ATOMMIC: An Advanced Toolbox for Multitask Medical Imaging Consistency to facilitate Artificial Intelligence applications from acquisition to analysis in Magnetic Resonance Imaging

AI is revolutionizing MRI along the acquisition and processing chain. Advanced AI frameworks have been developed to apply AI in various successive tasks, such as image reconstruction, quantitative parameter map estimation, and image segmentation. Existing frameworks are often designed to perform tasks independently or are focused on specific models or datasets, limiting generalization. We introduce ATOMMIC, an open-source toolbox that streamlines AI applications for accelerated MRI reconstruction and analysis. ATOMMIC implements several tasks using DL networks and enables MultiTask Learning (MTL) to perform related tasks integrated, targeting generalization in the MRI domain. We first review the current state of AI frameworks for MRI through a comprehensive literature search and by parsing 12,479 GitHub repositories. We benchmark 25 DL models on eight publicly available datasets to present distinct applications of ATOMMIC on accelerated MRI reconstruction, image segmentation, quantitative parameter map estimation, and joint accelerated MRI reconstruction and image segmentation utilizing MTL. Our findings demonstrate that ATOMMIC is the only MTL framework with harmonized complex-valued and real-valued data support. Evaluations on single tasks show that physics-based models, which enforce data consistency by leveraging the physical properties of MRI, outperform other models in reconstructing highly accelerated acquisitions. Physics-based models that produce high reconstruction quality can accurately estimate quantitative parameter maps. When high-performing reconstruction models are combined with robust segmentation networks utilizing MTL, performance is improved in both tasks. ATOMMIC facilitates MRI reconstruction and analysis by standardizing workflows, enhancing data interoperability, integrating unique features like MTL, and effectively benchmarking DL models

The link between market orientation and performance in the Australian public sector

Marketing academics and practitioners assume a direct link between market orientation and performance and argue that this applies to both business and non-business organisations. While this aspect has been studied in the business sector, this paper discusses the concepts of market orientation and performance and investigates this relationship in the Australian public sector. The conceptualization of market orientation used is that by Jaworski and Kohli (1993) on which basis MARKOR was developed. This instrument together with an instrument to measure the perceptions of performance of senior managers in the Australian public sector are used to investigate the hypothesized link. The findings confirm a positive relationship between market orientation and performance. The size and type of public sector organisation involved are also found to affect the levels of market orientation together with its components and performance. From the findings, implication are drawn and directions for future research discussed.peer-reviewe

Limitations of Quantitative Blush Evaluator (QuBE) as myocardial perfusion assessment method on digital coronary angiograms

Background and Aim: Quantitative Blush Evaluator (QuBE) is a software application that allows quantifying myocardial perfusion in coronary angiograms after a percutaneous coronary intervention. QuBE has some limitations such as the application of a crude filter to remove large scale structures and the absence of correction for cardiac motion. This study investigates the extent of these limitations and we hypothesize that enhanced image analysis methods can provide improvements. Methods: We calculated QuBE scores of 117 patients from the HEBE Trial and determined its association with the Myocardial Blush Grade (MBG) score. Accuracy of large-structure removal is qualitatively assessed for various sizes of a median filter. The influence of cardiac motion was evaluated by comparing the blush curve and QuBE score of the native QuBE with manually motion-corrected QuBE for 40 patients. The effect of different kernel sizes and motion correction to a potential improvement of the association between QuBE score and MBG was studied. Results: In our population, there was no significant association between QuBE score and MBG (p = 0.14). Median filters of various kernel sizes were unable to remove large structure related noise. Variations in filters and cardiac movement correction did not result in an improvement in the association with MBG scores (observer 1: p = 0.66; observer 2: p = 0.72). Conclusions: There was no significant association of QuBE with MBG scores in our population, which suggests that QuBE is not suitable for a quantitative assessment of myocardial perfusion. Alternative kernel sizes for the large structure removal filter and cardiac motion correction did not improve QuBE performance. Relevance for patients: Further improvements of QuBE to overcome its inherent limitations are necessary in order to establish QuBE as a reliable myocardial perfusion assessment method

Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning

CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute stroke. Conventional perfusion analysis performs a deconvolution of the measurements and thresholds the perfusion parameters to determine the tissue status. We pursue a data-driven and deconvolution-free approach, where a deep neural network learns to predict the final infarct volume directly from the native CTP images and metadata such as the time parameters and treatment. This would allow clinicians to simulate various treatments and gain insight into predicted tissue status over time. We demonstrate on a multicenter dataset that our approach is able to predict the final infarct and effectively uses the metadata. An ablation study shows that using the native CTP measurements instead of the deconvolved measurements improves the prediction.Comment: Accepted for publication in Medical Image Analysi

In-Silico Trials for Treatment of Acute Ischemic Stroke

Despite improved treatment, a large portion of patients with acute ischemic stroke due to a large vessel occlusion have poor functional outcome. Further research exploring novel treatments and better patient selection has therefore been initiated. The feasibility of new treatments and optimized patient selection are commonly tested in extensive and expensive randomized clinical trials. in-silico trials, computer-based simulation of randomized clinical trials, have been proposed to aid clinical trials. In this white paper, we present our vision and approach to set up in-silico trials focusing on treatment and selection of patients with an acute ischemic stroke. The INSIST project (IN-Silico trials for treatment of acute Ischemic STroke, www.insist-h2020.eu) is a collaboration of multiple experts in computational science, cardiovascular biology, biophysics, biomedical engineering, epidemiology, radiology, and neurology. INSIST will generate virtual populations of acute ischemic stroke patients based on anonymized data from the recent stroke trials and registry, and build on the existing and emerging in-silico models for acute ischemic stroke, its treatment (thrombolysis and thrombectomy) and the resulting perfusion changes. These models will be used to design a platform for in-silico trials that will be validated with existing data and be used to provide a proof of concept of the potential efficacy of this emerging technology. The platform will be used for preliminary evaluation of the potential suitability and safety of medication, new thrombectomy device configurations and methods to select patient subpopulations for better treatment outcome. This could allow generating, exploring and refining relavant hypotheses on potential causal pathways (which may follow from the evidence obtained from clinical trials) and improving clinical trial design. Importantly, the findings of the in-silico trials will require validation under the controlled settings of randomized clinical trials

Deep learning-based white matter lesion volume on CT is associated with outcome after acute ischemic stroke

Background: Intravenous thrombolysis (IVT) before endovascular treatment (EVT) for acute ischemic stroke might induce intracerebral hemorrhages which could negatively affect patient outcomes. Measuring white matter lesions size using deep learning (DL-WML) might help safely guide IVT administration. We aimed to develop, validate, and evaluate a DL-WML volume on CT compared to the Fazekas scale (WML-Faz) as a risk factor and IVT effect modifier in patients receiving EVT directly after IVT.Methods: We developed a deep-learning model for WML segmentation on CT and validated with internal and external test sets. In a post hoc analysis of the MR CLEAN No-IV trial, we associated DL-WML volume and WML-Faz with symptomatic-intracerebral hemorrhage (sICH) and 90-day functional outcome according to the modified Rankin Scale (mRS). We used multiplicative interaction terms between WML measures and IVT administration to evaluate IVT treatment effect modification. Regression models were used to report unadjusted and adjusted common odds ratios (cOR/acOR).Results: In total, 516 patients from the MR CLEAN No-IV trial (male/female, 291/225; age median, 71 [IQR, 62–79]) were analyzed. Both DL-WML volume and WML-Faz are associated with sICH (DL-WML volume acOR, 1.78 [95%CI, 1.17; 2.70]; WML-Faz acOR, 1.53 95%CI [1.02; 2.31]) and mRS (DL-WML volume acOR, 0.70 [95%CI, 0.55; 0.87], WML-Faz acOR, 0.73 [95%CI 0.60; 0.88]). Only in the unadjusted IVT effect modification analysis WML-Faz was associated with more sICH if IVT was given (p = 0.046). Neither WML measure was associated with worse mRS if IVT was given.Conclusion: DL-WML volume and WML-Faz had a similar relationship with functional outcome and sICH. Although more sICH might occur in patients with more severe WML-Faz receiving IVT, no worse functional outcome was observed. Clinical relevance statement: White matter lesion severity on baseline CT in acute ischemic stroke patients has a similar predictive value if measured with deep learning or the Fazekas scale. Safe administration of intravenous thrombolysis using white matter lesion severity should be further studied. Key Points: White matter damage is a predisposing risk factor for intracranial hemorrhage in patients with acute ischemic stroke but remains difficult to measure on CT. White matter lesion volume on CT measured with deep learning had a similar association with symptomatic intracerebral hemorrhages and worse functional outcome as the Fazekas scale. A patient-level meta-analysis is required to study the benefit of white matter lesion severity-based selection for intravenous thrombolysis before endovascular treatment.</p

Brain segmentation in patients with perinatal arterial ischemic stroke

BACKGROUND: Perinatal arterial ischemic stroke (PAIS) is associated with adverse neurological outcomes. Quantification of ischemic lesions and consequent brain development in newborn infants relies on labor-intensive manual assessment of brain tissues and ischemic lesions. Hence, we propose an automatic method utilizing convolutional neural networks (CNNs) to segment brain tissues and ischemic lesions in MRI scans of infants suffering from PAIS. MATERIALS AND METHODS: This single-center retrospective study included 115 patients with PAIS that underwent MRI after the stroke onset (baseline) and after three months (follow-up). Nine baseline and 12 follow-up MRI scans were manually annotated to provide reference segmentations (white matter, gray matter, basal ganglia and thalami, brainstem, ventricles, extra-ventricular cerebrospinal fluid, and cerebellum, and additionally on the baseline scans the ischemic lesions). Two CNNs were trained to perform automatic segmentation on the baseline and follow-up MRIs, respectively. Automatic segmentations were quantitatively evaluated using the Dice coefficient (DC) and the mean surface distance (MSD). Volumetric agreement between segmentations that were manually and automatically obtained was computed. Moreover, the scan quality and automatic segmentations were qualitatively evaluated in a larger set of MRIs without manual annotation by two experts. In addition, the scan quality was qualitatively evaluated in these scans to establish its impact on the automatic segmentation performance. RESULTS: Automatic brain tissue segmentation led to a DC and MSD between 0.78-0.92 and 0.18-1.08 mm for baseline, and between 0.88-0.95 and 0.10-0.58 mm for follow-up scans, respectively. For the ischemic lesions at baseline the DC and MSD were between 0.72-0.86 and 1.23-2.18 mm, respectively. Volumetric measurements indicated limited oversegmentation of the extra-ventricular cerebrospinal fluid in both the follow-up and baseline scans, oversegmentation of the ischemic lesions in the left hemisphere, and undersegmentation of the ischemic lesions in the right hemisphere. In scans without imaging artifacts, brain tissue segmentation was graded as excellent in more than 85% and 91% of cases, respectively for the baseline and follow-up scans. For the ischemic lesions at baseline, this was in 61% of cases. CONCLUSIONS: Automatic segmentation of brain tissue and ischemic lesions in MRI scans of patients with PAIS is feasible. The method may allow evaluation of the brain development and efficacy of treatment in large datasets

Thresholds for Arterial Wall Inflammation Quantified by 18F-FDG PET Imaging Implications for Vascular Interventional Studies

AbstractObjectivesThis study assessed 5 frequently applied arterial 18fluorodeoxyglucose (18F-FDG) uptake metrics in healthy control subjects, those with risk factors and patients with cardiovascular disease (CVD), to derive uptake thresholds in each subject group. Additionally, we tested the reproducibility of these measures and produced recommended sample sizes for interventional drug studies.Background18F-FDG positron emission tomography (PET) can identify plaque inflammation as a surrogate endpoint for vascular interventional drug trials. However, an overview of 18F-FDG uptake metrics, threshold values, and reproducibility in healthy compared with diseased subjects is not available.Methods18F-FDG PET/CT of the carotid arteries and ascending aorta was performed in 83 subjects (61 ± 8 years) comprising 3 groups: 25 healthy controls, 23 patients at increased CVD risk, and 35 patients with known CVD. We quantified 18F-FDG uptake across the whole artery, the most-diseased segment, and within all active segments over several pre-defined cutoffs. We report these data with and without background corrections. Finally, we determined measurement reproducibility and recommended sample sizes for future drug studies based on these results.ResultsAll 18F-FDG uptake metrics were significantly different between healthy and diseased subjects for both the carotids and aorta. Thresholds of physiological 18F-FDG uptake were derived from healthy controls using the 90th percentile of their target to background ratio (TBR) value (TBRmax); whole artery TBRmax is 1.84 for the carotids and 2.68 in the aorta. These were exceeded by >52% of risk factor patients and >67% of CVD patients. Reproducibility was excellent in all study groups (intraclass correlation coefficient >0.95). Using carotid TBRmax as a primary endpoint resulted in sample size estimates approximately 20% lower than aorta.ConclusionsWe report thresholds for physiological 18F-FDG uptake in the arterial wall in healthy subjects, which are exceeded by the majority of CVD patients. This remains true, independent of readout vessel, signal quantification method, or the use of background correction. We also confirm the high reproducibility of 18F-FDG PET measures of inflammation. Nevertheless, because of overlap between subject categories and the relatively small population studied, these data have limited generalizability until substantiated in larger, prospective event-driven studies. (Vascular Inflammation in Patients at Risk for Atherosclerotic Disease; NTR5006

Impact of Intracranial Volume and Brain Volume on the Prognostic Value of Computed Tomography Perfusion Core Volume in Acute Ischemic Stroke

Background: Computed tomography perfusion (CTP)-estimated core volume is associated with functional outcomes in acute ischemic stroke. This relationship might differ among patients, depending on brain volume. Materials and Methods: We retrospectively included patients from the MR CLEAN Registry. Cerebrospinal fluid (CSF) and intracranial volume (ICV) were automatically segmented on NCCT. We defined the proportion of the ICV and total brain volume (TBV) affected by the ischemic core as ICVcore and TBVcore. Associations between the core volume, ICVcore, TBVcore, and functional outcome are reported per interquartile range (IQR). We calculated the area under the curve (AUC) to assess diagnostic accuracy.Results: In 200 patients, the median core volume was 13 (5–41) mL. Median ICV and TBV were 1377 (1283–1456) mL and 1108 (1020–1197) mL. Median ICVcore and TBVcore were 0.9 (0.4–2.8)% and 1.7 (0.5–3.6)%. Core volume (acOR per IQR 0.48 [95%CI 0.33–0.69]), ICVcore (acOR per IQR 0.50 [95%CI 0.35–0.69]), and TBVcore (acOR per IQR 0.41 95%CI 0.33–0.67]) showed a lower likelihood of achieving improved functional outcomes after 90 days. The AUC was 0.80 for the prediction of functional independence at 90 days for the CTP-estimated core volume, the ICVcore, and the TBVcore. Conclusion:Correcting the CTP-estimated core volume for the intracranial or total brain volume did not improve the association with functional outcomes in patients who underwent EVT.</p

Association of Ischemic Core Imaging Biomarkers With Post-Thrombectomy Clinical Outcomes in the MR CLEAN Registry

Background: A considerable proportion of acute ischemic stroke patients treated with endovascular thrombectomy (EVT) are dead or severely disabled at 3 months despite successful reperfusion. Ischemic core imaging biomarkers may help to identify patients who are more likely to have a poor outcome after endovascular thrombectomy (EVT) despite successful reperfusion. We studied the association of CT perfusion-(CTP), CT angiography-(CTA), and non-contrast CT-(NCCT) based imaging markers with poor outcome in patients who underwent EVT in daily clinical practice. Methods: We included EVT-treated patients (July 2016–November 2017) with an anterior circulation occlusion from the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry with available baseline CTP, CTA, and NCCT. We used multivariable binary and ordinal logistic regression to analyze the association of CTP ischemic core volume, CTA-Collateral Score (CTA-CS), and Alberta Stroke Program Early CT Score (ASPECTS) with poor outcome (modified Rankin Scale score (mRS) 5-6) and likelihood of having a lower score on the mRS at 90 days. Results: In 201 patients, median core volume was 13 (IQR 5-41) mL. Median ASPECTS was 9 (IQR 8-10). Most patients had grade 2 (83/201; 42%) or grade 3 (28/201; 14%) collaterals. CTP ischemic core volume was associated with poor outcome [aOR per 10 mL 1.02 (95%CI 1.01–1.04)] and lower likelihood of having a lower score on the mRS at 90 days [aOR per 10 mL 0.85 (95% CI 0.78–0.93)]. In multivariable analysis, neither CTA-CS nor ASPECTS were significantly associated with poor outcome or the likelihood of having a lower mRS. Conclusion: In our population of patients treated with EVT in daily clinical practice, CTP ischemic core volume is associated with poor outcome and lower likelihood of shift toward better outcome in contrast to either CTA-CS or ASPECTS