UK Renal Imaging Network (UKRIN): Enabling clinical translation of functional MRI for kidney disease

Lead Research Organisation: University of Nottingham
Department Name: Sch of Physics & Astronomy

Abstract

The global burden of kidney disease is significant; 10% of the world's population have chronic kidney disease (CKD). Causes of CKD include acute kidney Injury (AKI), diabetes and high blood pressure. Other risk factors include cardiovascular disease and obesity. CKD confers a large increase in cardiovascular events, cardiovascular mortality or end-stage kidney disease, with an annual estimated cost in England of >£1.5 billion. Treatment options are limited by a lack of validated methods for patient stratification and assessment of response to therapy. Magnetic Resonance Imaging (MRI) has emerged as a promising new approach for assessing, monitoring and managing kidney disease. However, the potential of MRI kidney biomarkers is underestimated and unexplored in both clinical practice and research. Numerous research studies report small numbers of patients and use different MRI methods, making results from different studies difficult to compare or extrapolate into clinical practice.
We propose to work together to tackle the challenges associated with moving renal MRI to clinical use. This Partnership proposal will capitalise on the UK Renal Imaging Network (UKRIN), an existing network of 13 UK renal MRI sites. Through the Partnership grant we aim to develop a renal MRI platform, share expertise, build capacity, and develop a harmonized approach thus enabling data sharing, and accelerating the clinical potential of renal MRI. This will facilitate larger cohort studies, providing a unique contribution to the understanding and treatment of kidney disease. This is a global 1st initiative bringing together patients, leading scientists and clinical researchers, other multidisciplinary experts, Kidney Research UK and industry scanner manufacturers. The Partnership will consolidate the UK's global pioneering role in this field; enable clinical translation of this novel technology; and continue to accelerate new technological advances. This will serve as a platform for future collaborative research programmes, including multi-site clinical studies.
Our specific objectives for the three-year Partnership grant period are to develop a platform to become a national standard for renal MRI:
1) We will share experience in renal MRI that has built up across UK sites.
2) We will organise a series of renal MRI Symposia and Workshops.
3) We will produce a set of optimised scanning protocols for renal imaging that can be used by all the UK sites. These will include: (i) relatively straightforward structural and functional renal imaging methods that can applied quickly; (ii) more advanced cutting-edge methods that require additional development work; (iii) standardised quality assurance protocols that can be used to maintain a high level of scanner performance at all sites.
4) We will develop a national image sharing and analysis platform to develop the capability to share and aggregate data acquired across UKRIN sites, and to develop analysis pipelines that are optimised for renal MRI data. This will follow the template currently employed by the Dementias Platform UK (DPUK), using the open source XNAT informatics software. We will also facilitate links with the UK Renal Registry.
This proposal will establish a standard of renal MRI measures by creating and validating a platform to form the national guide. These new functional non-invasive renal MRI methods will allow various different renal aspects of each kidney to be assessed independently, with results reflecting instantaneous kidney function, rather than the metabolic consequences over time. Imaging the kidney using MRI methods has the potential to improve the management of kidney patients through better diagnosis, better assessment of prognosis and the effect of therapy, and accelerating new drug discovery. The Partnership grant will address key areas of governance, patient engagement, business development and exploitation all supported by an integrated communications plan.

Technical Summary

We propose an MRC Partnership to build on the UK's pioneering role in the development and standardisation of renal MRI. This will bring together MR physicists, radiologists, and nephrologists from major UK renal MRI research centres to create a platform for sharing expertise, building capacity, resolving challenges in data acquisition and analysis, and providing support for data sharing. Once established, this will provide technical support and shared databases of normative data to enable clinical validation through multi-centre studies.
The Partnership Grant will allow us to:
1) Build capacity and enhance the UK's expertise in renal MRI, sharing expertise across the three main MR vendor platforms, and delivering a programme of network activities including Workshops and Symposia.
2) Produce a series of optimised and harmonized protocols for renal MRI comprising: (i) "standard" sequences (BOLD R2*, diffusion-weighted imaging, T1 and T2-weighted images, angiography and phase contrast MRI, T1 mapping); (ii) more advanced protocols (such as arterial spin labelling and magnetic resonance elastography); (iii) quality control protocols.
3a) Set up a Data Analysis Centre (DAC) to develop and validate a software framework for processing multi-parametric renal MRI data. This will support future clinical studies using renal MRI in study design and setup, and image quality assurance and processing.
3b) Develop a centralised platform for data sharing and analysis using the framework of the Dementias Platform UK (DPUK) national XNAT-based informatics platform.
4) Demonstrate the Partnership capability by performing a "travelling kidney" study and collecting a normative data set of subjects, providing data for testing within- and between-site reproducibility across the three MR vendors.
5) Ensure that the UK MR research community can exploit investment in renal MRI and provide sustainability through the establishment of the UKRIN Strategy Board.

Planned Impact

The MRC has recently invested heavily in MR equipment through the Clinical Research Infrastructure Initiative (CRII) and the Advanced Imaging Centre (AIC) in Leeds, and analysis hubs such as the Leeds MRC Medical Bioinformatics Centre, and the MRC's Dementia Platform UK (DPUK) Imaging Informatics initiative. This Partnership grant will extend the capabilities of these infrastructures in renal MRI, building on the UK's lead in this area to form a sustainable platform for future work. This will impact positively on the UK's medical technology, through working with the MR vendors, pharmaceutical sectors, ultimately leading to benefits for health and well-being.

This Partnership will benefit the following groups:

Project Staff: It will provide training/experience in renal MRI across MR vendor platforms. This will increase employability of the PDRAs, and increase the profile of UK research.

Renal medicine: Through workshops and Symposia we will share expertise across the UKRIN sites, the wider renal community, and specialists in other disciplines such as histopathology and cardiology. This will highlight the potential of renal MRI to clinicians who currently have no expertise in this area. UKRIN collaborates with the UKKRC's Clinical Study Groups (CSGs), ensuring accessibility across the nephrology sub-specialities. Clinicians are members of the Partnership Steering Committee to ensure the needs of the clinical community are met. Results of this Partnership will be published widely on the dedicated website and through specialist and general interest journals.

General Public: We are committed to public engagement as evidenced by the involvement in a wide range of outreach activities by the UKRIN sites. KRUK have many years of experience communicating science to lay groups including patients and patient engagement teams, carers and the general public, and through KRUK's integral role in the Partnership we will widely disseminate our findings. We will disseminate to the general public through a website hosted by KRUK, and via social media (Twitter/Facebook). Patients are also involved as key partners in the development and planning for future research that build on the outputs from this Partnership. KRUK also has an active public affairs programme engaging with parliamentarians and other key healthcare policy influencers.

Diagnostics and pharmaceutical industry: An important outcome of the Partnership is the establishment of a platform providing image acquisition and analysis support for future large, cross-site, clinical trials which will have significant impact upon human health and the pharmaceutical industry. Multi-site studies involving large cohorts are recognized as being essential if meaningful inferences are to be made at a population level. However there are significant barriers to the successful implementation of such studies. By focusing on delivering a portfolio of common protocols, coupled with the tools for data sharing and management, we will produce the infrastructure needed to enable such large scale studies to succeed.

NHS and patients: The Partnership will lead to longer-term potential benefits on patient outcome and sustainability of health care. Improved risk stratification will facilitate targeting of interventions to patients at risk, thereby reducing the proportion requiring renal replacement therapy, the most expensive component of CKD treatment. In addition, stratification of low risk patients will spare them the burden and cost of unnecessary treatment. Improved understanding of pathophysiological changes will generate insights which may lead to new treatments which benefit patients in the long term. Forming partnerships with pharmaceutical industries will help in the evaluation and development of new drugs for kidney disease, potentially allowing drugs to be evaluated in fewer participants and over shorter observation periods, resulting in substantial cost savings.

Publications

10 25 50
 
Description Application of functional MRI to improve assessment of chronic kidney disease (AFiRM study)
Amount £1,954,960 (GBP)
Funding ID NIHR128494 
Organisation National Institute for Health Research 
Sector Public
Country United Kingdom
Start 08/2020 
End 08/2027
 
Title Additional file 1 of Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol 
Description Additional file 1: 1.1 MRI biomarkers. File type: PDF file. Title: List of primary MRI biomarkers. Description: A table listing the biomarkers that will be derived from the MRI data to address the primary objectives. 1.2 MRI acquisition protocol. PDF file. MRI acquisition protocol (reference scanner). MRI sequence parameters for the iBEAt protocol on the reference scanner (Siemens 3 T). 1.3 Renal ultrasound SOP. PDF file. Ultrasound Standard Operating Procedures. Standard operating procedures for Ultrasound scanning in iBEAt. 
Type Of Material Database/Collection of data 
Year Produced 2020 
Provided To Others? Yes  
URL https://springernature.figshare.com/articles/Additional_file_1_of_Prognostic_imaging_biomarkers_for_...
 
Title Additional file 1 of Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol 
Description Additional file 1: 1.1 MRI biomarkers. File type: PDF file. Title: List of primary MRI biomarkers. Description: A table listing the biomarkers that will be derived from the MRI data to address the primary objectives. 1.2 MRI acquisition protocol. PDF file. MRI acquisition protocol (reference scanner). MRI sequence parameters for the iBEAt protocol on the reference scanner (Siemens 3 T). 1.3 Renal ultrasound SOP. PDF file. Ultrasound Standard Operating Procedures. Standard operating procedures for Ultrasound scanning in iBEAt. 
Type Of Material Database/Collection of data 
Year Produced 2020 
Provided To Others? Yes  
URL https://springernature.figshare.com/articles/Additional_file_1_of_Prognostic_imaging_biomarkers_for_...
 
Title Additional file 2 of Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol 
Description Additional file 2: 2.1 Biofluid collection SOPs. PDF file. Biofluid collection protocol. The protocol for the collection of blood and urine samples within iBEAt. 2.2 SOPs Biofluid processing. PDF file. Biofluid processing protocol. The protocol for processing blood and urine samples within iBEAt. 2.3 Biofluid schematics. PDF file. iBEAt kit contents and biofluid processing schematics. Schematics of iBEAt collection kits, and processing and storage protocols for collected blood and urine samples within iBEAt. 
Type Of Material Database/Collection of data 
Year Produced 2020 
Provided To Others? Yes  
URL https://springernature.figshare.com/articles/Additional_file_2_of_Prognostic_imaging_biomarkers_for_...
 
Title Additional file 2 of Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol 
Description Additional file 2: 2.1 Biofluid collection SOPs. PDF file. Biofluid collection protocol. The protocol for the collection of blood and urine samples within iBEAt. 2.2 SOPs Biofluid processing. PDF file. Biofluid processing protocol. The protocol for processing blood and urine samples within iBEAt. 2.3 Biofluid schematics. PDF file. iBEAt kit contents and biofluid processing schematics. Schematics of iBEAt collection kits, and processing and storage protocols for collected blood and urine samples within iBEAt. 
Type Of Material Database/Collection of data 
Year Produced 2020 
Provided To Others? Yes  
URL https://springernature.figshare.com/articles/Additional_file_2_of_Prognostic_imaging_biomarkers_for_...
 
Title Additional file 3 of Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol 
Description Additional file 3: 3.0 CRF Screening. PDF file. Study recruitment - prescreening / screening. Clinical record form for prescreening / screening data. 3.1 CRF Adherence Checklist. PDF file. Baseline visit (V1) - adherence checklist. Clinical record form documenting participant adherence to guidance for the baseline visit. 3.2 CRF Limited Clinical Exam. PDF file. Limited Clinical Exam. Clinical record form for clinical examination data including, for example, blood pressure, height and weight. 3.3 CRF Medical and Family Hx. PDF file. Baseline (V1) - Medical and family history V2. Clinical record form for medical and family history (version 2). 3.4 CRF Local Study Labs. PDF file. Baseline (V1) - local study labs. Clinical record form for laboratory measurements performed at recruiting centre. 3.5 CRF Routine Labs. PDF file. Baseline visit (V1) - labs. Clinical record form for documenting all available laboratory values in the year prior to the baseline visit. 3.6 CRF Medications. PDF file. Medication log. Clinical record form documenting all current medications. 3.7 CRF Ultrasound. PDF file. Baseline visit (V1) - Ultrasound. Clinical record form for the renal ultrasound measurements. 3.8 CRF Biosamples. PDF file. Study biosamples. Clinical record form / checklist documenting what biofluid samples were collected and processed for the iBEAt study. 
Type Of Material Database/Collection of data 
Year Produced 2020 
Provided To Others? Yes  
URL https://springernature.figshare.com/articles/Additional_file_3_of_Prognostic_imaging_biomarkers_for_...
 
Title Additional file 3 of Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol 
Description Additional file 3: 3.0 CRF Screening. PDF file. Study recruitment - prescreening / screening. Clinical record form for prescreening / screening data. 3.1 CRF Adherence Checklist. PDF file. Baseline visit (V1) - adherence checklist. Clinical record form documenting participant adherence to guidance for the baseline visit. 3.2 CRF Limited Clinical Exam. PDF file. Limited Clinical Exam. Clinical record form for clinical examination data including, for example, blood pressure, height and weight. 3.3 CRF Medical and Family Hx. PDF file. Baseline (V1) - Medical and family history V2. Clinical record form for medical and family history (version 2). 3.4 CRF Local Study Labs. PDF file. Baseline (V1) - local study labs. Clinical record form for laboratory measurements performed at recruiting centre. 3.5 CRF Routine Labs. PDF file. Baseline visit (V1) - labs. Clinical record form for documenting all available laboratory values in the year prior to the baseline visit. 3.6 CRF Medications. PDF file. Medication log. Clinical record form documenting all current medications. 3.7 CRF Ultrasound. PDF file. Baseline visit (V1) - Ultrasound. Clinical record form for the renal ultrasound measurements. 3.8 CRF Biosamples. PDF file. Study biosamples. Clinical record form / checklist documenting what biofluid samples were collected and processed for the iBEAt study. 
Type Of Material Database/Collection of data 
Year Produced 2020 
Provided To Others? Yes  
URL https://springernature.figshare.com/articles/Additional_file_3_of_Prognostic_imaging_biomarkers_for_...
 
Title Additional file 4 of Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol 
Description Additional file 4: 4.0 Biopsy SOP. PDF file. Biopsy and pathology SOPs. Protocol for storing and capturing meta-data regarding the renal biopsy tissue for the iBEAt study. (EXE 543 kb) 
Type Of Material Database/Collection of data 
Year Produced 2020 
Provided To Others? Yes  
URL https://springernature.figshare.com/articles/Additional_file_4_of_Prognostic_imaging_biomarkers_for_...
 
Title Additional file 4 of Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol 
Description Additional file 4: 4.0 Biopsy SOP. PDF file. Biopsy and pathology SOPs. Protocol for storing and capturing meta-data regarding the renal biopsy tissue for the iBEAt study. (EXE 543 kb) 
Type Of Material Database/Collection of data 
Year Produced 2020 
Provided To Others? Yes  
URL https://springernature.figshare.com/articles/Additional_file_4_of_Prognostic_imaging_biomarkers_for_...
 
Title T2-weighted Kidney MRI Segmentation 
Description A dataset containing 100 T2-weighted abdominal MRI scans and manually defined kidney masks. This MRI sequence is designed to optimise contrast between the kidneys and surrounding tissue to increase the accuracy of segmentation. Half of the acquisitions were acquired of healthy control subjects while the other half were acquired from Chronic Kidney Disease (CKD) patients. Ten of the subjects were scanned five times in the same session to enable assessment of the precision of Total Kidney Volume (TKV) measurements. More information about each subject can be found in the included csv file. This dataset was used to train a Convolutional Neural Network (CNN) to automatically segment the kidneys. For more information about the dataset please refer to this article. For an executable that allows automated segmentation of the kidneys from this dataset please refer to this software. 
Type Of Material Database/Collection of data 
Year Produced 2021 
Provided To Others? Yes  
URL https://zenodo.org/record/5153567
 
Title T2-weighted Kidney MRI Segmentation Dataset. 
Description A data set comprising T2-weighted kidney images and associated masks 
Type Of Material Database/Collection of data 
Year Produced 2021 
Provided To Others? Yes  
Impact This dataset is being shared with other renal imaging groups, such as the RESPECT consortium. 
URL https://doi.org/10.5281/zenodo.4068851.
 
Description Collaboration with Gold Standard phantoms 
Organisation Gold Standard Phantoms Limited
Country United Kingdom 
Sector Private 
PI Contribution Running comparison data sets using the QASPER phantom
Collaborator Contribution Sharing results of using the QASPER phantom
Impact No formal outcomes to date - but data sets collected
Start Year 2020
 
Description Collaboration with the RESPECT consortium (comprising four University sites in EU with lead site in University of Bergamo) 
Organisation University of Bergamo
Country Italy 
Sector Academic/University 
PI Contribution Sharing of imaging protocols and datasets
Collaborator Contribution Input to future data sharing of harmonised data sets
Impact Sharing of data sets and protocols. Output publications
Start Year 2021
 
Description Quality in Organ Donation (QUOD) 
Organisation University of Oxford
Department Quality in Organ Donation
Country United Kingdom 
Sector Academic/University 
PI Contribution Development of protocols for ex-vivo imaging
Collaborator Contribution Access to discarded organs for ex-vivo imaging
Impact Compiling grant application
Start Year 2019
 
Title Modelling software for MRE data 
Description A finite element model of the human torso was created using anatomical MR imaging data of one of the healthy volunteers. This model included left and right kidney, bone (spine and ribs), fat, and other soft tissue. Both kidneys were modelled as uniform tissue with values estimated from literature. MRE was simulated at 60 and 90 Hz by delivering a 30-µm harmonic displacement in the anterior-posterior direction 
Type Of Technology New/Improved Technique/Technology 
Year Produced 2021 
Impact Design of kidney MRE in-vivo experiments 
 
Title Renal Segmentor 
Description Machine learning segmentor to compute total kideny volume 
Type Of Technology Software 
Year Produced 2021 
Impact Data being applied to a number of multicentre studies including CMOR and AFIRM studies 
URL https://www.nottingham.ac.uk/research/groups/spmic/research/renal-mri-group/advanced-imaging-analysi...
 
Title UKRIN Kidney Analysis Toolbox. UKRIN-MAP 
Description Software developed for renal MRI analysis and made available as open source code 
Type Of Technology Software 
Year Produced 2020 
Impact Analysis of data across 3 main MRI vendors - Philips, Siemens and GE. Software shared across renal community. 
 
Title WEZEL 
Description Python toolbox for renal imaging applications. 
Type Of Technology Software 
Year Produced 2020 
Open Source License? Yes  
Impact Application to AFIRM renal imaging datasets 
URL https://github.com/QIB-Sheffield/wezel
 
Title dbdicom 
Description Full Changelog: https://github.com/QIB-Sheffield/dbdicom/commits/v0.0.5 dbdicom dbdicom is a Python interface for reading and writing DICOM databases. 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', ) 
TO DO In addition to filtering, the results can also be sorted by attribute:
studies = folder.studies( sortby = 'StudyDate', PatientName = 'John Dory', ) 
In this case the resulting studies will appear in the list in order of Study Date. Sorting can also be done based on two or more attributes:
studies = folder.studies( sortby = ['PatientName', 'StudyDate', 'StudyDescription'] ) 
In this case the result will be a 3-dimensional list. For instance to access all studies of patient 3 do:
studies[3][:][:] 
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 
Type Of Technology Software 
Year Produced 2022 
Open Source License? Yes  
Impact Use with DICOM renal imaging datasets 
URL https://zenodo.org/record/6424665
 
Description 3rd International Symposium on Functional Renal Imaging - organised by UK Renal Imagin Network 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact he symposium will be held from 15th -17th October at the East Midlands Conference Centre, University of Nottingham, UK.

The symposium brought together scientists and clinicians from several disciplines, in a unique attempt to shape the future of renal functional imaging. It provided an overview of cutting edge clinical and pre-clinical renal imaging techniques, and explored the clinical relevance of renal imaging, the future directions of renal functional MR, and the harmonization of these approaches with clinical applications.

It provided a platform for engagement with colleagues and peers, and fostered the development of local, national and international collaborations to explore multi-disciplinary imaging approaches. The symposium attracted basic scientists, clinical scientists and clinicians from physiology, nephrology, radiology, internal medicine and related fields, as well as experts in imaging sciences and physics from all levels, ranging from students to advanced users and international experts.
Year(s) Of Engagement Activity 2019
URL https://www.nottingham.ac.uk/research/groups/spmic/research/uk-renal-imaging-network/3rd-renal-sympo...
 
Description Applications of Machine Learning in Renal MRI, Renal MRI Study Group Virtual Meeting 
Form Of Engagement Activity A formal working group, expert panel or dialogue
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact Outline of renal machine learning methods presented to other researchers in renal MRI
Year(s) Of Engagement Activity 2022
 
Description Cardiovascular, metabolic and kidney disease: crosscutting science and best practice 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Professional Practitioners
Results and Impact A 1-day workshop on 'Multimorbidity and clustering of common diseases are major problems for health service providers'. This 1-day conference aimed to stimulate discussion and ideas and help set the foundation for establishing strategic partnerships to ultimately improve the health of patients with multimorbidity.
Year(s) Of Engagement Activity 2019
URL https://www.rcplondon.ac.uk/events/cardiovascular-metabolic-and-kidney-disease-crosscutting-science-...
 
Description Frontiers masterclass for the undergrads Oct 2021: Renal MRI at Nottingham: Physics, Techniques, and Clinical Applications. 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Undergraduate students
Results and Impact Outline of renal research to undergraduates
Year(s) Of Engagement Activity 2021
 
Description Meeting with MR Vendors 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Industry/Business
Results and Impact Meeting with MRI vendors, charity KRUK and patient representatives
Year(s) Of Engagement Activity 2019
 
Description Multi-parametric MRI in Renal Disease - British Association of MR Radiographers (http://www.bamrr.org/home) Professor Sue Francis 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Other audiences
Results and Impact Talk to the British Association of MR Radiographers on renal MR imaging, and highlight of UKRIN grant aims and future directions
Year(s) Of Engagement Activity 2018
 
Description Multiparametric MRI in Renal Disease' - Talk at the IPEM Advanced Approaches to Body MRI' on Tuesday 26th February in Liverpool - Professor Sue Francis 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Other audiences
Results and Impact Talk to the Institute of Physics and Engineering in Medicine on renal MRI
Year(s) Of Engagement Activity 2019
 
Description Press release on UKRIN maps 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Other audiences
Results and Impact Press release on UKRIN maps partnership grant: https://www.nottingham.ac.uk/news/pressreleases/2018/november/imaging-project-launched-to-transform-treatment-of-kidney-disease.aspx
Year(s) Of Engagement Activity 2018
 
Description QUOD visit to UKRIN 
Form Of Engagement Activity A formal working group, expert panel or dialogue
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Third sector organisations
Results and Impact Meeting with QUOD (The Quality in Organ Donation) to discuss wider use of imaging in organs
Year(s) Of Engagement Activity 2019
 
Description Renal imaging glomcom - MRI to assess renal tissue oxygenation and function : A bold story - panellist 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact Discussion of BOLD imaging
Year(s) Of Engagement Activity 2020
 
Description Talk - The UK Renal Imaging Network and UK partnership grant - 2018 plenary meeting of the PARENCHIMA project in Prague - Prof Sue Francis 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Other audiences
Results and Impact Talk onThe UK Renal Imaging Network and UK partnership grant at the 2018 plenary meeting of the PARENCHIMA project in Prague - Prof Sue Francis
Year(s) Of Engagement Activity 2018
 
Description Talk UK Kidney Week 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact This was part of a session on 'Working together to understand fibrosis and regeneration'.
Assessing the efficacy and safety of cell therapies in preclinical models of kidney -Bettina Wilm
Working together to bridge the gap to translation - Dr Claire Sharpe, Reader in Renal Medicine, King's College London/King's College Hospital
The potential of whole organ MRI and non-invasive assessment of renal fibrosis - Professor Susan Francis, Professor of MR Physics, University of Nottingham
The patient perspective
This sparked discussion in terms of using imaging to assess fibrosis - both with clinicians and with pharma industry
Year(s) Of Engagement Activity 2019
URL http://ukkw.org.uk/wp-content/uploads/2019/04/Programme-UKKW-2019.pdf
 
Description Talk to British Chapter of ISMRM - Shefiield September 2019 - Advances in reanl MRI 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Professional Practitioners
Results and Impact Talk on 'Advances in Renal MRI' to promote work of UKRIN-MAPS to the MR community
Year(s) Of Engagement Activity 2019
URL https://www.bc-ismrm2019.org.uk/programme
 
Description The Renal MRI Group A Whistle-Stop Tour. 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Postgraduate students
Results and Impact Postgraduate and academic staff at UoN, sparked questions and discussions and increased interest within the university in renal imaging
Year(s) Of Engagement Activity 2020
 
Description The UKRIN-MAPS Harmonised Renal Multiparametric MRI Protocol presented at ISMRM Workshop on Kidney MRI (Lisbon) 
Form Of Engagement Activity A formal working group, expert panel or dialogue
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact Promoted renal protocols of UKRIN project
Year(s) Of Engagement Activity 2021
 
Description UK Renal Imaging Network at QUOD Symposium 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Professional Practitioners
Results and Impact Presentation at the Quality in Organ Donation Symposium on UK Renal Imaging Network
Year(s) Of Engagement Activity 2019
 
Description UKRIN Meeting Sheffield 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Professional Practitioners
Results and Impact Meeting or project partners and others interested in renal MRI in UK. Outline of results to-date of UKRIN_MAPS
Year(s) Of Engagement Activity 2020
 
Description Website promoting UKRIN Partnership grant 
Form Of Engagement Activity Engagement focused website, blog or social media channel
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Other audiences
Results and Impact Website to disseminate UKRIN Partnership outcomes: https://www.nottingham.ac.uk/research/groups/spmic/research/uk-renal-imaging-network/ukrin-maps.aspx
Year(s) Of Engagement Activity 2018,2019