dbdicom 0.0.9

Reading and writing DICOM databases

dbdicom

dbdicom is a Python interface for reading and writing DICOM databases.

CAUTION: dbdicom is developed in public but is work in progress and backwards compatibility is not guaranteed. It is very likely there WILL be breaking changes in future versions

Installation

Run pip install dbdicom.

Browsing a DICOM folder

Reading and opening a DICOM folder

Open a DICOM database in a given folder, read it and print a summary of the content:

from dbdicom import Folder

folder = Folder('C:\\Users\\MyName\\MyData\\DICOMtestData')
folder.open()
folder.print()

The first time the folder is read this will be relatively slow. This is because each individual DICOM file in the folder is read and summarised in a table (csv file). If the folder is reopened again later, the table can be read directly and opening will be much faster.

Use scan() to force a rereading of the folder. This may be of use when files have become corrupted, or have been removed/modified by external applications:

folder.scan()

After making changes to the DICOM data, the folder should be closed properly so any changes can be either saved or rolled back as needed:

folder.close()

If unsaved changes exist, close() will prompt the user to either save or restore to the last saved state.

Retrieving objects from the folder

A DICOM database has a hierarchical structure. The files are instances of a specific DICOM class and correspond to real-world objects such as images or regions-of-interest. Instances are grouped into a series, and multiple series are grouped into studies. Typically a study consist of all the data derived in a single examination of a subject. Studies are grouped into patients, which correspond to the subjects the study is performed upon. A patient can be an actual patient, but can also be a healthy volunteer, an animal, a physical reference object, or a digital reference object.

To return a list of all patients, studies, series or instances in the folder:

instances = folder.instances()
series = folder.series()
studies = folder.studies()
patients = folder.patients()

The same functions can be used to retrieve the children of a certain parent object. For instance, to get all studies of a patient:

studies = patient.studies()

Or all series under the first of those studies:

series = studies[0].series()

Or all instances of a study:

instances = study.instances()

And so on for all other levels in the hierarchy. Individual objects can also be access directly using indices. For instance to retrieve the first instance in the folder:

instance = folder.instances(0)

These can be chained together for convencience, e.g. to get all instances instance of series 5 in study 1 of patient 2:

instance = folder.patients(2).studies(1).series(5).instances()

These functions also work to find objects higher up in the hierarchy. For instance, to find the patient of a given series:

patient = series.patients()

In this case the function will return a single object rather than a list.

Finding DICOM objects in the folder

Each DICOM file has a number of attributes describing the properties of the object. Examples are PatientName, StudyDate, etc. A full list of attributes for specific objects can be found here: https://dicom.innolitics.com/.

Each known attribute is identified most easily by a keyword, which has a capitalised notation. Objects in the folder can be can also be listed by searching on any DICOM tag:

instances = folder.instances(PatientName = 'John Dory')

This will only return the instances for patient John Dory. Objects can also be searched on multiple DICOM tags:

series = folder.instances(
    PatientName = 'John Dory', 
    ReferringPhysicianName = 'Dr. No', 
)

In this case objects are only returned if both conditions are fullfilled. Any arbitrary number of conditions can be entered, and higher order objects can be found in the same way:

studies = folder.studies(
    PatientName = 'John Dory', 
    ReferringPhysicianName = 'Dr. No', 
)

As an alternative to calling explicit object types, you can call children() and parent to move through the hierarchy:

studies = patient.children()
patient = studies[0].parent

The same convenience functions are available, such as using an index or searching by keywords:

studies = patient.children(ReferringPhysicianName = 'Dr. No')
study = patient.children(0)

Moving and removing objects

To remove an object from the folder, call remove() on the object:

study.remove()
instance.remove()

remove() can be called on Patient, Study, Series or Instances.

Moving an object to another parent can be done with move_to() For instance to move a study from one patient to another:

study = folder.patients(0).studies(0)
new_parent = folder.patients(1)
study.move_to(new_parent)

Objects can also be moved to objects higher up in the hierarchy. Any missing parents will be automatically created. For instance:

instance = folder.instances(0)
study = folder.studies(1)
instance.move_to(study)

This will move instance from its current parent series to study. Since no new parent series under study has been provided, a new series will be created under study and used as a parent for instance.

Copying and creating objects

A DICOM object can be copied by calling copy():

study = folder.patients(0).studies(0)
new_study = study.copy()

This will create a copy of the object in the same parent object, i.e. study.copy() in the example above has created a new study in patient 0. This can be used for instance to copy-paste a study from one patient to another:

study = folder.patients(0).studies(0)
new_parent = folder.patients(1)
study.copy().move_to(new_parent)

This is equivalent to using copy_to():

study.copy_to(new_parent)   

To create a new object, call new_child() on the parent:

series = study.new_child()

series will now be a new (empty) series under study.

Export and import

To export an object out of the folder to an external folder, call export() on any dicom object with the export path as argument:

series.export(path)

If no path is given then the user will be asked to select one.

TO DO Equivalently to import DICOM files from an external folder, call import() with a list of files:

folder.import(files)

Creating and modifying DICOM files

Reading DICOM attributes

An object's DICOM attributes can be read by using the DICOM keyword of the attribute:

dimensions = [instance.Rows, instance.Columns]

All attributes can also be accessed at series, study, patient or folder level. In this case they will return a single value taken from their first instance.

rows = folder.patient(0).series(0).Rows

To print the Rows for all instances in the series, iterate over them:

for instance in series.instances():
    print(instance.Rows)

DICOM attributes can also be accessed using the list notation, using either the keyword as a string or a (group, element) pair.

columns = instance['Columns']
columns = instance[(0x0028, 0x0010)]

The tags can also be accessed as a list, for instance:

dimensions = ['Rows', (0x0028, 0x0010)]
dimensions = instance[dimensions] 

This will return a list with two items. As shown in the example, the items in the list can be either KeyWord strings or (group, element) pairs. This also works on higher-level objects:

dimensions = ['Rows', (0x0028, 0x0010)]
dimensions = patient[dimensions] 

As for single KeyWord attributes this will return one list taken from the first instance of the patient.

Editing attributes

DICOM tags can be modified using the same notations:

instance.EchoTime = 23.0

or also:

instance['EchoTime'] = 23.0

or also:

instance[(0x0018, 0x0081)] = 23.0

Multiple tags can be inserted in the same line:

shape = ['Rows', 'Columns']
instance[shape] = [128, 192]

When setting values in a series, study or patient, all the instances in the object will be modified. For instance, to set all the Rows in all instances of a series to 128:

series.Rows = 128

This is shorthand for:

for instance in series.instances():
     instance.Rows = 128

Read and write

By default all changes to a DICOM object are made on disk. For instance if a DICOM attribute is changed

instance.Rows = 128

The data are read from disk, the change is made, the data are written to disk again and memory is cleared. Equally, if a series is copied to another study, all its instances will be read, any necessary changes made, and then written to disk and cleared from memory.

For many applications reading and writing from disk is too slow. For faster access at the cost of some memory usage, the data can be read into memory before performing any manipulations:

series.read()

After this all changes are made in memory only. At any point the changes can be written out again by calling write():

series.write()

This will still retain the data in memory for an further editing. In order to delete them from memory and free up the space, call clear():

series.clear()

After calling clear(), all subsequent changes are made to disk again. These operations can be called on patients, studies, series or instances.

Save and restore

All changes made in a DICOM folder are reversible until they are saved. To save all changes, use save():

folder.save()

This will permanently burn all changes that are made on disk. Changes that are only made in memory will not be saved in this way. In order to save all changes including this that are made in memory, make sure to call write() first. These commands can also be piped for convenience:

folder.write().save()

In order to reverse any changes made, use restore() to revert back to the last saved state:

folder.restore()

This will roll back all changes on disk to the last changed state. As for save(), changes made in memory alone will not be reversed. In order to restore all changes in memory as well, read the data again after restoring:

folder.restore().read()

This will read the entire folder in meomory, which is not usually appropriate. However, save() and restore() can also be called at the level of individual objects:

series.restore()

will reverse all changes made since the last save, but only for this series. Equivalently:

series.save()

will save all changes made in the series permanently.

DICOM Classes

Each DICOM file in a folder holds and instance of a DICOM class, which in turn represents an object in the real world such as an MR image, or an image co-registration, an ECG, etc. The innolitics DICOM browser shows all possible DICOM Classes in an easily searchable manner.

In dbdicom such DICOM classes are represented by a separate python class. When an instance or list of instances are generated, for instance through:

instances = series.instances()

then each instance is automatically returned as an instance of the appropriate class. As an example, if the first instance of the series represents an MR Image, then instances[0] will be an instance of the class "MRImage", which on itself inherits functionality from a more general "Image" class. This means instance[0] automatically has access to functionality relevant for images, such as:

array = instances[0].array()

this will return a 2D numpy array holding the pixel data, and will automatically correct for particular MR image issues such as the use of private rescale slopes and intercepts for Philips data.

Other relevant functionality is explained in the reference guide of the individual classes. At the moment the DICOM classes are very limited in scope, but this will be extended over time as needs arise in ongoing projects.

Creating DICOM files from scratch

TO DO DICOM data can be created from scratch by instantiating one of the DICOM classes:

new_image = MRImage()

This will create an MRI image with empty pixel data. Since no parent series/study/patient are provide, defaults will be automatically created. At this point the image will only exist in memory but can be edited in the usual way. For instance to assign pixel data based on an empty numpy array:

array = numpy.zeros(128, 128)
new_image.set_array(array)

In order to save the image to disk an instance of the folder class needs to be provided. This can point to an empty folder, or to an existing DICOM database where the new data will be added:

new_image.folder = Folder('C:\\Users\\MyName\\MyData\\New Folder')

After setting a folder, the image can be written to disk:

new_image.write()

An instance can also be read from a single file:

image = MRImage('C:\\Users\\steve\\Data\\my_dicom_file.ima')

Changes to the file can then be made as usual:

image.PatientName = 'John Dory'
image.array = numpy.zeros((128,128)

and then saved as image.write(). When used in this way the class is just a simple wrapped for a pydicom dataset.

User interactions

dbdicom can be used in standalone scripts or at command line, to streamline integration in a GUI, communication with the user should be performed via two dedicated attributes status and dialog. dialog and status attributes are available to the folder class, and to any DICOM object.

The status attribute is used to send messages to the user, or update on progress of a calculation:

series.status.message("Starting calculation...")

When operating in command line mode this will simply print the message to the terminal. If dbdicom is used in a GUI, this will print the same message to the status bar. Equivalently, the user can be updated on the progress of a calculation via:

series.status.message("Calculating..")
for i in range(length):
    series.status.progress(i, length)

This will print the message with a percentage progress at each iteraion. When used in a GUI, this will update the porgress bar of the GUI. For use in a GUI, it is required to reset the progress bar after exiting the loop:

series.status.hide()

When operating in command line, this statement does nothing, but it makes the pipeline ready to be deloyed in a GUI without modification.

In addition, dialogs can be used to send messages to the user or prompt for input. In some cases a dialog may halt the operation of te program until the user has performed the appropriate action, such as hitting enter or entering a value. In command line operator or scripts the user will be prompted for input at the terminal. When using in a GUI, the user will be prompted via a pop-up. Example:

series.dialog.question("Do you wish to proceed?", cancel=True)

When used in a script, this will ask the user to enter either "y" (for yes), "n" (for no) or "c" (for cancel) and the program execution will depend on the answer. When the scame script is deployed in a GUI, the question will be asked via a pop-up window and a button push to answer. A number of different dialogs are available via the dialog attribute (see reference guide).

About dbdicom

Why dbdicom?

DICOM is scary. And it has been the universally accepted standard for medical images for decades. Why is that? It is because it is scary. DICOM is extremely detailed and rigorous in the description of its terminology and structure. It has to be, because DICOM deals with the most complex and sensitive data possible: your medical history. All of it. Every single one of your DICOM images in a clinical archive contains the key to access all of your medical details. This allows doctors to link your images to your blood tests, family history, previous diagnosis treatments, other imaging, and so on. And this is important to make the best possible informed decisions when it comes to your health.

In medical imaging research this additional information is often seen as a nuisance and discarded prior to processing of the images. Typically a data array of some sort is extracted, perhaps also some key geometrical descriptors such as pixel sizes or a transformation matrix, and all the other information is ignored.

Conversion into a lossy data format is often sufficient for method development or basic scientific research, but when it comes to deploying these methods in clinical studies, all this additional (discarded) information is just as important as in clinical practice. It ensures that all derived data are properly traceable to the source, and can be compared between subjects and within a subject over time. It allows to test for instance whether a new (expensive) imaging method provides an additive benefit over and above (cheap) data from medical history, clinical exams or blood tests. And so, if we accept that new image analysis methods ultimately will need to be tested clinically (and ideally sooner rather than later), then we simply can't avoid the need to convert results back to DICOM. In practice this step often requires a major rewrite of image processing pipelines set up for basic research, creating a major barrier to clinical translation of new methods.

Quantitative imaging is another area where the information discarded by conversion to lossy formats is important. Quantification involves the application of complex signal models to multi-dimensional imaging data that are acquired by varying contrast parameters such as (in MRI) echo times, b-values, gradient directions, inversion times, flip angle etc. Often many of these are varied at the same time, and not necessarily in some clean incremental order - as in MR fingerprinting. The models that interpret these data need access to this information, and often also need to encode it alongside the images that are produced. When images are first converted from DICOM into some lossy data format, this is often not possible because the application was not foreseen when the lossy format was first defined. This then requires ad-hoc solutions retaining part of the original DICOM information in unstructured free text fields or separate newly defined header files.

All these problems can be solved, for current and any imaginable or unimaginable future applications, by dropping conversions into lossy image formats and simply reading from DICOM and writing to DICOM.

If only DICOM wasn't so scary!!

What is dbdicom?

dbdicom is a programming interface that makes reading and writing DICOM data intuitive for the practicing medical imaging scientist working in Python. We promise you won't even know it's DICOM. In fact the documentation hardly even mentions DICOM at all. It will certainly not mention things like composite information object definitions, application entities, service-object pairs, unique identifiers, etc etc. This is the language of DICOM, and it's confusing in part because the concepts date back to the 1970's and 1980's when the standard was developed. But then again, that is exactly what you would expect from a successful standard. It doesn't change. It shouldn't change.

dbdicom wraps around DICOM using a language and code structure that is native to the 2020's. It allows you to develop your medical imaging methods using DICOM files only, which means your prototypes of new analysis methods can be deployed in clinical trials just like that. It also means that any result you generate can easily be integrated in open access DICOM databases and can be visualised along with any other images of the same subject by anyone with a DICOM viewer (i.e. literally anyone).

Since dbdicom is primarily a development tool, it can be used from command line or to write stand-alone scripts. However, since dbdicom is all about facilitating translation into clinical trials and ultimately clinical practice, all scripts written in dbdicom are set up for deployment in a graphical user interface. Convenience classes are provided for user interaction that print to a terminal when used in a script, but will automatically generate pop-up windows or progress bars when the same script is deployed inside a dbdicom compatible graphical user interface.

Acknowledgements

dbdicom relies heavily on pydicom for basic read/write of DICOM files, with some additional features provided by nibabel and dcm4che. Graphical user interface compatibility is provided by PyQt5. Documentation is generated by pdoc3. Basic image manipulation is provided by numpy and scipi, and sorting and tabulating of data by pandas. Export to other formats is provided by matplotlib.