Data Format Standardization and DICOM Integration for

Data Format Standardization and

DICOM Integration for Hyperpolarized

C MRI

Ernesto Diaz

, Renuka Sriram

, Jeremy W. Gordon

, Avantika Sinha

, Xiaoxi Liu

, Sule Sahin

Jason Crane

, Marram P Olson

, Hsin-Yu Chen

, Jenna Bernard

, Daniel B. Vigneron

1,2

, Zhen

Jane Wang

, Duan Xu

1,2

, Peder E. Z. Larson

1,2

Affiliations:

Department of Radiology and Biomedical Imaging, University of California – San Francisco,

San Francisco, California, USA

UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, Berkeley and

University of California, San Francisco, California, USA

Corresponding author address:

pede[email protected], 1700 4

Street, Byers Hall Room 102C, San Francisco, CA 94143

Submitted to the Journal of Imaging Informatics in Medicine as a Technical Note

Keywords: Hyperpolarized

C MRI, metabolic imaging, DICOM format, Experiment metadata

Statements and Declarations

Funding

This work was supported by NIH grants P41EB013598, U24CA253377, R01CA262630, and

R01CA249909. Authors PEZL, DBV, and JWG received research support from GE Healthcare.

Competing Interests

Financial interests: Authors ED, RS, AS, XL, JB, ZJW, and XU declare they have no financial

interests. Authors PEZ, DBV, and JWG received research funding from GE Healthcare

Author Contributions

All authors contributed to the study conception and design. DICOM tools were created by

Ernesto Diaz, Jeremy Gordon, Jason Crane, Marram P Olson, and Xiaoxi Liu. Data collection

and analysis were performed by Ernesto Diaz. Creation of the proposed parameters was

performed by Peder Larson. The first draft of the manuscript was written by Ernesto Diaz and

Peder Larson, and all authors commented on previous versions of the manuscript. All authors

read and approved the final manuscript.

Ethics approval

This manuscript presents technical methods but does not include any in vivo study results.

Consent to participate

Not applicable

Consent to publish

Not applicable

Abstract

Hyperpolarized (HP)

C MRI has shown promise as a valuable modality for in vivo

measurements of metabolism and is currently in human trials at 15 research sites worldwide.

With this growth it is important to adopt standardized data storage practices as it will allow sites

to meaningfully compare data.

In this paper we (1) describe data that we believe should be stored and (2) demonstrate

pipelines and methods that utilize the Digital Imaging and Communications in Medicine

(DICOM) standard. This includes proposing a set of minimum set of information that is specific

to HP

C MRI studies. We then show where the majority of these can be fit into existing DICOM

Attributes, primarily via the “Contrast/Bolus” module.

We also demonstrate pipelines for utilizing DICOM for HP

C MRI. DICOM is the most common

standard for clinical medical image storage and provides the flexibility to accommodate the

unique aspects of HP

C MRI, including the HP agent information but also spectroscopic and

metabolite dimensions. The pipelines shown include creating DICOM objects for studies on

human and animal imaging systems with various pulse sequences. We also show a python-

based method to efficiently modify DICOM objects to incorporate the unique HP

C MRI

information that is not captured by existing pipelines. Moreover, we propose best practices for

C MRI data storage that will support future multi-site trials, research studies and technical

developments of this imaging technique.

Introduction

Hyperpolarized (HP)

C MRI has shown promise as a valuable modality particularly for in vivo

measurements of metabolism

, and is currently in human trials at 15 research sites worldwide

with over 75 human research publications

. It is based on intravenous injection of a

hyperpolarized contrast agent that has been enriched with

C. The most widely used agent is

C-pyruvate for measuring metabolism, but there are also a range of other promising agents

undergoing clinical translation including

C-urea (perfusion)

C-alpha-ketoglutarate

(metabolism)

, and

C-fumarate (necrosis)

. To date, human studies have been performed to

characterize normal metabolism, and clinical studies have been performed to study in altered

metabolism in prostate cancer, brain tumors, kidney tumors, pancreatic cancer, metastatic

disease, liver disease, and heart disease

As HP

C MRI continues to grow, it is important to adopt standardized data storage practices,

allowing sites to compare data. The Digital Imaging and Communications in Medicine (DICOM)

format is an attractive standard format as it is commonly used in digital imaging, medical

imaging, and communications. The DICOM file type consists of the image and the metadata of

the image packed into a single file. The information in the metadata is organized as a constant

and is standardized by a series of data elements. By extracting the data elements, we can

access important information regarding the patient demographics and study parameters that are

crucial for study interpretation. The DICOM standard is actively supported and updated. It also

supports the interchange of information between computer systems such as picture archiving

and communication system (PACS).

The goal of this work is to describe an approach for HP

C MRI data standardization utilizing

the DICOM format. We first describe the data requirements, focusing on the unique aspects of

C MRI. We then describe current processing pipelines that support the creation of DICOM

objects from multiple vendors and pulse sequences. Finally, we demonstrate how this

information can be incorporated into DICOM format.

Hyperpolarized

C MRI Data

The data requirements for HP

C MRI are outlined briefly below. This includes performing

imaging to spatially resolve signals, encoding of multiple metabolites to measure metabolic

conversion, performing dynamic measurements to capture the rapid kinetics, and characteristics

of the HP agent, all of which that will affect the results and analysis.

Imaging

C experiments can be spatially resolved using MRI techniques, providing important

localization to visualize metabolic activity within the body. Therefore, a format to store multi-

dimensional data needs to be supported.

Metabolite encoding

C MRI has the unique ability to encode signals from multiple metabolites, distinguishing

the injected agent, or substrate, from resulting metabolic products that are converted in vivo.

The most common combination of metabolites is [1-

C]pyruvate (substrate), [1-

C]lactate

(product), [1-

C]alanine (product), and

C-bicarbonate (product). Thus, the data should support

a metabolite encoding dimension. Metabolite encoding in the acquisition is typically done with

MR spectroscopy methods, chemical shift encoding (e.g. IDEAL) methods, or metabolite-

specific imaging

. The data must support metabolite encoding through identification of the

individual compounds and/or the resonant frequencies, or with a spectroscopy dimension.

Dynamic Measurements

In a HP

C MRI experiment, rapid metabolite kinetics following injection, including perfusion

and metabolic conversion, that are typically captured with time-resolved imaging. Thus, the data

should support dynamic measurements and record timings of the measurements.

C Agents

To analyze HP

C data, it is important to know the characteristics of the HP

C agent, how it

was prepared, and how it was used. Table 1 lists such relevant HP

C agent information. Thus,

the data should support capturing of this information.

C Agent Information

Purpose/Description

C agent(s) injected

Describe the composition of the injected agents. Note

that this can include simultaneous injection of multiple

agents

Administration route of agent

How was the HP solution delivered

Total Injected Volume

How much HP solution was injected in total

Concentration of each HP agent

Concentration of each HP agent in HP solution

Start Time of injection

Describe the timing of the start of injection, important to

know relative to the data acquisition timing

Total injection duration

Over how long was the HP solution injected

Polarization

What was the measured polarization of the HP agent(s).

Typically back-calculated to the time of dissolution.

Polarization Measurement Timing

timing of the polarization measurement relative to the data

acquisition

Agent Relaxation Rates (e.g. T1)

Expected or measured relaxation rates used to calculate

polarization

Dissolution Timing

Timing of the dissolution of the HP agent, most important

to know relative to the data acquisition

Table 1:Hyperpolarized

C agent information that we typically keep track of during our studies.

DICOM Processing Pipelines

Our current processing pipelines support DICOM creation from multiple vendors and pulse

sequences, listed in Table 2. The spectroscopy sequences use an open-source standards-

based software framework(SIVIC) for DICOM creation which includes support for DICOM

Spectroscopy

(7,8).

Table 2: Currently supported sequences with DICOM creation pipelines in use at our institution

in alphabetical order.

MR Spectroscopy and Spectroscopic Imaging with SIVIC

MR spectroscopy and spectroscopic imaging (MRS/I) for HP

C MRI includes data sampling at

various time delays to resolve a spectrum and separate

C-labeled compounds. For processing

of MRS/I data, we utilize the libraries in SIVIC for data processing and DICOM creation,

including support of the DICOM MRS standard

. This software can reads in multiple vendor

MRS raw data files (Bruker, GE, Philips, Siemens, Varian), and it for HP

C MRI studies on GE

Healthcare and Bruker MRI scanners.

1. Reads in raw data

2. Performs MR spectroscopy or MR spectroscopic imaging reconstructions

Scanner Vendor

Sequences

Raw Data

Tools

Output

Bruker

Slab-selective

MRS, chemical

shift imaging

(CSI) and Echo-

planar

spectroscopic

imaging

MRS Raw

File or Dad

File

SIVIC

MATLAB

Spectroscopy

DICOMs

Bruker

Metabolite-

Specific

Imaging

FID

Bruker Paravision

MATLAB

Metabolite

image, AUC,

kinetic rate

DICOMs

GE Healthcare

Slab-selective

MRS, chemical

shift imaging

(CSI) and Echo-

planar

spectroscopic

imaging

Pfile or

ScanArchive

SIVIC

Spectroscopy

DICOMs

GE Healthcare

Metabolite-

specific EPI

Pfile or

ScanArchive

GE Orchestra

Toolbox

MATLAB

Metabolite

images, AUC

ratio and kinetic

rate DICOMs

RTHawk Research

(Vista.ai)

Metabolite-

specific spiral

and bSSFP

RTHawk raw

data

Real-time

reconstructed

DICOM

MATLAB to improve

data

Metabolite

image DICOMs

3. Writes to DICOM MRS file

4. Optional – extract peak amplitudes of areas from spectra and create individual

metabolite DICOM files

5. Optional - write raw data or partially processed data to DICOM

6. Optional - secondary capture of MRS and anatomical visualization to DICOM

GE Metabolite-specific Imaging

Metabolite-specific imaging for HP

C MRI uses a spectral-spatial RF pulse to selectively excite

a single metabolite

and is followed by fast imaging readout such as echo-planar imaging (EPI)

or spirals. We have implemented EPI-based metabolite-specific imaging for GE Healthcare MRI

scanners along with a reconstruction pipeline based on the GE Orchestra reconstruction

toolbox

. This leverages the EPI processing and DICOM creation functionality that is utilized by

GE product pulse sequences.

1. Reads in raw data (Pfile or ScanArchive)

2. Perform EPI reconstruction including Nyquist ghost correction

3. Perform HP-specific coil combination

4. Create area-under-curve (AUC) and kinetic rate maps

5. Write metabolite images and parameter maps to DICOM

Figure 1: Example of how HP

C MRI can be used in a workflow for improved targeting of

biopsies in prostate cancer

. Using DICOM has been critical in the success of this workflow, as

this allows for integration into PACS as well as the fusion biopsy platform, with radiologists and

urologists easily able to review the images and resulting annotations outlining the biopsy

targets. Figure reproduced with permission from

RTHawk Metabolite-specific Imaging

We have implemented metabolite-specific imaging with EPI and spirals, as well as metabolite-

specific balanced steady-state free-precession (bSSFP) sequences

13-15

on the RTHawk

Research platform. This is a vendor-neutral platform that allows for advanced control of the

MRI system including real-time reconstructions and feedback as well as pulse sequence

programming.

1. We utilize real-time reconstruction built into RTHawk during the scanning process,

resulting in individual metabolite DICOM images at each timepoint.(Note: This uses the

sum of squares coil combination technique.)

2. The DICOMs Images are exported when the data is transferred from the RTHawk

workstation to the local network.

3. A second, improved image reconstruction is performed using raw complex-valued data

and a more advanced coil combination method to enhance the data quality

4. To integrate the improved data into DICOM, a MATLAB script is employed that reads the

metadata from the real-time reconstructed DICOMs Images, replaces the original voxel

data with the results of the second, optimized reconstruction, and creates new DICOMs

Images with appropriate new unique identifiers (UIDs).

Bruker Metabolite-specific Imaging

Similar to the GE metabolite-specific imaging sequence, it uses a spectral-spatial RF pulse to

excite a single metabolite

, and is followed by a fast echo-planar imaging (EPI) readout

1. Bruker scanner has its own built-in EPI reconstruction and an option to write

reconstructed metabolite maps to DICOM files.

2. A MATLAB script is used to read in the DICOM files. From that data, we generate AUC

maps and kinetic rate maps, and write back to DICOM files.

3. Optional: Reads in Bruker raw data using a MATLAB script, use a custom reconstruction

script and write to DICOM.

DICOM HP Agent Integration

Many of the HP agent metadata that are desirable for analyses (Table 1) can be integrated into

existing DICOM attributes, particularly within the “Contrast/Bolus” module. However, the above

DICOM pipelines by default do not include support for these metadata. To address this, we built

a tool to modify HP

C DICOM objects based on user input to include this HP agent metadata.

Currently, the HP

C images created by MRI scanners or in-house reconstruction pipelines do

not utilize the Contrast/Bolus Attributes. We seek to leverage the existing Contrast/Bolus

Attributes in DICOM to store information to identify the study and experimental parameters that

are valuable for HP

C data analysis by researchers and clinicians. Table 3 lists the attributes

we are adding to our images.

TAG

Attribute Name

H MRI

Example

Values

C Description

C Example Values

(0018,0010)

Contrast/Bolus Agent

MAGNEVIST

ORAL &

OMNIPAQUE

C agent(s)

injected

HYPERPOLARIZED [1-

13C]PYRUVATE

HYPERPOLARIZED [2-

13C]PYRUVATE

HYPERPOLARIZED [1-

13C]PYRUVATE +

[13C,15N]-UREA

(0018,1040)

Contrast/Bolus Route

Administration route

of contrast agent

(0018,1041)

Contrast/Bolus

Volume

Injected volume

(mL)

(0018,1042)

Contrast/Bolus Start

Time

Define start of

injection relative to

data acquisition (Not

DICOM compliant)

Start Time in

HHMMSS.FFFFFF

(DICOM compliant)

(0018,1047)

Contrast/Bolus Flow

Duration

Total injection

duration [s]

(0018,1048)

Contrast/Bolus

Ingredient

IODINE

BARIUM

GADOLINIUM

CARBON

DIOXIDE

C enriched

compounds

[1-^13^C]PYRUVATE

[2-^13^C]PYRUVATE

[^13^C,^15^N]UREA

[1-^13^C]ALPHA-

KETOGLUTARATE

(0018,1049)

Contrast/Bolus

Ingredient

Concentration

Concentration of

each

C compound

[mg/mL]

(0400,0550)

Modified Attributes

Sequence

Store prior versions

of an attributes that

were removed or

modified

(0400,0562)

Attribute Modification

DateTime

Tracked Date and

Time when

attributes were

modified

(0400,0563)

Modifying System

Describes what

modified the

attributes

SIVIC-

HP_agent_DICOM_tool.py

Table 3: DICOM attributes proposed for addition to HP

C MRI study data. This captures the

majority but not all the desired parameters listed in Table 1.Note that the “Contrast/Bolus

Ingredient” attributes listed can include more than one entry to support multiple simultaneously

polarized HP

C agents

such as pyruvate/urea

3,14

. The proposed

C naming conventions are

in a style that is consistent with the style used for PET in DICOM.

In DICOM, each attribute or data element is identified by a unique tag (TAG), which is

comprised of a group number and an element number. The value representation (VR) is a code

that indicates the data type used to encode the value(s) of the attribute. Additionally, the value

multiplicity (VM) indicates how many values can be present in the attribute. These three

components, work together to provide a comprehensive understanding of the attribute and its

role within the DICOM file, allowing for accurate and efficient manipulation and interpretation of

the medical imaging data.

We reviewed the DICOM standards definition and found the listed attributes in Table 3 can

capture much of the unique HP agent metadata. This includes ingredients, injection timing, and

dosing characteristics. These can all be added in a DICOM compliant fashion.

One important feature captured in this proposal is the inclusion of multiple ingredients, which

can capture dual-agent (“co-polarized”) or multi-agent HP studies

3,14,17

. In these studies, multiple

C-enriched compounds are simultaneously hyperpolarized and simultaneously injected. The

“Contrast/Bolus Agent Sequence”, which can capture use of multiple agents that affect the data

in a DICOM object, was not used since when multiple HP compounds are desired, they are

combined into a single agent with multiple ingredients. The use of multiple agents is not

performed to our knowledge, as it would require rapid dissolution of multiple separate samples

following hyperpolarized to inject both before the signal decays.

We also proposed standardized ingredient names that are consistent with the style used in PET.

In these, the enriched isotope(s) are denoted with “^” around the nucleus, e.g. “^13^C”. We also

use a standard chemical naming convention to identify the site of enrichment within the

molecule.

We used the PyDicom python toolbox to access the data in DICOM files (Figure 2). PyDicom is

a general-purpose DICOM framework whose purpose is to reads and write DICOM attributes. It

can also add, delete, and modify any attributes to DICOM objects.

Figure 2: This is an example of the PyDicom functions used. It reads a file, then adds an

attribute and saves the file.

The basic workflow of the python script created using the packages PyDicom and OS is as

follows: User would run the script in the terminal with a parameter of a directory path for

DICOMS that need to be modified. The prompts would pop up one by one and the user can

write its own inputs or press enter for default values as shown in Figure 3. Once the user has

entered the prompts, it will loop through the folder and add all the attributes to each DICOM file.

We have also aimed to capture the modifications made to the DICOM file when the tool is used.

For this, we used the Modified Attributes Sequence to describe what attributes we modified. For

Modifying System, it shows the name of this tool we used to change the attributes. We used

DateTime within the Modified Attributes Sequence to keep track of the date when we modified

the attributes, and this shows other researchers when we modified them. This maybe a non-

standard use of these attributes.

This tool has been demonstrated on DICOM objects from a metabolite-specific EPI sequence

on a GE 3T scanner, reconstructed with a custom implementation based on the GE Orchestra

package, DICOM from Bruker 2D CSI scan generated by SIVIC, and a DICOM from a GE echo-

planar spectroscopic imaging (EPSI) sequence generated by SIVIC.

The script is available within SIVIC at

https://github.com/SIVICLab/sivic/tree/master/applications/dicom_tools.

Figure 4: The prompts questions that gives option for default values or user input. As well,

showing the attributes that has been modified.

C MRI Data Storage Best Practices

We have also created a proposed set of Data Storage Best Practices. These were based upon

a survey of research groups at our institution, performing both human and preclinical HP

imaging studies at our institution. The proposed set of Data Storage Best Practices aim to

ensure that the stored data completely describes the study as is necessary for subsequent

processing. This should also be done so that future researchers can easily retrieve and analyze

the data without the need to search multiple sources of information.

Storage of essential study data should be done in a centralized location with high-quality back

up, using a single folder per study, including:

• HP spectroscopy/image data (DICOM recommended)

• HP raw data

•

H image data (DICOM recommended)

• Copy of any processing scripts

• Metadata – any other information about the study. This can either be directly noted in the

data folder, or in a separate database/spreadsheet. At a minimum this should include

o Image acquisition data (ideally in DICOM files):

o Flip angle information, especially for variable flip angle or metabolite-specific flip

schedules

o Injection timing characteristics – length of injection, timing of imaging relative to

injection(See Table 1)

o Dose characteristics – volume injected, concentration of agent, subject weight,

polarization.(See Table 1)

• Other Study data - directly recorded in the study folder, or in a separate

database/spreadsheet.

• Study “Key” needed in all data locations to link to other metadata sources.

o Option 1: Unique ID for each study

o Option 2: Unique ID for each subject + Unique ID for each study

Discussion and Unmet Needs

Using the DICOM standard has significant advantages for HP

C MRI data. Given this

technology is being translated into clinical studies, DICOM will allow for seamless integration

into existing image viewing and analysis platforms, ultimately supporting use in clinical

workflows. The use of standardized DICOM objects would also be valuable in the multi-site

clinical trials setting, which are likely to begin within the next several years.

We also believe that the DICOM format will be valuable for the preclinical studies given the

flexibility of the format. A pre-clinical DICOM standard has recently been proposed

, and a

recent assessment preclinical imaging metadata need has also advocated for integration into

DICOM

. While adoption is less consistent in preclinical imaging systems, we have

demonstrated a pipeline for our Bruker preclinical MRI scanner.

While the Contrast/Bolus module in DICOM supported most HP agent parameters we wanted to

save, there were several parameters without an existing attribute. These include the polarization

measurements, T1 relaxation rate, and dissolution timing. These could be stored in private data

elements. This could also justify the need for a HP Agent MRI Module in DICOM. This could

largely follow the examples from PET, which also must capture similar information: polarization

is analogous to specific activity, relaxation rates are analogous to half-life, and dissolution timing

is analogous to dose creation timing. The PET Isotope Module (C.8.9.2) could serve as a good

model.

Several other current features of the DICOM standard may also be relevant for HP MRI. The

Enhanced MR Information Object Definitions (IODs) are attractive because they allow for more

MRI specific data sources such as raw k-space and spectroscopy data and can be used for

“raw” images (e.g. dynamic metabolite images) as well as derived parameter maps (e.g. kinetic

rate maps). The Enhanced Contrast/Bolus Module may also be relevant to store additional HP

agent parameters.

Hyperpolarization is also used in HP

129

Xe MRI, a modality that unique images pulmonary

ventilation, gas exchange, and terminal airway morphology

and received FDA approval in

2021. There are many shared aspects of both HP MRI methods, including the hyperpolarization

of an agent, administration of this agent (inhaled for HP

129

Xe), relatively rapid imaging of the

agent, and measuring agent kinetics. With this in mind, we believe it would be beneficial to

coordinate efforts across the HP modalities when developing DICOM integration methods and

specifications of metadata.

C MRS and MRSI acquisition methods are more challenging to use in a standardized

fashion compared to imaging-based methods (e.g. metabolite-specific imaging). This is because

the adoption of DICOM MRS by vendors is rather limited, leading to the community

development of tools such as SIVIC

. If MRS and MRSI methods continue to show value, it

would be important for researchers to work closely with vendor partners and ideally have the

DICOM MRS standard integrated into the scanners.

Ultimately, we believe it is important that both a standardized and essential set of DICOM

attributes are recorded for HP

C MRI studies. This should be standardized so as not to

introduce confusion, for example due to different naming conventions or use of different DICOM

attributes. This should also include an essential set of attributes to allow for robust and

reproducible analyses of HP

C MRI data. The final definitions are beyond the scope of this

paper, but we think would be best addressed by a consensus of HP

C MRI researchers in

future discussions as the community recently formed the HP 13C Consensus Group

. Following

consensus, any changes to the DICOM standard should then be proposed by drafting a

Correction Proposal (CP) and work with the DICOM MR Working Group (WG 16) to submit it to

the base standards WG (WG 6).

Conclusion

We have proposed a set of attributes to store HP

C MRI metadata, particularly regarding the

HP agent, and implemented via PyDicom a program to add these a set of these attributes in a

DICOM-compliant fashion based on user input. This was shown to work in line with a variety of

DICOM creation pipelines that are used for HP

C data. In addition, we have proposed a

broader set of data storage best practices. Providing further data standardization will advance

this technology by allowing sites to seamlessly share and compare data, including in the context

of multi-site trials, and by using DICOM HP

C MRI can be easily integrated into other clinical

and research workflows.

References

1. Wang ZJ, Ohliger MA, Larson PEZ, et al. Hyperpolarized 13C MRI: State of the Art and

Future Directions. Radiology. Published online March 5, 2019:182391.

doi:10.1148/radiol.2019182391

2. Larson PE, Bernard JM, Bankson JA, et al. Current Methods for Hyperpolarized [1-

13C]pyruvate MRI Human Studies. Published online September 7, 2023.

doi:10.48550/arXiv.2309.04040

3. Qin H, Tang S, Riselli AM, et al. Clinical translation of hyperpolarized 13C pyruvate and urea

MRI for simultaneous metabolic and perfusion imaging. Magnetic Resonance in Medicine.

n/a(n/a). doi:10.1002/mrm.28965

4. Chaumeil MM, Larson PEZ, Yoshihara HAI, et al. Non-invasive in vivo assessment of IDH1

mutational status in glioma. Nat Commun. 2013;4:2429. doi:10.1038/ncomms3429

5. Clatworthy MR, Kettunen MI, Hu DE, et al. Magnetic resonance imaging with hyperpolarized

[1,4-(13)C2]fumarate allows detection of early renal acute tubular necrosis. Proc Natl Acad Sci

U S A. 2012;109(33):13374-13379. doi:10.1073/pnas.1205539109

6. Gordon JW, Chen H, Dwork N, Tang S, Larson PEZ. Fast Imaging for Hyperpolarized MR

Metabolic Imaging. Magnetic Resonance Imaging. 2021;53(3):686-702. doi:10.1002/jmri.27070

7. Crane JC, Olson MP, Nelson SJ. SIVIC: Open-Source, Standards-Based Software for

DICOM MR Spectroscopy Workflows. International Journal of Biomedical Imaging.

2013;2013:e169526. doi:10.1155/2013/169526

8. SIVIC. doi:10.5281/zenodo.4777197

9. Crane JC, Gordon JW, Chen HY, et al. Hyperpolarized 13C MRI data acquisition and

analysis in prostate and brain at University of California, San Francisco. NMR in Biomedicine.

2020;n/a(n/a):e4280. doi:10.1002/nbm.4280

10. Cunningham CH, Chen AP, Lustig M, et al. Pulse sequence for dynamic volumetric imaging

of hyperpolarized metabolic products. J Magn Reson. 2008;193(1):139-146.

11. Chen HY, Bok RA, Cooperberg MR, et al. Improving multiparametric MR-transrectal

ultrasound guided fusion prostate biopsies with hyperpolarized 13 C pyruvate metabolic

imaging: A technical development study. Magn Reson Med. 2022;88(6):2609-2620.

doi:10.1002/mrm.29399

12. Zhu Z, Zhu X, Ohliger MA, et al. Coil combination methods for multi-channel hyperpolarized

13C imaging data from human studies. Journal of Magnetic Resonance. 2019;301:73-79.

doi:10.1016/j.jmr.2019.01.015

13. Tang S, Bok R, Qin H, et al. A metabolite-specific 3D stack-of-spiral bSSFP sequence for

improved lactate imaging in hyperpolarized [1-13C]pyruvate studies on a 3T clinical scanner.

Magnetic Resonance in Medicine. n/a(n/a). doi:10.1002/mrm.28204

14. Liu X, Tang S, Mu C, et al. Development of specialized magnetic resonance acquisition

techniques for human hyperpolarized [13 C,15 N2 ]urea + [1-13 C]pyruvate simultaneous

perfusion and metabolic imaging. Magn Reson Med. 2022;88(3):1039-1054.

doi:10.1002/mrm.29266

15. Liu X, Tang S, Cui D, et al. A metabolite specific 3D stack-of-spirals bSSFP sequence for

improved bicarbonate imaging in hyperpolarized [1-13C]Pyruvate MRI. J Magn Reson.

2023;353:107518. doi:10.1016/j.jmr.2023.107518

16. Sahin SI, Ji X, Agarwal S, et al. Metabolite-Specific Echo Planar Imaging for Preclinical

Studies with Hyperpolarized 13C-Pyruvate MRI. Tomography. 2023;9(2):736-749.

doi:10.3390/tomography9020059

17.Wilson DM, Keshari KR, Larson PEZ, et al. Multi-compound Polarization by DNP Allows

Simultaneous Assessment of Multiple Enzymatic Activities In Vivo. J Magn Reson.

2010;205(1):141-147.

18.Gordon JW, Vigneron DB, Larson PEZ. Development of a symmetric echo planar imaging

framework for clinical translation of rapid dynamic hyperpolarized (13) C imaging. Magn Reson

Med. Published online February 2016. doi:10.1002/mrm.26123

19. Kalen JD, Clunie DA, Liu Y, Tatum JL, Jacobs PM, Kirby J, Freymann JB, Wagner U,

Smith KE, Suloway C, et al. Design and Implementation of the Pre-Clinical DICOM Standard in

Multi-Cohort Murine Studies. Tomography. 2021; 7(1):1-9.

https://doi.org/10.3390/tomography7010001

20. Moore SM, Quirk JD, Lassiter AW, Laforest R, Ayers GD, Badea CT, Fedorov AY, Kinahan

PE, Holbrook M, Larson PEZ, et al. Co-Clinical Imaging Metadata Information (CIMI) for Cancer

Research to Promote Open Science, Standardization, and Reproducibility in Preclinical

Imaging. Tomography. 2023; 9(3):995-1009. https://doi.org/10.3390/tomography9030081

21. Niedbalski PJ, Hall CS, Castro M, et al. Protocols for multi-site trials using hyperpolarized

129Xe MRI for imaging of ventilation, alveolar-airspace size, and gas exchange: A position

paper from the 129Xe MRI clinical trials consortium. Magn Reson Med. 2021; 86: 2966–2986.

https://doi.org/10.1002/mrm.28985