13:30 | 0538.
| Improved across-scanner reproducibility using vendor-agnostic diffusion sequences Qiang Liu1,2, Lipeng Ning1, Imam Ahmed Shaik1, Borjan Gagoski3, Berkin Bilgic4,5, William Grissom6, Jon-Fredrik Nielsen7, Maxim Zaitsev8, and Yogesh Rathi1 1Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States, 2School of Biomedical Engineering, Southern Medical University, Guangzhou, China, 3Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States, 4Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 5Department of Radiology, Harvard Medical School, Boston, MA, United States, 6Department of Biomedical Engineering, Case School of Engineering, Case Western Reserve University, Cleveland, OH, United States, 7fMRI Laboratory and Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, 8Division of Medical Physics, Department of Radiology, University Medical Center Freiburg, Freiburg, Germany Keywords: Diffusion Acquisition, Diffusion Tensor Imaging Motivation: The reproducibility of diffusion MRI (dMRI) data collected at multiple sites can be affected by differences between MRI scanners, especially scanners from different manufacturers. Goal(s): To develop vendor-neutral dMRI pulse sequences using our Pulseq development platform and reduce the inter-scanner variability between scanners from different vendors. Approach: Using a diffusion phantom and with three human subjects, we tested inter-scanner variability using Pulseq and vendor-specific product sequences. We report inter-scanner variations using standard error for mean diffusivity and fractional anisotropy.
Results: Pulseq sequence yielded dramatically better results (>2x reduction in variability) enhancing the reliability of dMRI measurements across scanners. Impact: The vendor-neutral Pulseq-diffusion sequence has the potential to harmonize data acquisition and improve the robustness of diffusion MRI, making it an invaluable tool for advancing multi-site studies. |
13:42 | 0539.
| Harnessing QA/QC protocols for diffusion MRI neuroimaging workflows with MRIQC Teresa Gomez1, Yibei Chen2, Céline Provins3, Christopher J Markiewicz4, Ariel Rokem1, and Oscar Esteban3 1Dept. of Psychology and eScience Institute, University of Washington, Seattle, WA, United States, 2McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States, 3Dept. of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 4Dept. of Psychology, Stanford University, Stanford, CA, United States Keywords: Software Tools, Software Tools, QA/QC Motivation: Reliable neuroimaging pipelines require the implementation of robust QA/QC protocols. Goal(s): Developing an extension of MRIQC for the QA/QC of diffusion MRI data. Approach: We build on MRIQC's infrastructure to generate individual visual reports of dMRI images and define new image quality metrics (IQMs). Results: We developed a minimal processing pipeline for whole-brain dMRI data of human adults. The processing pipeline generates individual visual reports for the QA of unprocessed inputs. The pipeline also extracts IQMs to train automated decision-making, following MRIQC's established pattern. Impact: MRIQC is a widely-adopted tool for the QA/QC of unprocessed MRI data. However, support for dMRI was previously lacking. This MRIQC extension will improve QA/QC of dMRI by bringing it to the highest standards and will facilitate the implementation of rigorous protocols in multimodal neuroimaging. |
13:54 | 0540.
| Towards Reproducible Intravoxel Incoherent Motion (IVIM) Analysis: The ISMRM Open Science Initiative for Perfusion Imaging Oscar Jalnefjord1,2, Ivan A. Rashid3,4, Daan Kuppens5,6, Merel van der Thiel7,8, Petra van Houdt9, Paulien HM Voorter7,8, Eric T Peterson10, and Oliver Gurney-Champion5,6 1Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, 2Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden, 3Medical Radiation Physics, Department of Translational Medicine, Lund University, Malmö, Sweden, 4Radiation Physics, Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden, 5Department of Radiology and Nuclear Imaging, Amsterdam UMC location University of Amsterdam, Amsterdam, Netherlands, 6Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, Netherlands, 7Department of Radiology & Nuclear Medicine, School for Mental Health & Neuroscience, Maastricht University Medical Center, Maastricht, Netherlands, 8School for Mental Health & Neuroscience, Maastricht University, Maastricht, Netherlands, 9Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, Netherlands, 10Biosciences, Neuroscience Program, SRI International, Menlo Park, CA, United States Keywords: Software Tools, Perfusion, Reproducible research Motivation: Lack of validated and open-source intravoxel incoherent motion (IVIM) post-processing and fitting code is hindering reproducible research, limiting the validation and large-scale roll-out of IVIM imaging. Goal(s): To create an open-source code repository for IVIM-related code. Approach: Scientists interested in IVIM are encouraged to upload their code to our open-source code repository built by the ISMRM OSIPI task force 2.4, where automated testing and evaluation based on reference data are used to enable quality control of the code. Results: As of November 2023, 19 code contributions have been submitted by 6 different institutes, all passing automated testing. Impact: The work of ISMRM OSIPI task force 2.4 enables an open-source platform for validated code relevant to intravoxel incoherent motion (IVIM) imaging, thus reducing duplicate development, improving reproducibility, and serving as a benchmark for future methods. |
14:06 | 0541.
| Automated Quality Control for Multi-Vendor, Multi-Centre Renal Imaging Studies Alexander J Daniel1, Martin Craig1,2, David L Thomas3,4,5, Iosif Mendichovszky6,7, Steven Sourbron8, David M Morris9, Andrew N Priest6,7, Charlotte E Buchanan1, and Susan T Francis1,2 1Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom, 2NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, United Kingdom, 3Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, 4Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, 5Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, 6Department of Radiology, Addenbrooke’s Hospital, Cambridge, United Kingdom, 7Department of Radiology, University of Cambridge, Cambridge, United Kingdom, 8Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom, 9Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom Keywords: Software Tools, Software Tools, Standardisation, Quality Control Motivation: It is critical that MRI data acquired in multi-site, multi-vendor studies conforms to a standardised acquisition protocol. Goal(s): To develop XNAT tools to highlight scans that do not conform to a specified protocol or are of insufficient quality, enabling rapid correction of errors before future scans. Approach: Multi-site DICOM data is uploaded to XNAT after acquisition, by integrating software tools with this database, investigators are informed if data does not conform. Results: DICOM-QC, a tool to automatically compare DICOM metadata to predefined values, and ImageSNR-QC to calculate image SNR, applied here to a multi-site kidney study. Impact: This work outlines two tools that integrate with XNAT, DICOM-QC
and ImageSNR-QC, which can be used by any investigators running large studies
to ensure uploaded data conforms to the study protocol, ensuring consistency
over sites, vendors, and repeated longitudinal scans. |
14:18 | 0542.
| A repository-integrated tool for monitoring imaging protocol compliance in a multi-centre whole-body MRI myeloma study Sam Keaveney1,2, Damien J McHugh3,4, Mihaela Rata1,2, Alina Dragan1, Matthew Blackledge1,2, Erica Scurr1, Jessica M Winfield1,2, Dow-Mu Koh1,2, Simon J Doran1,2, Michael Berks4, James PB O'Connor2,4,5, Alexander King6, Winston J Rennie7, Suchi Gaba8, Priya Suresh9, Paul Malcolm10, Amy Davis11, Anjumara Nilak12, Aarti Shah13, Sanjay Gandhi14, Mauro Albrizio15, Arnold Drury16, Guy Pratt17, Gordon Cook18,19, Sadie Roberts18, Andrew Hall18, Matthew Jenner6, Sarah Brown18, Martin Kaiser20,21, Penny L Hubbard Cristinacce4, and Christina Messiou1,2 1MRI Unit, The Royal Marsden Hospital NHS Foundation Trust, London, United Kingdom, 2Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom, 3Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, United Kingdom, 4Quantitative Biomedical Imaging, Division of Cancer Sciences, The University of Manchester, Manchester, United Kingdom, 5Department of Radiology, The Christie NHS Foundation Trust, Manchester, United Kingdom, 6University Hospitals Southampton NHS Foundation Trust, Southampton, United Kingdom, 7University Hospitals of Leicester NHS Trust, Leicester, United Kingdom, 8University Hospitals of North Midlands NHS Trust, Stoke-on-Trent, United Kingdom, 9University Hospitals Plymouth NHS Trust, Plymouth, United Kingdom, 10Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, United Kingdom, 11Epsom and St. Helier University Hospitals NHS Trust, Epsom, United Kingdom, 12Worcestershire Acute Hospitals NHS Foundation Trust, Worcester, United Kingdom, 13Hampshire Hospitals NHS Foundation Trust, Basingstoke, United Kingdom, 14North Bristol NHS Trust, Bristol, United Kingdom, 15Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom, 16Royal Bournemouth and Christchurch Hospitals NHS Foundation Trust, Bournemouth, United Kingdom, 17University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom, 18Clinical Trials Research Unit, Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, United Kingdom, 19Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom, 20Division of Genetics and Epidemiology, The Institute of Cancer Research, London, United Kingdom, 21Department of Haematology, The Royal Marsden Hospital NHS Foundation Trust, London, United Kingdom Keywords: Software Tools, Translational Studies, Standardisation, reproducibility, QA/QC, multi-centre studies Motivation: Standardisation of imaging protocols in multi-centre studies is challenging, which can hamper clinical translation. Goal(s): This study aimed to develop and demonstrate a software tool that automatically assesses imaging protocol compliance. Approach: The tool was containerised and integrated into an imaging repository. It was applied to a dataset from a whole-body MRI (WB-MRI) myeloma study, which included 174 examinations acquired across 10 sites with scanners from three manufacturers. Results: The software successfully identified some parameters and sites where persistent deviations occurred, although 88% of examinations were conducted according to the relevant clinical guidelines with good overall compliance to site-specific protocols. Impact: Repository-integrated software is presented for automated
monitoring of imaging protocol compliance to support standardisation in multi-centre
studies and clinical translation. A multi-centre whole-body MRI study
demonstrates good compliance that could have been improved further with
proactive monitoring using this tool. |
14:30 | 0543.
| Integrated Registration and Harmonization Framework for Quantitative T2-weighted MRI Analysis following Prostate Cancer Radiotherapy Evangelia I. Zacharaki1, Adrian L. Breto 1, Ahmad Algohary 1, Veronica M. Wallaengen1, Sanoj Punnen 1, Matthew C. Abramowitz 1, Alan Pollack1, and Radka Stoyanova1 1University of Miami, Miami, FL, United States Keywords: Data Processing, Quantitative Imaging, Prostate Cancer Motivation: The reliable evaluation of T2-weighted MRI (T2w) signal change in prostate cancer following radiotherapy (RT) is challenging due to deformations (physiological and RT-related) and scanner/protocol acquisition variability. Goal(s): To develop an automated and reproducible methodology for quantification of T2w signal change in longitudinal studies following RT. Approach: The methodology includes T2w image intensity harmonization and deformable registration of post-RT to pre-RT images for automated detection of prostate, peripheral zone and tumor volume. Results: The repeatability in T2w intensity estimation improved following the automatic registration relative to manual contours; and the quantitative changes of T2w reached significance when pre- and post-RT series were compared. Impact: The
developed methodology allows to automatically detect ROIs in post-RT MRI exams, reduces
data acquisition-related variation and improves imaging features’ repeatability,
thereby enables the quantitative characterization of RT-induced changes in T2w. |
14:42 | 0544.
| PhyCHarm : Physics-Constrained Deep Neural Networks for Multi-Scanner Harmonization Gawon Lee1, Junhyeok Lee1, Dong Hye Ye2, and Se-Hong Oh1 1Biomedical Engineering, Hankuk University of Foreign Studies, Yongin-si, Korea, Republic of, 2Computer Science, Georgia State University, Atlanta, GA, United States Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence, Data Harmonization, Bloch equation Motivation: The MR scanner effect in a multi-site dataset can affect bias in statistical analysis or reduce generality in deep neural networks. Goal(s): We aim to suggest a MR physics-informed harmonization framework (PhyCHarm) that generates consistent quantitative maps and harmonized T1w images. Approach: We introduce a Quantitative Maps Generator and a Harmonization Network to be trained with a constraint loss based on a signal equation. Results: PhyCHarm shows the highest evaluation scores in both networks and consistent segmentation accuracy in the downstream task (FSL FAST GM and WM segmentation). Impact: PhyCHarm works based on the Bloch equation. PhyCHarm
enables us to reduce scanner effects efficiently in the dataset before
conducting test/retest, longitudinal, or multi-site studies. It can be helpful
to ensure deep neural networks' generality. |
14:54 | 0545.
| How frequently should we use a phantom for QA? A preliminary assessment Kalina V Jordanova1, Stephen E Ogier1, Stephen E Russek1, Cassandra M Stoffer1, Guido Buonincontri2, Mathias Nittka2, and Kathryn E Keenan1 1NIST: National Institute of Standards and Technology, Boulder, CO, United States, 2Siemens Healthcare GmbH, Erlangen, Germany Keywords: Phantoms, Precision & Accuracy, Quality Assurance, MR Fingerprinting, Quantitative Imaging, Relaxometry, Measurement & Correction Motivation: Currently, we do not know how frequently quality assurance (QA) should be performed on an MRI scanner to detect changes that impact quantitative measurements. Goal(s): Our goal is to determine the frequency of QA measurements needed during the course of a quantitative in vivo study to have confidence in the in vivo measurements. Approach: Phantom quantitative QA measurements were made immediately before or after the in vivo measurements over the duration of a repeatability study. Results: All quantitative phantom measurements had variation well below 10 % over the course of the 99 day study. Impact: We now know that for measurements using magnetic resonance fingerprinting on this system, QA using phantom measurements is only necessary at the start and end of an in vivo study when the study duration is less than approximately 3 months. |
15:06 | 0546.
| Testing the Quantitative Imaging Biomarkers Alliance (QIBA) PDFF Profile in the Liver: Results from 416 Scanners at 1.5T and 3T Adrienne G. Siu1, Mary Jean Solywoda1, Tom Davis1, Matthew D. Robson1, and Roberto Salvati1 1Perspectum, Oxford, United Kingdom Keywords: Quantitative Imaging, Validation, Liver, Phantoms Motivation: The Quantitative Imaging Biomarkers Alliance (QIBA) PDFF Profile describes the expected performance of an imaging technique when measuring PDFF. However, the expected performance in phantoms was determined in one phantom on 27 scanners and may not be applicable widely. Goal(s): We tested the hypothesis that the QIBA PDFF bias criteria (mean:within ±5.0%, maximum:within ±7.0% (percentage points)) cannot be attained at scale with multiple phantoms on >400 scanners. Approach: We calculated the QIBA PDFF criteria using phantom data from 416 scanners across three vendors at 1.5T and 3T. Results: All six combinations of scanner vendor and field strength passed the QIBA PDFF criteria. Impact: The hypothesis that the QIBA PDFF bias criteria cannot be maintained with multiple phantoms on >400 scanners was disproven in a novel dataset with 416 scanners, strongly suggesting that it is possible to achieve this level of performance at scale. |
15:18 | | DiscussionPenny Cristinacce The University of Manchester, Manchester, United Kingdom |