Background Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing... Show moreBackground Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing artificial intelligence algorithms and workflows. To ensure data reproducibility, criteria for standardised segmentation are critical but currently unavailable. Methods A modified Delphi process initiated by the European Imaging Biomarker Alliance (EIBALL) of the European Society of Radiology (ESR) and the European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group was undertaken. Three multidisciplinary task forces addressed modality and image acquisition, segmentation methodology itself, and standards and logistics. Devised survey questions were fed via a facilitator to expert participants. The 58 respondents to Round 1 were invited to participate in Rounds 2-4. Subsequent rounds were informed by responses of previous rounds. Results/conclusions Items with >= 75% consensus are considered a recommendation. These include system performance certification, thresholds for image signal-to-noise, contrast-to-noise and tumour-to-background ratios, spatial resolution, and artefact levels. Direct, iterative, and machine or deep learning reconstruction methods, use of a mixture of CE marked and verified research tools were agreed and use of specified reference standards and validation processes considered essential. Operator training and refreshment were considered mandatory for clinical trials and clinical research. Items with a 60-74% agreement require reporting (site-specific accreditation for clinical research, minimal pixel number within lesion segmented, use of post-reconstruction algorithms, operator training refreshment for clinical practice). Items with <= 60% agreement are outside current recommendations for segmentation (frequency of system performance tests, use of only CE-marked tools, board certification of operators, frequency of operator refresher training). Recommendations by anatomical area are also specified. Show less
Chappell, M.A.; McConnell, F.A.K.; Golay, X.; Gunther, M.; Hernandez-Tamames, J.A.; Osch, M.J. van; Asllani, I. 2021
The mismatch in the spatial resolution of Arterial Spin Labeling (ASL) MRI perfusion images and the anatomy of functionally distinct tissues in the brain leads to a partial volume effect (PVE),... Show moreThe mismatch in the spatial resolution of Arterial Spin Labeling (ASL) MRI perfusion images and the anatomy of functionally distinct tissues in the brain leads to a partial volume effect (PVE), which in turn confounds the estimation of perfusion into a specific tissue of interest such as gray or white matter. This confound occurs because the image voxels contain a mixture of tissues with disparate perfusion properties, leading to estimated perfusion values that reflect primarily the volume proportions of tissues in the voxel rather than the perfusion of any particular tissue of interest within that volume. It is already recognized that PVE influences studies of brain perfusion, and that its effect might be even more evident in studies where changes in perfusion are co-incident with alterations in brain structure, such as studies involving a comparison between an atrophic patient population vs control subjects, or studies comparing subjects over a wide range of ages. However, the application of PVE correction (PVEc) is currently limited and the employed methodologies remain inconsistent. In this article, we outline the influence of PVE in ASL measurements of perfusion, explain the main principles of PVEc, and provide a critique of the current state of the art for the use of such methods. Furthermore, we examine the current use of PVEc in perfusion studies and whether there is evidence to support its wider adoption. We conclude that there is sound theoretical motivation for the use of PVEc alongside conventional, 'uncorrected', images, and encourage such combined reporting. Methods for PVEc are now available within standard neuroimaging toolboxes, which makes our recommendation straightforward to implement. However, there is still more work to be done to establish the value of PVEc as well as the efficacy and robustness of existing PVEc methods. Show less
Combining arterial spin-labeling MRA with contrast-enhanced time-resolved MRA holds promise as an alternative to DSA for confirmation of obliteration following gamma knife radiosurgery for brain... Show moreCombining arterial spin-labeling MRA with contrast-enhanced time-resolved MRA holds promise as an alternative to DSA for confirmation of obliteration following gamma knife radiosurgery for brain AVMs, having provided 100% sensitivity and specificity in the study.BACKGROUND AND PURPOSE:Intra-arterial DSA has been traditionally used for confirmation of cure following gamma knife radiosurgery for AVMs. Our aim was to evaluate whether 4D arterial spin-labeling MRA and contrast-enhanced time-resolved MRA in combination can be an alternative to DSA for confirmation of AVM obliteration following gamma knife radiosurgery.MATERIALS AND METHODS:In this prospective study, 30 patients undergoing DSA for confirmation of obliteration following gamma knife radiosurgery for AVMs (criterion standard) also underwent MRA, including arterial spin-labeling MRA and contrast-enhanced time-resolved MRA. One dataset was technically unsatisfactory, and the case was excluded. The DSA and MRA datasets of 29 patients were independently and blindly evaluated by 2 observers regarding the presence/absence of residual AVMs.RESULTS:The mean time between gamma knife radiosurgery and follow-up DSA/MRA was 53?months (95% CI, 42?64 months; range, 22?168 months). MRA total scanning time was 9 minutes and 17 seconds. Residual AVMs were detected on DSA in 9 subjects (obliteration rate = 69%). All residual AVMs were detected on at least 1 MRA sequence. Arterial spin-labeling MRA and contrast-enhanced time-resolved MRA showed excellent specificity and positive predictive values individually (100%). However, their sensitivity and negative predictive values were suboptimal due to 1 false-negative with arterial spin-labeling MRA and 2 with contrast-enhanced time-resolved MRA (sensitivity = 88% and 77%, negative predictive values = 95% and 90%, respectively). Both sensitivity and negative predictive values increased to 100% if a composite assessment of both MRA sequences was performed. Diagnostic accuracy (receiver operating characteristic) and agreement (?) are maximized using arterial spin-labeling MRA and contrast-enhanced time-resolved MRA in combination (area under receiver operating characteristic curve?= 1, P??=?1, P? Show less
Fournier, L.; Costaridou, L.; Bidaut, L.; Michoux, N.; Lecouvet, F.E.; Geus-Oei, L.F. de; ... ; European Soc Radiology 2021
PurposeIntra-arterial Digital Subtraction Angiography (DSA) is the gold standard technique for radiosurgery target delineation in brain Arterio-Venous Malformations (AVMs). This study aims to... Show morePurposeIntra-arterial Digital Subtraction Angiography (DSA) is the gold standard technique for radiosurgery target delineation in brain Arterio-Venous Malformations (AVMs). This study aims to evaluate whether a combination of three Magnetic Resonance Angiography sequences (triple-MRA) could be used for delineation of brain AVMs for Gamma Knife Radiosurgery (GKR).MethodsFifteen patients undergoing DSA for GKR targeting of brain AVMs also underwent triple-MRA: 4D Arterial Spin Labelling based angiography (ASL-MRA), Contrast-Enhanced Time-Resolved MRA (CE-MRA) and High Definition post-contrast Time-Of-Flight angiography (HD-TOF). The arterial phase of the AVM nidus was delineated on triple-MRA by an interventional neuroradiologist and a consultant neurosurgeon (triple-MRA volume). Triple-MRA volumes were compared to AVM targets delineated by the clinical team for delivery of GKR using the current planning paradigm, i.e., stereotactic DSA and volumetric MRI (DSA volume). Difference in size, degree of inclusion (DI) and concordance index (CcI) between DSA and triple-MRA volumes are reported.ResultsAVM target volumes delineated on triple-MRA were on average 9.8% smaller than DSA volumes (95%CI:5.6-13.9%; SD:7.14%; p = .003). DI of DSA volume in triple-MRA volume was on average 73.5% (95%CI:71.2-76; range: 65-80%). The mean percentage of triple-MRA volume not included on DSA volume was 18% (95%CI:14.7-21.3; range: 7-30%).ConclusionThe technical feasibility of using triple-MRA for visualisation and delineation of brain AVMs for GKR planning has been demonstrated. Tighter and more precise delineation of AVM target volumes could be achieved by using triple-MRA for radiosurgery targeting. However, further research is required to ascertain the impact this may have in obliteration rates and side effects. Show less
Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before... Show moreExisting quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Show less
Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a... Show moreArterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners.The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. To facilitate collaboration and data-exchange, the toolbox follows several standards and recommendations for data structure, provenance, and best analysis practice.ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow.ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts which may increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice. Show less
