*) into a matrix or cell array of matrices.

Usage

data = readBrukerRaw(Acqp, path_to_dataFile,

['specified_NRs', NR_array],

['specified_Jobs', job_array],

['precision', precision_str]);

Input arguments (optional in square brackets)

Acqp An acqp struct as generated by the function

readBrukerParamFile('path/acqp')

path_to_dataFile Full path and name (fid/rawdata.job0) of the raw data file

'specified_NRs',

NR_array

A list of repetitions to be read, starting with 1, when

using a standard fid file

'specified_Jobs',

job_array

A list of jobs to read, the first job being job number 0. If

only the fid-file should be read use 'specified_Jobs',[ -1 ]

'precision',

precision_string

Precision of the imported data: 'single' or 'double'. Single

precision uses 4 bytes to represent a (real) floating point

number, 'double' uses 8 bytes. Default is 'double'.

Output

data •If path_to_dataFile contains only an fid file, data is a cell with

a 3D matrix of size (ScanSize, numScans, numChannels)

•If path_to_dataFile contains only job files, data is a cell array

{job0, job1, job2, ...}, in which job* are 3D matrices of size

(numChannels, ScanSize, numScans)

•If path_to_dataFile contains an fid and job files, data will be a

cell array {fidFile, job0, ...}

Example

data = readBrukerRaw(Acqp,'TestData/2/fid','precision','single');

6.3 readBruker2dseq

Reads Bruker images (2dseq files) into a matrix

Usage

image = readBruker2dseq(path_to_2dseq, Visu,

['imageType', ForceType],['dim5_n', DimArray]) ;

Input arguments (optional in square brackets)

path_to_2dseq Full path and name of the 2dseq image file

Visu A visu struct as generated by the function

readBrukerParamFile('path/visu_pars')

'imageType',

ForceType

Force image type to be 'complex' or 'real'. Default is 'auto',

which reads the image type from the visu parameters

'dim5_n',

DimArray

Increase output dimensionality. Normally all frames are

indexed in the 5th dimension. dim5_n expands the 5th

dimension, e. g., with 24 visuFrames: 'dim5_n', [3 4 2]

Output

image complex or real image matrix with dimensions (dim1, dim2, dim3,

dim4, dimVisuFrame). With 'dim5_n' option, the dimensions are

(dim1, dim2, dim3, dim4, dim5_n(1), dim5_n(2), dim5_n(3), ...)

6.4 convertRawToFrame

Sorts the scans read by readBrukerRaw into frames

Usage

frame = convertRawToFrame(data, Acqp, ['specified_NRs', NRarray]);

Input arguments (optional in square brackets)

data A raw data matrix (fid/rawdata.job*), as read by

the function ReadBrukerRaw. Make sure it is not

a cell or cell array.

Acqp An acqp struct as generated by the function

readBrukerParamFile('path/acqp')

'specified_NRs', NRarray A list of repetitions to be converted.

Output

frame 5D frame matrix with size (ScanSize, numScansPerFrame,

numReceiveChannels, numObjects, numRepetitions)

6.5 convertFrameToCKData

Sorts the frames generated by convertRawToFrame into a k-space matrix. The

methods currently supported are RARE, FLASH, MSME and FISP.

Usage

ckdata = convertFrameToCKData(frame, Acqp, Method,

['specified_NRs', NRarray], ['useMethod', useMethod]);

Input arguments (optional in square brackets)

frame Frame data as generated by convertRawToFrame

Acqp An acqp struct as generated by the function

readBrukerParamFile('path/acqp')

Method A method struct as generated by the function

readBrukerParamFile('path/method') . This input is only

required if useMethod is true

'specified_NRs',

NRarray

A list of repetitions to be converted.

useMethod Indicates if parameters from the method file should be

used. Default is true.

Output

ckdata 7D matrix with Cartesian k-space data, with dimensions

(dim1,dim2,dim3,dim4,numChannels,numObjects,numRepetitions)

For more functions see

>> doc Contents

and the MATLAB source code documentation.

7 Warnings and Error messages

•Warning: Template can't be read. Proceeding without template

During image export, the method ImageDataObject.writeImage tries to read a

template visu parameter file in the expno path. If there is none, the remaining

parameter files are used to generate the visu parameters.

•Warning: You are using an unsupported acquisition method. Most probably

your result will be incorrect.

This warning occurs during conversion of frame data k-space data for methods

other than RARE, MSME, FLASH, FISP. Check results carefully!

•Cannot open parameter file. Problem opening file /opt/PV5.1/.../visu_pars

A set of parameters is required for the operation but the parameter file is

missing.

•Warning: It's recommended to read the reco-file first. ??? Reference to non-

existent field 'RECO_transposition'.

For running the CKDataObject.reco function, a set of reco parameters is

needed. Either call kdataObj.readReco to read an existing reco parameter file,

or specify the kdataObj.Reco structure yourself.

8 Feedback

If you notice a problem with a pvmatlab function that is not addressed in this manual,

you should first make sure that the problem persists after reinstalling pvmatlab (see

section 2.2) and is thus not caused by any adaption of the pvmatlab code.

If this is the case, verify that the problem is reproducible and send an email with a

description of the issue to Bruker software support (mri-software-support@bruker.de),

including

•Circumstances in which the malfunction occurs

•Computer platform, ParaVision version, MATLAB version

•Exact contents of error messages (if any)

•Anything else that could help reproduce the problem, e. g. sample data or

saved MATLAB workspace. Attachments should be smaller than 5 MB, send

larger files only when Bruker software support requests them.

9 Appendix

9.1 Some important parameters

Parameter name In file Description

ACQ_method acqp Name of the acquisition method e. g. RARE, FLASH

ACQ_dim acqp Acquisition dimension, 1D / 2D / 3D

ACQ_size acqp Dimensions of each object, e. g. [128 128 64] for 3D

NI acqp Number of objects (slices)

NR acqp Number of repetitions

ACQ_obj_order acqp Order in which the objects are measured

ACQ_jobs acqp Size of each job and more

ACQ_jobs_size acqp Number of jobs

PVM_Matrix method Dimension of encoding matrix

PVM_EncSteps1 method Phase encoding steps

PVM_EncSteps2 method Phase encodings in second phase encoding direction (3D)

VisuCoreFrameType visu_pars Type of the image: complex / real

VisuFGOrderDesc visu_pars Contains the FrameGroup dimensions

VisuCoreDim visu_pars Image dimension 1D / 2D / 3D

VisuCoreSize visu_pars Dimensions of each visuFrame e.g. [128 128 64] in 3D

VisuCoreFrameCount visu_pars Number of visuFrames in one data set.

VisuCoreDataSlope visu_pars Scaling factor of image data

VisuCoreDataOffs visu_pars Scaling offset of image data

9.2 Required parameters

The following table lists the minimum set of parameters that are required for the

respective functions

Functions Condition Parameter

struct

Required parameters

ReadBrukerRaw

RawDataObject.readRaw

Acqp GO_raw_data_format

BYTORDA

ACQ_size

GO_data_save

GO_block_size

AQ_mod

raw data in job files Acqp ACQ_jobs

ACQ_jobs_size

convertRawToFrame

FrameDataObject.calcFrameData

Acqp NI

ACQ_size

ACQ_phase_factor

ACQ_obj_order

ACQ_dim

convertFrameToCKData

CKDataObject.calcKData

Acqp NI

ACQ_size

ACQ_dim

AQ_mod

useMethod == true Method PVM_Matrix

PVM_EncSteps1

PVM_Matrix is 3D Method PVM_EncSteps2

readBruker2dseq

ImageDataObject.readImage

Visu VisuCoreWordType

VisuCoreByteOrder

VisuCoreSize

VisuCoreFrameCount

VisuCoreDataSlope

VisuCoreDataOffs

VisuCoreDim

VisuCoreDimDesc

ImageType not set

ImageType == 'auto'

Visu VisuCoreFrameType

10 Legal matters

In the following, the term “BRUKER” refers to Bruker BioSpin MRI GmbH, Rudolf-

Plank-Str. 23, 76275 Ettlingen, while the term “CLIENT” refers to the person or

institution receiving the MATLAB package for handling ParaVision data, pvmatlab.

The MATLAB package for handling ParaVision (pvmatlab) is provided to CLIENT for

non-commercial use at CLIENT's site. It is not allowed to use pvmatlab or any of its

parts in a product or software package that is distributed or sold to third parties.

The pvmatlab package must not be used for studies on humans.

The pvmatlab package is provided 'AS IS', without warranty of any kind, either

expressed or implied, including but not limited to the implied warranties of

merchantability and fitness for a particular purpose (Some states do not allow the

exclusion of implied warranties, so the exclusion may not apply to you).

Neither BRUKER nor its dealers or distributors assume liability for any alleged or

actual damage, direct, indirect, incidental or consequential, arising in any way out of

the use of or the inability to use pvmatlab.

By providing CLIENT with pvmatlab, it is not implied that BRUKER will also provide

customer support to CLIENT, should problems occur with using the package or as a

consequence thereof. Also BRUKER does not guarantee that pvmatlab is serviced

regularly.

All rights are reserved. In particular, this document, the pvmatlab source code and the

test data are protected by copyright. They may not be reproduced, adapted,

transmitted, or stored in any form by any process (electronic or otherwise) without the

specific written consent of BRUKER. Exempt from this regulation are reproduction,

adaption, transmission and storage that are necessary to use pvmatlab efficiently at

CLIENT's site, and to enable CLIENT's personnel access to pvmatlab functionality.

All product names used in this document are trademarks of their respective owners.

ParaVision is a registered trademark of Bruker BioSpin MRI GmbH in Germany, in

the United States of America and other countries; Bruker is a registered trademark of

BRUKER-PHYSIK AG.

Product names of other manufacturers are used solely to identify their products and

BRUKER is in no way associated or affiliated with these companies. In particular,

MATLAB is a registered trademark of The MathWorks, Inc.; Windows is a registered

trademark of Microsoft Corporation in the United States of America and other

countries; Linux is a registered trademark of Linus Torvalds in the United States of

America and other countries; Mac is a registered trademark of Apple Computer, Inc.

Pvmatlab Manual

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