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Purpose: To systematically review the techniques that address undersampling artifacts in accelerated quantitative magnetic resonance imaging (qMRI)., Methods: A literature search was conducted using the Embase, Medline, Web of Science Core Collection, Coherence Central Register of Controlled Trials, and Google Scholar databases for studies, published before July 2022 proposing reconstruction techniques for accelerated qMRI. Studies are reviewed according to inclusion criteria, and included studies are categorized based on the methodology used., Results: A total of 292 studies included in the review are categorized. A technical overview of each category is provided, and the categories are described in a unified mathematical framework. The distribution of the reviewed studies over time, application domain, and parameters of interest is illustrated., Conclusion: An increasing trend in the number of articles that propose new techniques for accelerated qMRI reconstruction indicates the importance of acceleration in qMRI. The techniques are mostly validated for relaxometry parameters and brain scans. The categories of techniques are compared based on theoretical grounds, highlighting existing trends and potential gaps in the field., (© 2023 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Purpose: To improve image quality of multi-contrast imaging with the proposed Autocalibrated Parallel Imaging Reconstruction for Extended Multi-Contrast Imaging (APIR4EMC)., Methods: APIR4EMC reconstructs multi-contrast images in an autocalibrated parallel imaging reconstruction framework by adding contrasts as virtual coils. Compensation of signal evolution along the echo train of different contrasts is performed to improve signal prediction for missing samples. As a proof of concept, we performed prospectively accelerated phantom and in-vivo brain acquisitions with T1, T1-fat saturated (Fatsat), T2, PD, and FLAIR contrasts. The k-space sampling patterns of these acquisitions were jointly optimized. Images were jointly reconstructed with the proposed APIR4EMC method as well as individually with GRAPPA. Root mean square error (RMSE) to fully sampled reference images and g-factor maps were computed for both methods in the phantom experiment. Visual evaluation was performed in the in-vivo experiment., Results: Compared to GRAPPA, APIR4EMC reduced artifacts and improved SNR of the reconstructed images in the phantom acquisitions. Quantitatively, APIR4EMC substantially reduced noise amplification (g-factor) as well as RMSE compared to GRAPPA. Signal evolution compensation reduced artifacts. In the in-vivo experiments, 1 mm 3 isotropic 3D images with contrasts of T1, T1-Fatsat, T2, PD, and FLAIR were acquired in as little as 7.5 min with the acceleration factor of 9. Reconstruction quality was consistent with the phantom results., Conclusion: Compared to single contrast reconstruction with GRAPPA, APIR4EMC reduces artifacts and noise amplification in accelerated multi-contrast imaging., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

With the increasing size of datasets used in medical imaging research, the need for automated data curation is arising. One important data curation task is the structured organization of a dataset for preserving integrity and ensuring reusability. Therefore, we investigated whether this data organization step can be automated. To this end, we designed a convolutional neural network (CNN) that automatically recognizes eight different brain magnetic resonance imaging (MRI) scan types based on visual appearance. Thus, our method is unaffected by inconsistent or missing scan metadata. It can recognize pre-contrast T1-weighted (T1w),post-contrast T1-weighted (T1wC), T2-weighted (T2w), proton density-weighted (PDw) and derived maps (e.g. apparent diffusion coefficient and cerebral blood flow). In a first experiment,we used scans of subjects with brain tumors: 11065 scans of 719 subjects for training, and 2369 scans of 192 subjects for testing. The CNN achieved an overall accuracy of 98.7%. In a second experiment, we trained the CNN on all 13434 scans from the first experiment and tested it on 7227 scans of 1318 Alzheimer's subjects. Here, the CNN achieved an overall accuracy of 98.5%. In conclusion, our method can accurately predict scan type, and can quickly and automatically sort a brain MRI dataset virtually without the need for manual verification. In this way, our method can assist with properly organizing a dataset, which maximizes the shareability and integrity of the data.

Purpose: Diagnosing desmoid-type fibromatosis (DTF) requires an invasive tissue biopsy with β-catenin staining and CTNNB1 mutational analysis, and is challenging due to its rarity. The aim of this study was to evaluate radiomics for distinguishing DTF from soft tissue sarcomas (STS), and in DTF, for predicting the CTNNB1 mutation types., Methods: Patients with histologically confirmed extremity STS (non-DTF) or DTF and at least a pretreatment T1-weighted (T1w) MRI scan were retrospectively included. Tumors were semi-automatically annotated on the T1w scans, from which 411 features were extracted. Prediction models were created using a combination of various machine learning approaches. Evaluation was performed through a 100x random-split cross-validation. The model for DTF vs. non-DTF was compared to classification by two radiologists on a location matched subset., Results: The data included 203 patients (72 DTF, 131 STS). The T1w radiomics model showed a mean AUC of 0.79 on the full dataset. Addition of T2w or T1w post-contrast scans did not improve the performance. On the location matched cohort, the T1w model had a mean AUC of 0.88 while the radiologists had an AUC of 0.80 and 0.88, respectively. For the prediction of the CTNNB1 mutation types (S45 F, T41A and wild-type), the T1w model showed an AUC of 0.61, 0.56, and 0.74., Conclusions: Our radiomics model was able to distinguish DTF from STS with high accuracy similar to two radiologists, but was not able to predict the CTNNB1 mutation status., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

Purpose: To reduce artifacts and scan time of GRASE imaging by selecting an optimal sampling pattern and jointly reconstructing gradient echo and spin echo images., Methods: We jointly reconstruct images for the different echo types by considering these as additional virtual coil channels in the novel Autocalibrated Parallel Imaging Reconstruction with Sampling Pattern Optimization for GRASE (APIR4GRASE) method. Besides image reconstruction, we identify optimal sampling patterns for the acquisition. The selected optimal patterns were validated on phantom and in-vivo acquisitions. Comparison to the conventional GRASE without acceleration, and to the GRAPPA reconstruction with a single echo type was also performed., Results: Using identified optimal sampling patterns, APIR4GRASE eliminated modulation artifacts in both phantom and in-vivo experiments; mean square error (MSE) was reduced by 78% and 94%, respectively, compared to the conventional GRASE with similar scan time. Both artifacts and g-factor were reduced compared to the GRAPPA reconstruction with a single echo type., Conclusion: APIR4GRASE substantially improves the speed and quality of GRASE imaging over the state-of-the-art, and is able to reconstruct both spin echo and gradient echo images., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Purpose: Patients with 1p/19q codeleted low-grade glioma (LGG) have longer overall survival and better treatment response than patients with 1p/19q intact tumors. Therefore, it is relevant to know the 1p/19q status. To investigate whether the 1p/19q status can be assessed prior to tumor resection, we developed a machine learning algorithm to predict the 1p/19q status of presumed LGG based on preoperative MRI., Experimental Design: Preoperative brain MR images from 284 patients who had undergone biopsy or resection of presumed LGG were used to train a support vector machine algorithm. The algorithm was trained on the basis of features extracted from post-contrast T1-weighted and T2-weighted MR images and on patients' age and sex. The performance of the algorithm compared with tissue diagnosis was assessed on an external validation dataset of MR images from 129 patients with LGG from The Cancer Imaging Archive (TCIA). Four clinical experts also predicted the 1p/19q status of the TCIA MR images., Results: The algorithm achieved an AUC of 0.72 in the external validation dataset. The algorithm had a higher predictive performance than the average of the neurosurgeons (AUC 0.52) but lower than that of the neuroradiologists (AUC of 0.81). There was a wide variability between clinical experts (AUC 0.45-0.83)., Conclusions: Our results suggest that our algorithm can noninvasively predict the 1p/19q status of presumed LGG with a performance that on average outperformed the oncological neurosurgeons. Evaluation on an independent dataset indicates that our algorithm is robust and generalizable., (©2019 American Association for Cancer Research.)

Purpose: To evaluate in vivo T 2 mapping as quantitative, imaging-based biomarker for meniscal degeneration in humans, by studying the correlation between T 2 relaxation time and degree of histological degeneration as reference standard., Methods: In this prospective validation study, 13 menisci from seven patients with radiographic knee osteoarthritis (median age 67 years, three males) were included. Menisci were obtained during total knee replacement surgery. All patients underwent pre-operative magnetic resonance imaging using a 3-T MR scanner which included a T 2 mapping pulse sequence with multiple echoes. Histological analysis of the collected menisci was performed using the Pauli score, involving surface integrity, cellularity, matrix organization, and staining intensity. Mean T 2 relaxation times were calculated in meniscal regions of interest corresponding with the areas scored histologically, using a multi-slice multi-echo postprocessing algorithm. Correlation between T 2 mapping and histology was assessed using a generalized least squares model fit by maximum likelihood., Results: The mean T 2 relaxation time was 22.4 ± 2.7 ms (range 18.5-27). The median histological score was 10, IQR 7-11 (range 4-13). A strong correlation between T 2 relaxation time and histological score was found (r s  = 0.84, CI 95% 0.64-0.93)., Conclusion: In vivo T 2 mapping of the human meniscus correlates strongly with histological degeneration, suggesting that T 2 mapping enables the detection and quantification of early compositional changes of the meniscus in knee OA., Key Points: • Prospective histology-based study showed that in vivo T 2 mapping of the human meniscus correlates strongly with histological degeneration. • Meniscal T 2 mapping allows detection and quantifying of compositional changes, without need for contrast or special MRI hardware. • Meniscal T 2 mapping provides a biomarker for early OA, potentially allowing early treatment strategies and prevention of OA progression.

With aging, the brain undergoes several structural changes. These changes reflect the normal aging process and are therefore not necessarily pathologic. In fact, better understanding of these normal changes is an important cornerstone to also disentangle pathologic changes. Several studies have investigated normal brain aging, both cross-sectional and longitudinal, and focused on a broad range of magnetic resonance imaging (MRI) markers. This study aims to comprise the different aspects in brain aging, by performing a comprehensive longitudinal assessment of brain aging, providing trajectories of volumetric (global and lobar; subcortical and cortical), microstructural, and focal (presence of microbleeds, lacunar or cortical infarcts) brain imaging markers in aging and the sequence in which these markers change in aging. Trajectories were calculated on 10,755 MRI scans that were acquired between 2005 and 2016 among 5286 persons aged 45 years and older from the population-based Rotterdam Study. The average number of MRI scans per participant was 2 scans (ranging from 1 to 4 scans), with a mean interval between MRI scans of 3.3 years (ranging from 0.2 to 9.5 years) and an average follow-up time of 5.2 years (ranging from 0.3 to 9.8 years). We found that trajectories of the different volumetric, microstructural, and focal markers show nonlinear curves, with accelerating change with advancing age. We found earlier acceleration of change in global and lobar volumetric and microstructural markers in men compared with women. For subcortical and cortical volumes, results show a mix of more linear and nonlinear trajectories, either increasing, decreasing, or stable over age for the subcortical and cortical volume and thickness. Differences between men and women are visible in several parcellations; however, the direction of these differences is mixed. The presence of focal markers show a nonlinear increase with age, with men having a higher probability for cortical or lacunar infarcts. The data presented in this study provide insight into the normal aging process in the brain, and its variability., (Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Altered perfusion might play an important role in the pathophysiology of patellofemoral pain (PFP), a common knee complaint with unclear pathophysiology., Purpose: To investigate differences in dynamic contrast-enhanced (DCE)-MRI perfusion parameters between patients with PFP and healthy control subjects., Population/subjects/phantom/specimen/animal Model: Thirty-five adult patients with PFP and 44 healthy adult control subjects., Field Strength/sequence: 3T DCE-MRI consisting of a sagittal, anterior-posterior, frequency-encoded, fat-suppressed 3D spoiled gradient-echo sequence with intravenous contrast administration., Assessment: Patellar bone volumes of interest (VOIs) were delineated by a blinded observer. Quantitative perfusion parameters (k ep and k trans ) were calculated from motion-compensated DCE-MRI data by fitting Tofts' model. Weighted mean and unweighted median values of k ep and k trans were computed within the patellar bone VOIs., Statistical Tests: Differences in patellar bone perfusion parameters were compared between groups by linear regression analyses, adjusted for confounders., Results: Mean differences of weighted mean and unweighted median were 0.0039 (95% confidence interval [CI] -0.0013; 0.0091) and 0.0052 (95% CI -0.0078; 0.018) for k trans , and 0.046 (95% CI -0.021; 0.11) and 0.069 (95% CI -0.017; 0.15) for k ep , respectively. All perfusion parameters were not significantly different between groups (P-values: 0.32; 0.47 for k trans , and 0.24; 0.15) for k ep . However, a significant difference in variance between populations was observed for k trans (P-value 0.007)., Data Conclusion: Higher patellar bone perfusion parameters were found in patients with PFP when compared to healthy control subjects, but these differences were not statistically significant. This result, and the observed significant difference in k trans variance, warrant further research., Level of Evidence: 1 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1344-1350., (© 2018 The Authors Journal of Magnetic Resonance Imaging published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.)

T2-mapping is a widely used quantitative MRI technique in osteoarthritis research. An important challenge for its application in the context of high tibial osteotomy (HTO) is the presence of metallic fixation devices. In this study, we evaluated the possibility of performing T2-mapping after a HTO, by assessing the extent of magnetic susceptibility artifacts and the influence on T2 relaxation times caused by two commonly used fixation devices. T2-mapping with a 3D fast spin-echo sequence at three Tesla was performed on 11 human cadaveric knee joints before and after implantation of a titanium plate and screws (n = 5) or cobalt chrome staples (n = 6). Mean T2 relaxation times were calculated in six cartilage regions, located in the distal and posterior cartilage of femoral condyles and the cartilage of tibial plateaus, both medially and laterally. T2 relaxation times before and after the implantation were compared with paired t-tests and Wilcoxon rank tests. Due to the extent of the magnetic susceptibility artifact, it was not possible to segment the knee cartilage and thus calculate T2 relaxation times in the lateral weight-bearing femoral and tibial cartilage regions only in the cobalt chrome group. In all cartilage regions of the titanium implanted knees and those unaffected by artifacts due to cobalt chrome implants, T2 relaxation times did not significantly differ between the two scans. Our results suggest that accurate T2-mapping after a HTO procedure is possible in all areas after implantation of a titanium fixation device and in most areas after implantation of a cobalt chrome fixation device. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:1206-1212, 2018., (© 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.)

Purpose: To identify the optimal combination of pharmacokinetic model and arterial input function (AIF) for quantitative analysis of blood perfusion in the patellar bone using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)., Materials and Methods: This method design study used a random subset of five control subjects from an Institutional Review Board (IRB)-approved case-control study into patellofemoral pain, scanned on a 3T MR system with a contrast-enhanced time-resolved imaging of contrast kinetics (TRICKS) sequence. We systematically investigated the reproducibility of pharmacokinetic parameters for all combinations of Orton and Parker AIF models with Tofts, Extended Tofts (ETofts), and Brix pharmacokinetic models. Furthermore, we evaluated if the AIF should use literature parameters, be subject-specific, or group-specific. Model selection was based on the goodness-of-fit and the coefficient of variation of the pharmacokinetic parameters inside the patella. This extends previous studies that were not focused on the patella and did not evaluate as many combinations of arterial and pharmacokinetic models., Results: The vascular component in the ETofts model could not reliably be recovered (coefficient of variation [CV] of v p >50%) and the Brix model parameters showed high variability of up to 20% for k el across good AIF models. Compared to group-specific AIF, the subject-specific AIF's mostly had higher residual. The best reproducibility and goodness-of-fit were obtained by combining Tofts' pharmacokinetic model with the group-specific Parker AIF., Conclusion: We identified several good combinations of pharmacokinetic models and AIF for quantitative analysis of perfusion in the patellar bone. The recommended combination is Tofts pharmacokinetic model combined with a group-specific Parker AIF model., Level of Evidence: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:848-858., (© 2017 The Authors Journal of Magnetic Resonance Imaging published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.)

An automated method for registering B-mode ultrasound (US) and magnetic resonance imaging (MRI) of the carotid arteries is proposed. The registration uses geometric features, namely, lumen centerlines and lumen segmentations, which are extracted fully automatically from the images after manual annotation of three seed points in US and MRI. The registration procedure starts with alignment of the lumen centerlines using a point-based registration algorithm. The resulting rigid transformation is used to initialize a rigid and subsequent non-rigid registration procedure that jointly aligns centerlines and segmentations by minimizing a weighted sum of the Euclidean distance between centerlines and the dissimilarity between segmentations. The method was evaluated in 28 carotid arteries from eight patients and six healthy volunteers. First, the automated US lumen segmentation method was validated and optimized in a cross-validation experiment. Next, the effect of the weighting parameter of the proposed registration dissimilarity metric and the control point spacing in the non-rigid registration was evaluated. Finally, the proposed registration method was evaluated in comparison to an existing intensity-and-point-based method, a registration using only the centerlines and a registration using manual US lumen segmentations. Registration accuracy was measured in terms of the mean surface distance between manual US segmentations and the registered MRI segmentations. The average mean surface distance was 0.78 ± 0.34 mm for all subjects, 0.65 ± 0.09 mm for healthy volunteers and 0.87 ± 0.42 mm for patients. The results for the complete set were significantly better (Wilcoxon test, p < 0.01) than the results for the intensity-and-point-based method and the centerline-based registration method. We conclude that the proposed method can robustly and accurately register US and MR images of the carotid artery, allowing multimodal analysis of the carotid plaque to improve plaque assessment., (Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Image registration is typically formulated as an optimization process, which aims to find the optimal transformation parameters of a given transformation model by minimizing a cost function. Local minima may exist in the optimization landscape, which could hamper the optimization process. To eliminate local minima, smoothing the cost function would be desirable. In this paper, we investigate the use of a randomized smoothing (RS) technique for stochastic gradient descent (SGD) optimization, to effectively smooth the cost function. In this approach, Gaussian noise is added to the transformation parameters prior to computing the cost function gradient in each iteration of the SGD optimizer. The approach is suitable for both rigid and nonrigid registrations. Experiments on synthetic images, cell images, public CT lung data, and public MR brain data demonstrate the effectiveness of the novel RS technique in terms of registration accuracy and robustness., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Background and Aims: In a large stroke-free population, we sought to identify cardiovascular risk factors and carotid plaque components associated with carotid plaque burden, lumen volume and stenosis., Methods: The carotid arteries of 1562 stroke-free participants from The Rotterdam Study were imaged on a 1.5-Tesla MRI scanner. Inner and outer wall of the carotid arteries were automatically segmented and lumen volume (mm 3 ), wall volume (outer wall-inner wall) and plaque burden (wall volume/outer wall volume) (%) were quantified. Plaque components were visually determined and luminal stenosis was assessed. We analyzed associations of cardiovascular risk factors and carotid plaque components with plaque burden and lumen volumes using regression analysis., Results: We investigated 2821 carotid plaques and found that women had larger plaque burden (50.7 ± 7.8% vs. 49.2 ± 7.7%, p < 0.0001) and smaller lumen volumes (933 ± 286 mm 3 vs. 1078 ± 334 mm 3 , p < 0.0001) than men. In women, age, HDL-cholesterol and systolic blood pressure, and in men, total cholesterol, non-HDL cholesterol and statin use were independently associated with higher plaque burden and lumen volume. Furthermore, smoking and diabetes were associated with lumen volume in men (respectively p = 0.04 and p = 0.002). Intraplaque hemorrhage (IPH) and lipid were related to a larger plaque burden (OR 1.30 [1.05-1.60] and OR 1.28[1.06-1.55]). Finally, within the highest quartile of plaque burden, IPH was strongly associated with luminal stenosis independent of age, sex, plaque burden and composition (Beta = 15.2; [11.8-18.6])., Conclusions: Several cardiovascular risk factors and plaque components, in particular IPH, are associated with higher plaque burden. Carotid IPH is strongly associated with an increased luminal stenosis., (Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.)

Purpose: To determine if T1ρ mapping can be used as an alternative to delayed gadolinium-enhanced magnetic resonance imaging of cartilage (dGEMRIC) in the quantification of cartilage biochemical composition in vivo in human knees with osteoarthritis., Materials and Methods: This study was approved by the institutional review board. Written informed consent was obtained from all participants. Twelve patients with knee osteoarthritis underwent dGEMRIC and T1ρ mapping at 3.0 T before undergoing total knee replacement. Outcomes of dGEMRIC and T1ρ mapping were calculated in six cartilage regions of interest. Femoral and tibial cartilages were harvested during total knee replacement. Cartilage sulphated glycosaminoglycan (sGAG) and collagen content were assessed with dimethylmethylene blue and hydroxyproline assays, respectively. A four-dimensional multivariate mixed-effects model was used to simultaneously assess the correlation between outcomes of dGEMRIC and T1ρ mapping and the sGAG and collagen content of the articular cartilage., Results: T1 relaxation times at dGEMRIC showed strong correlation with cartilage sGAG content (r = 0.73; 95% credibility interval [CI] = 0.60, 0.83) and weak correlation with cartilage collagen content (r = 0.40; 95% CI: 0.18, 0.58). T1ρ relaxation times did not correlate with cartilage sGAG content (r = 0.04; 95% CI: -0.21, 0.28) or collagen content (r = -0.05; 95% CI = -0.31, 0.20)., Conclusion: dGEMRIC can help accurately measure cartilage sGAG content in vivo in patients with knee osteoarthritis, whereas T1ρ mapping does not appear suitable for this purpose. Although the technique is not completely sGAG specific and requires a contrast agent, dGEMRIC is a validated and robust method for quantifying cartilage sGAG content in human osteoarthritis subjects in clinical research., ((©) RSNA, 2015.)

Background: To evaluate the influence of image registration on apparent diffusion coefficient (ADC) images obtained from abdominal free-breathing diffusion-weighted MR images (DW-MRIs)., Methods: A comprehensive pipeline based on automatic three-dimensional nonrigid image registrations is developed to compensate for misalignments in DW-MRI datasets obtained from five healthy subjects scanned twice. Motion is corrected both within each image and between images in a time series. ADC distributions are compared with and without registration in two abdominal volumes of interest (VOIs). The effects of interpolations and Gaussian blurring as alternative strategies to reduce motion artifacts are also investigated., Results: Among the four considered scenarios (no processing, interpolation, blurring and registration), registration yields the best alignment scores. Median ADCs vary according to the chosen scenario: for the considered datasets, ADCs obtained without processing are 30% higher than with registration. Registration improves voxelwise reproducibility at least by a factor of 2 and decreases uncertainty (Fréchet-Cramér-Rao lower bound). Registration provides similar improvements in reproducibility and uncertainty as acquiring four times more data., Conclusion: Patient motion during image acquisition leads to misaligned DW-MRIs and inaccurate ADCs, which can be addressed using automatic registration., (© 2014 Wiley Periodicals, Inc.)

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n=30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org., (Copyright © 2015 Elsevier Inc. All rights reserved.)

In this work we present a framework for reliably detecting significant differences in quantitative magnetic resonance imaging and evaluate it with diffusion tensor imaging (DTI) experiments. As part of this framework we propose a new spatially regularized maximum likelihood estimator that simultaneously estimates the quantitative parameters and the spatially-smoothly-varying noise level from the acquisitions. The noise level estimation method does not require repeated acquisitions. We show that the amount of regularization in this method can be set a priori to achieve a desired coefficient of variation of the estimated noise level. The noise level estimate allows the construction of a Cramér-Rao-lower-bound based test statistic that reliably assesses the significance of differences between voxels within a scan or across different scans. We show that the regularized noise level estimate improves upon existing methods and results in a substantially increased precision of the uncertainty estimates of the DTI parameters. It enables correct specification of the null distribution of the test statistic and with it the test statistic obtains the highest sensitivity and specificity. The source code of the estimation framework, test statistic and experiment scripts are made available to the community.

Objectives: To assess (1) whether normal and degenerated menisci exhibit different T1GD on delayed gadolinium-enhanced MRI of the meniscus (dGEMRIM), (2) the reproducibility of dGEMRIM and (3) the correlation between meniscus and cartilage T1GD in knee osteoarthritis (OA) patients., Methods: In 17 OA patients who underwent dGEMRIM twice within 7 days, meniscus and cartilage T1GD was calculated. Meniscus pathology was evaluated on conventional MRI. T1GD in normal and degenerated menisci were compared using a Student's t-test. Reproducibility was assessed using ICCs. Pearson's correlation was calculated between meniscus and cartilage T1GD., Results: A trend towards lower T1GD in degenerated menisci (mean: 402 ms; 95% CI: 359-444 ms) compared to normal menisci (mean: 448 ms; 95% CI: 423-473 ms) was observed (p = 0.05). Meniscus T1GD ICCs were 0.85-0.90. The correlation between meniscus and cartilage T1GD was moderate in the lateral (r = 0.52-0.75) and strong in the medial compartment (r = 0.78-0.94)., Conclusions: Our results show that degenerated menisci have a clear trend towards lower T1GD compared to normal menisci. Since these results are highly reproducible, meniscus degeneration may be assessed within one delayed gadolinium-enhanced MRI simultaneously with cartilage. The strong correlation between meniscus and cartilage T1GD suggests concomitant degeneration in both tissues in OA, but also suggests that dGEMRIC may not be regarded entirely as sulphated glycosaminoglycan specific., Key Points: dGEMRIM T1 GD can possibly be used to assess meniscal degeneration; dGEMRIM yields highly reproducible meniscal T1 GD in early stage osteoarthritic patients; Concomitant degeneration of cartilage and meniscus tissue occurs in early stage osteoarthritis; dGEMRIC cannot be regarded as entirely sulphated glycosaminoglycan specific.

Because hypoperfusion of brain tissue precedes atrophy in dementia, the detection of dementia may be advanced by the use of perfusion information. Such information can be obtained noninvasively with arterial spin labeling (ASL), a relatively new MR technique quantifying cerebral blood flow (CBF). Using ASL and structural MRI, we evaluated diagnostic classification in 32 prospectively included presenile early stage dementia patients and 32 healthy controls. Patients were suspected of Alzheimer's disease (AD) or frontotemporal dementia. Classification was based on CBF as perfusion marker, gray matter (GM) volume as atrophy marker, and their combination. These markers were each examined using six feature extraction methods: a voxel-wise method and a region of interest (ROI)-wise approach using five ROI-sets in the GM. These ROI-sets ranged in number from 72 brain regions to a single ROI for the entire supratentorial brain. Classification was performed with a linear support vector machine classifier. For validation of the classification method on the basis of GM features, a reference dataset from the AD Neuroimaging Initiative database was used consisting of AD patients and healthy controls. In our early stage dementia population, the voxelwise feature-extraction approach achieved more accurate results (area under the curve (AUC) range = 86 - 91%) than all other approaches (AUC = 57 - 84%). Used in isolation, CBF quantified with ASL was a good diagnostic marker for dementia. However, our findings indicated only little added diagnostic value when combining ASL with the structural MRI data (AUC = 91%), which did not significantly improve over accuracy of structural MRI atrophy marker by itself., (Copyright © 2014 Wiley Periodicals, Inc.)

Objectives: After allogeneic stem cell transplantation (SCT), a reliable diagnosis of acute graft versus host disease (aGvHD) is essential for an early and successful treatment. It is the aim of this analysis to assess intestinal aGvHD by magnetic resonance imaging (MRI)., Methods: Prior to allogeneic SCT, 64 consecutive patients underwent abdominal MRI examination on a 3 T MR system, including axial and coronal T2w sequences and a three-dimensional dynamic T1w, contrast enhanced sequence. After SCT, 20 patients with suspected aGvHD received a second MRI as well as an endoscopic examination., Results: Nine patients suffered from histologically proven intestinal aGvHD. In eleven patients intestinal aGvHD was excluded. In all aGvHD patients typical MRI findings with long-segment bowel wall thickening--always involving the terminal ileum--with profound submucosal oedema, were detected. The bowel wall was significantly thickened in patients with intestinal aGvHD. Bowel contrast enhancement spared the submucosa while demonstrating strong mucosal hyperemia., Conclusions: In intestinal aGvHD, a characteristic MR-appearance can be detected. This MRI pattern might facilitate an early and non-invasive diagnosis of intestinal aGvHD. MRI might thus be used as a sensitive tool to rule out or support the clinical diagnosis of aGvHD., Key Points: • Acute intestinal graft versus host disease (aGvHD) can be assessed by MRI. • The aGvHD of the bowel demonstrates a characteristic MR imaging pattern. • Bowel wall shows extensive long-segment wall thickening with profound submucosal oedema. • Terminal ileum seems invariably affected; other bowel segments show variable involvement. • Colonoscopy in suspected aGvHD should include inspection of terminal ileum.

Purpose: To introduce a semiautomatic algorithm to perform the registration of free-hand B-Mode ultrasound (US) and magnetic resonance imaging (MRI) of the carotid artery., Methods: The authors' approach combines geometrical features and intensity information. The only user interaction consists of placing three seed points in US and MRI. First, the lumen centerlines are used as landmarks for point based registration. Subsequently, in a joint optimization the distance between centerlines and the dissimilarity of the image intensities is minimized. Evaluation is performed in left and right carotids from six healthy volunteers and five patients with atherosclerosis. For the validation, the authors measure the Dice similarity coefficient (DSC) and the mean surface distance (MSD) between carotid lumen segmentations in US and MRI after registration. The effect of several design parameters on the registration accuracy is investigated by an exhaustive search on a training set of five volunteers and three patients. The optimum configuration is validated on the remaining images of one volunteer and two patients., Results: On the training set, the authors achieve an average DSC of 0.74 and a MSD of 0.66 mm on volunteer data. For the patient data, the authors obtain a DSC of 0.77 and a MSD of 0.69 mm. In the independent set composed of patient and volunteer data, the DSC is 0.69 and the MSD is 0.87 mm. The experiments with different design parameters show that nonrigid registration outperforms rigid registration, and that the combination of intensity and point information is superior to approaches that use intensity or points only., Conclusions: The proposed method achieves an accurate registration of US and MRI, and may thus enable multimodal analysis of the carotid plaque.

In traditional free-form deformation (FFD) based registration, a B-spline basis function is commonly utilized to build the transformation model. As the B-spline order increases, the corresponding B-spline function becomes smoother. However, the higher-order B-spline has a larger support region, which means higher computational cost. For a given D-dimensional nth-order B-spline, an mth-order B-spline where (m < or = n) has (m +1/n + 1)D times lower computational complexity. Generally, the third-order B-spline is regarded as keeping a good balance between smoothness and computation time. A lower-order function is seldom used to construct the deformation field for registration since it is less smooth. In this research, we investigated whether lower-order B-spline functions can be utilized for efficient registration, by using a novel stochastic perturbation technique in combination with a postponed smoothing technique to higher B-spline order. Experiments were performed with 3D lung and brain scans, demonstrating that the lower-order B-spline FFD in combination with the proposed perturbation and postponed smoothing techniques even results in better accuracy and smoothness than the traditional third-order B-spline registration, while substantially reducing computational costs.

Introduction: Viscosupplementation with hyaluronic acid (HA) of osteoarthritic (OA) knee joints has a well-established positive effect on clinical symptoms. This effect, however, is only temporary and the working mechanism of HA injections is not clear. It was suggested that HA might have disease modifying properties because of its beneficial effect on cartilage sulphated glycosaminoglycan (sGAG) content. Delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) is a highly reproducible, non-invasive surrogate measure for sGAG content and hence composition of cartilage. The aim of this study was to assess whether improvement in cartilage structural composition is detected using dGEMRIC 14 weeks after 3 weekly injections with HA in patients with early-stage knee OA., Methods: In 20 early-stage knee OA patients (KLG I-II), 3D dGEMRIC at 3T was acquired before and 14 weeks after 3 weekly injections with HA. To evaluate patient symptoms, the knee injury and osteoarthritis outcome score (KOOS) and a numeric rating scale (NRS) for pain were recorded. To evaluate cartilage composition, six cartilage regions in the knee were analyzed on dGEMRIC. Outcomes of dGEMRIC, KOOS and NRS before and after HA were compared using paired t-testing. Since we performed multiple t-tests, we applied a Bonferroni-Holm correction to determine statistical significance for these analyses., Results: All KOOS subscales ('pain', 'symptoms', 'daily activities', 'sports' and 'quality of life') and the NRS pain improved significantly 14 weeks after Viscosupplementation with HA. Outcomes of dGEMRIC did not change significantly after HA compared to baseline in any of the cartilage regions analyzed in the knee., Conclusions: Our results confirm previous findings reported in the literature, showing persisting improvement in symptomatic outcome measures in early-stage knee OA patients 14 weeks after Viscosupplementation. Outcomes of dGEMRIC, however, did not change after Viscosupplementation, indicating no change in cartilage structural composition as an explanation for the improvement of clinical symptoms.

Objectives: To evaluate the effect of automated registration in delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) of the knee on the occurrence of movement artefacts on the T1 map and the reproducibility of region-of-interest (ROI)-based measurements., Methods: Eleven patients with early-stage knee osteoarthritis and ten healthy controls underwent dGEMRIC twice at 3 T. Controls underwent unenhanced imaging. ROIs were manually drawn on the femoral and tibial cartilage. T1 calculation was performed with and without registration of the T1-weighted images. Automated three-dimensional rigid registration was performed on the femur and tibia cartilage separately. Registration quality was evaluated using the square root Cramér-Rao lower bound (CRLB(σ)). Additionally, the reproducibility of dGEMRIC was assessed by comparing automated registration with manual slice-matching., Results: Automated registration of the T1-weighted images improved the T1 maps as the 90% percentile of the CRLB(σ) was significantly (P < 0.05) reduced with a median reduction of 55.8 ms (patients) and 112.9 ms (controls). Manual matching and automated registration of the re-imaged T1 map gave comparable intraclass correlation coefficients of respectively 0.89/0.90 (patients) and 0.85/0.85 (controls)., Conclusions: Registration in dGEMRIC reduces movement artefacts on T1 maps and provides a good alternative to manual slice-matching in longitudinal studies.

Quantitative information about the geometry of the carotid artery bifurcation is relevant for investigating the onset and progression of atherosclerotic disease. This paper proposes an automatic approach for quantifying the carotid bifurcation angle, carotid area ratio, carotid bulb size and the vessel tortuosity from multispectral MRI. First, the internal and external carotid centerlines are determined by finding a minimum cost path between user-defined seed points where the local costs are based on medialness and intensity. The minimum cost path algorithm is iteratively applied after curved multi-planar reformatting to refine the centerline. Second, the carotid lumen is segmented using a topology preserving geodesic active contour which is initialized by the extracted centerlines and steered by the MR intensities. Third, the bifurcation angle and vessel tortuosity are automatically extracted from the segmented lumen. The methods for centerline tracking and lumen segmentation are evaluated by comparing their accuracy to the inter- and intra-observer variability on 48 datasets (96 carotid arteries) acquired as part of a longitudinal population study. The evaluation reveals that 94 of 96 carotid arteries are segmented successfully. The distance between the tracked centerlines and the reference standard (0.33 mm) is similar to the inter-observer variation (0.32 mm). The lumen segmentation accuracy (average DSC=0.89, average mean absolute surface distance=0.31 mm) is close to the inter-observer variation (average dice=0.92, average mean surface distance=0.23 mm). The correlation coefficient of manually and automaticly derived bifurcation angle, carotid proximal area ratio, carotid proximal bulb size and vessel totuosity quantifications are close to the correlation of these measures between observers. This demonstrates that the automated method can be used for replacing manual centerline annotation and manual contour drawing for lumen segmentation in MRIs data prior to quantifying the carotid bifurcation geometry., (Copyright © 2012 Elsevier B.V. All rights reserved.)

It has been hypothesized that white matter lesions at different locations may have different etiology and clinical consequences. Several approaches for the quantification of local white matter lesion load have been proposed in the literature, most of which rely on a distinction between lesions in a periventricular region close to the ventricles and a subcortical zone further away. In this work we present a novel automated method for local white matter lesion volume quantification in magnetic resonance images. The method segments and measures the white matter lesion volume in 43 regions defined by orientation and distance to the ventricles, which allows a more spatially detailed study of lesion load. The potential of the method was demonstrated by analyzing the effect of blood pressure on the regional white matter lesion volume in 490 elderly subjects taken from a longitudinal population study. The method was also compared to two commonly used techniques to assess the periventricular and subcortical lesion load. The main finding was that high blood pressure was primarily associated with lesion load in the vascular watershed area that forms the border between the periventricular and subcortical regions. It explains the associations found for both the periventricular and subcortical load computed for the same data, and that were reported in the literature. But the proposed method can localize the region of association with greater precision than techniques that distinguish between periventricular and subcortical lesions only., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Accurate automated brain structure segmentation methods facilitate the analysis of large-scale neuroimaging studies. This work describes a novel method for brain structure segmentation in magnetic resonance images that combines information about a structure's location and appearance. The spatial model is implemented by registering multiple atlas images to the target image and creating a spatial probability map. The structure's appearance is modeled by a classifier based on Gaussian scale-space features. These components are combined with a regularization term in a Bayesian framework that is globally optimized using graph cuts. The incorporation of the appearance model enables the method to segment structures with complex intensity distributions and increases its robustness against errors in the spatial model. The method is tested in cross-validation experiments on two datasets acquired with different magnetic resonance sequences, in which the hippocampus and cerebellum were segmented by an expert. Furthermore, the method is compared to two other segmentation techniques that were applied to the same data. Results show that the atlas- and appearance-based method produces accurate results with mean Dice similarity indices of 0.95 for the cerebellum, and 0.87 for the hippocampus. This was comparable to or better than the other methods, whereas the proposed technique is more widely applicable and robust.

We present a new method for automated characterization of atherosclerotic plaque composition in ex vivo MRI. It uses MRI intensities as well as four other types of features: smoothed, gradient magnitude and Laplacian images at several scales, and the distances to the lumen and outer vessel wall. The ground truth for fibrous, necrotic and calcified tissue was provided by histology and μCT in 12 carotid plaque specimens. Semi-automatic registration of a 3D stack of histological slices and μCT images to MRI allowed for 3D rotations and in-plane deformations of histology. By basing voxelwise classification on different combinations of features, we evaluated their relative importance. To establish whether training by 3D registration yields different results than training by 2D registration, we determined plaque composition using (1) a 2D slice-based registration approach for three manually selected MRI and histology slices per specimen, and (2) an approach that uses only the three corresponding MRI slices from the 3D-registered volumes. Voxelwise classification accuracy was best when all features were used (73.3 ± 6.3%) and was significantly better than when only original intensities and distance features were used (Friedman, p < 0.05). Although 2D registration or selection of three slices from the 3D set slightly decreased accuracy, these differences were non-significant.

Purpose: To evaluate if erythromycin compromises liver-specific enhancement of gadoxetic acid; both compounds competing in organic anion transporting peptides (OATP) -mediated hepatocytic uptake., Materials and Methods: The study was approved by institutional review board. Twelve healthy subjects (nine men, three woman; mean age, 38.7 years) were examined twice by MR imaging with prior administration of NaCl solution (placebo) or 1000 mg of erythromycin following a randomized sequence. Gadoxetic acid (0.025 mmol/kg body weight) was administered 15 min after the end of infusions. Pre- and 20 min postcontrast two-dimensional gradient-recalled-echo sequences were acquired. Relative enhancements of liver parenchyma and ratio of means were calculated from signal intensity measurements. Plasma levels of gadoxetic acid and erythromycin were determined and given in geometric means and coefficients of variation (CV)., Results: Concentration of erythromycin directly after end of infusion was 13.9 mg/L (CV 14.9%). Gadolinium plasma concentrations 5 min after gadoxetic acid administration were 138.7 μmol/L (CV 20.4%) after erythromycin infusion and 129.6 μmol/L (CV 22.8%) after placebo. Mean relative enhancements of liver parenchyma were 88.1 (SD 24.9%) after erythromycin infusion and 92.6 (SD 17.9%) after placebo. Ratio of relative enhancements was 0.951 (95% confidence interval, 0.833; 1.061; statistically not significant)., Conclusion: Coadministration of erythromycin has no effect on gadoxetic acid enhanced liver MR imaging., (Copyright © 2011 Wiley-Liss, Inc.)

Brain-Computer interface technologies mean to create new communication channels between our mind and our environment, independent of the motor system, by detecting and classifying self regulation of local brain activity. BCIs can provide patients with severe paralysis a means to communicate and to live more independent lives. There has been a growing interest in using invasive recordings for BCI to improve the signal quality. This also potentially gives access to new control strategies previously inaccessible by non-invasive methods. However, before surgery, the best implantation site needs to be determined. The blood-oxygen-level dependent signal changes measured with fMRI have been shown to agree well spatially with those found with invasive electrodes, and are the best option for pre-surgical localization. We show, using real-time fMRI at 7T, that eye movement-independent visuospatial attention can be used as a reliable control strategy for BCIs. At this field strength even subtle signal changes can be detected in single trials thanks to the high contrast-to-noise ratio. A group of healthy subjects were instructed to move their attention between three (two peripheral and one central) spatial target regions while keeping their gaze fixated at the center. The activated regions were first located and thereafter the subjects were given real-time feedback based on the activity in these regions. All subjects managed to regulate local brain areas without training, which suggests that visuospatial attention is a promising new target for intracranial BCI. ECoG data recorded from one epilepsy patient showed that local changes in gamma-power can be used to separate the three classes.

We present a stochastic optimisation method for intensity-based monomodal image registration. The method is based on a Robbins-Monro stochastic gradient descent method with adaptive step size estimation, and adds a preconditioning matrix. The derivation of the pre-conditioner is based on the observation that, after registration, the deformed moving image should approximately equal the fixed image. This prior knowledge allows us to approximate the Hessian at the minimum of the registration cost function, without knowing the coordinate transformation that corresponds to this minimum. The method is validated on 3D fMRI time-series and 3D CT chest follow-up scans. The experimental results show that the preconditioning strategy improves the rate of convergence.

Background: Magnetic resonance imaging (MRI), together with histology, is widely used to diagnose and to monitor treatment in oncology. Spatial correspondence between these modalities provides information about the ability of MRI to characterize cancerous tissue. However, registration is complicated by deformations during pathological processing, and differences in scale and information content., Methodology/principal Findings: This study proposes a methodology for establishing an accurate 3D relation between histological sections and high resolution in vivo MRI tumor data. The key features of the methodology are: 1) standardized acquisition and processing, 2) use of an intermediate ex vivo MRI, 3) use of a reference cutting plane, 4) dense histological sampling, 5) elastic registration, and 6) use of complete 3D data sets. Five rat pancreatic tumors imaged by T2*-w MRI were used to evaluate the proposed methodology. The registration accuracy was assessed by root mean squared (RMS) distances between manually annotated landmark points in both modalities. After elastic registration the average RMS distance decreased from 1.4 to 0.7 mm. The intermediate ex vivo MRI and the reference cutting plane shared by all three 3D images (in vivo MRI, ex vivo MRI, and 3D histology data) were found to be crucial for the accurate co-registration between the 3D histological data set and in vivo MRI. The MR intensity in necrotic regions, as manually annotated in 3D histology, was significantly different from other histologically confirmed regions (i.e., viable and hemorrhagic). However, the viable and the hemorrhagic regions showed a large overlap in T2(*)-w MRI signal intensity., Conclusions: The established 3D correspondence between tumor histology and in vivo MRI enables extraction of MRI characteristics for histologically confirmed regions. The proposed methodology allows the creation of a tumor database of spatially registered multi-spectral MR images and multi-stained 3D histology.

Tagged magnetic resonance imaging (tMRI) is a well-known noninvasive method allowing quantitative analysis of regional heart dynamics. Its clinical use has so far been limited, in part due to the lack of robustness and accuracy of existing tag tracking algorithms in dealing with low (and intrinsically time-varying) image quality. In this paper, we propose a novel probabilistic method for tag tracking, implemented by means of Bayesian particle filtering and a trans-dimensional Markov chain Monte Carlo (MCMC) approach, which efficiently combines information about the imaging process and tag appearance with prior knowledge about the heart dynamics obtained by means of non-rigid image registration. Experiments using synthetic image data (with ground truth) and real data (with expert manual annotation) from preclinical (small animal) and clinical (human) studies confirm that the proposed method yields higher consistency, accuracy, and intrinsic tag reliability assessment in comparison with other frequently used tag tracking methods.

Atlas-based segmentation is a powerful generic technique for automatic delineation of structures in volumetric images. Several studies have shown that multi-atlas segmentation methods outperform schemes that use only a single atlas, but running multiple registrations on volumetric data is time-consuming. Moreover, for many scans or regions within scans, a large number of atlases may not be required to achieve good segmentation performance and may even deteriorate the results. It would therefore be worthwhile to include the decision which and how many atlases to use for a particular target scan in the segmentation process. To this end, we propose two generally applicable multi-atlas segmentation methods, adaptive multi-atlas segmentation (AMAS) and adaptive local multi-atlas segmentation (ALMAS). AMAS automatically selects the most appropriate atlases for a target image and automatically stops registering atlases when no further improvement is expected. ALMAS takes this concept one step further by locally deciding how many and which atlases are needed to segment a target image. The methods employ a computationally cheap atlas selection strategy, an automatic stopping criterion, and a technique to locally inspect registration results and determine how much improvement can be expected from further registrations. AMAS and ALMAS were applied to segmentation of the heart in computed tomography scans of the chest and compared to a conventional multi-atlas method (MAS). The results show that ALMAS achieves the same performance as MAS at a much lower computational cost. When the available segmentation time is fixed, both AMAS and ALMAS perform significantly better than MAS. In addition, AMAS was applied to an online segmentation challenge for delineation of the caudate nucleus in brain MRI scans where it achieved the best score of all results submitted to date.

Radiation therapy for cervical cancer can benefit from image registration in several ways, for example by studying the motion of organs, or by (partially) automating the delineation of the target volume and other structures of interest. In this paper, the registration of cervical data is addressed using mutual information (MI) of not only image intensity, but also features that describe local image structure. Three aspects of the registration are addressed to make this approach feasible. First, instead of relying on a histogram-based estimation of mutual information, which poses problems for a larger number of features, a graph-based implementation of alpha-mutual information (alpha-MI) is employed. Second, the analytical derivative of alpha-MI is derived. This makes it possible to use a stochastic gradient descent method to solve the registration problem, which is substantially faster than nonderivative-based methods. Third, the feature space is reduced by means of a principal component analysis, which also decreases the registration time. The proposed technique is compared to a standard approach, based on the mutual information of image intensity only. Experiments are performed on 93 T2-weighted MR clinical data sets acquired from 19 patients with cervical cancer. Several characteristics of the proposed algorithm are studied on a subset of 19 image pairs (one pair per patient). On the remaining data (36 image pairs, one or two pairs per patient) the median overlap is shown to improve significantly compared to standard MI from 0.85 to 0.86 for the clinical target volume (CTV, p = 2 x 10(-2)), from 0.75 to 0.81 for the bladder (p = 8 x 10(-6)), and from 0.76 to 0.77 for the rectum (p = 2 x 10(-4)). The registration error is improved at important tissue interfaces, such as that of the bladder with the CTV, and the interface of the rectum with the uterus and cervix.

An automatic method for delineating the prostate (including the seminal vesicles) in three-dimensional magnetic resonance scans is presented. The method is based on nonrigid registration of a set of prelabeled atlas images. Each atlas image is nonrigidly registered with the target patient image. Subsequently, the deformed atlas label images are fused to yield a single segmentation of the patient image. The proposed method is evaluated on 50 clinical scans, which were manually segmented by three experts. The Dice similarity coefficient (DSC) is used to quantify the overlap between the automatic and manual segmentations. We investigate the impact of several factors on the performance of the segmentation method. For the registration, two similarity measures are compared: Mutual information and a localized version of mutual information. The latter turns out to be superior (median DeltaDSC approximately equal 0.02, p < 0.01 with a paired two-sided Wilcoxon test) and comes at no added computational cost, thanks to the use of a novel stochastic optimization scheme. For the atlas fusion step we consider a majority voting rule and the "simultaneous truth and performance level estimation" algorithm, both with and without a preceding atlas selection stage. The differences between the various fusion methods appear to be small and mostly not statistically significant (p > 0.05). To assess the influence of the atlas composition, two atlas sets are compared. The first set consists of 38 scans of healthy volunteers. The second set is constructed by a leave-one-out approach using the 50 clinical scans that are used for evaluation. The second atlas set gives substantially better performance (DeltaDSC=0.04, p < 0.01), stressing the importance of a careful atlas definition. With the best settings, a median DSC of around 0.85 is achieved, which is close to the median interobserver DSC of 0.87. The segmentation quality is especially good at the prostate-rectum interface, where the segmentation error remains below 1 mm in 50% of the cases and below 1.5 mm in 75% of the cases.

Purpose: To compare the potential of magnetic resonance imaging (MRI) to multislice computed tomography (CT) for evaluating stent-graft placement in the thoracic aorta., Methods: Susceptibility artifacts in 2 different stent-graft systems (Talent and Excluder) were evaluated in vitro in 2 angulations (straight and 33 degrees curved) using 3 different MRI gradient echo sequences (True FISP, 2-dimensional FLASH, and 3-dimensional Turbo FLASH). The size of the stent-related artifact was measured, and the relative stent lumen was calculated. In vivo stent demarcation, stent patency, and additional findings were determined in 13 patients (3 Talent, 9 Excluder, and 1 combined) and compared to CT findings., Results: In vitro, both endograft systems proved to be MR compatible, with the relative stent lumen value ranging from 82% to 100% in the straight configuration; in a curved model, the relative stent lumen value ranged from 56% to 92% with the 3D Turbo FLASH sequence, which provided the smallest susceptibility artifacts. The Excluder endoprosthesis caused significant signal inhomogeneity within the stent in a curved configuration. In vivo, MRI and multislice CT showed similar results, with CT imaging slightly superior in stent demarcation and MRI better in demonstrating thrombus. CT beam hardening artifacts were pronounced in the Talent system, while the Excluder device caused significant signal inhomogeneity within the stent on magnetic resonance angiography., Conclusions: Multislice CT and contrast-enhanced MRI are fast, reliable means of providing all relevant information for surveillance of fully MR-compatible stent-grafts in the thoracic aorta.

Simple Summary: This study aims to improve the preoperative classification of nerve sheath tumors using radiomics, a method that extracts quantitative data from medical images. By analyzing MRI scans, we seek to develop a more accurate way to distinguish between different types of nerve sheath tumors before surgery. Our findings could lead to better treatment planning and outcomes for patients with these tumors. This research has the potential to enhance the diagnostic process and contribute to more personalized care for individuals with nerve sheath tumors, ultimately benefiting the medical community and patients alike. Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive soft-tissue tumors prevalent in neurofibromatosis type 1 (NF1) patients, posing a significant risk of metastasis and recurrence. Current magnetic resonance imaging (MRI) imaging lacks decisiveness in distinguishing benign peripheral nerve sheath tumors (BPNSTs) and MPNSTs, necessitating invasive biopsies. This study aims to develop a radiomics model using quantitative imaging features and machine learning to distinguish MPNSTs from BPNSTs. Clinical data and MRIs from MPNST and BPNST patients (2000–2019) were collected at a tertiary sarcoma referral center. Lesions were manually and semi-automatically segmented on MRI scans, and radiomics features were extracted using the Workflow for Optimal Radiomics Classification (WORC) algorithm, employing automated machine learning. The evaluation was conducted using a 100× random-split cross-validation. A total of 35 MPNSTs and 74 BPNSTs were included. The T1-weighted (T1w) MRI radiomics model outperformed others with an area under the curve (AUC) of 0.71. The incorporation of additional MRI scans did not enhance performance. Combining T1w MRI with clinical features achieved an AUC of 0.74. Experienced radiologists achieved AUCs of 0.75 and 0.66, respectively. Radiomics based on T1w MRI scans and clinical features show some ability to distinguish MPNSTs from BPNSTs, potentially aiding in the management of these tumors. [ABSTRACT FROM AUTHOR]

Distinguishing malignant from benign liver lesions based on magnetic resonance imaging (MRI) is an important but often challenging task, especially in noncirrhotic livers. We developed and externally validated a radiomics model to quantitatively assess T2-weighted MRI to distinguish the most common malignant and benign primary solid liver lesions in noncirrhotic livers. Data sets were retrospectively collected from three tertiary referral centers (A, B, and C) between 2002 and 2018. Patients with malignant (hepatocellular carcinoma and intrahepatic cholangiocarcinoma) and benign (hepatocellular adenoma and focal nodular hyperplasia) lesions were included. A radiomics model based on T2-weighted MRI was developed in data set A using a combination of machine learning approaches. The model was internally evaluated on data set A through cross-validation, externally validated on data sets B and C, and compared to visual scoring of two experienced abdominal radiologists on data set C. The overall data set included 486 patients (A: 187, B: 98, and C: 201). The radiomics model had a mean area under the curve (AUC) of 0.78 upon internal validation on data set A and a similar AUC in external validation (B: 0.74 and C: 0.76). In data set C, the two radiologists showed moderate agreement (Cohen's κ: 0.61) and achieved AUCs of 0.86 and 0.82. Our T2-weighted MRI radiomics model shows potential for distinguishing malignant from benign primary solid liver lesions. External validation indicated that the model is generalizable despite substantial MRI acquisition protocol differences. Pending further optimization and generalization, this model may aid radiologists in improving the diagnostic workup of patients with liver lesions. [ABSTRACT FROM AUTHOR]

Summary: Background: Body composition between prepubertal children and adolescents varies, and it is unclear how physical activity and sedentary behaviour affect adolescent body composition. Objectives: This study aimed to examine the associations of physical activity and screen time with overall and specific fat depots in the general adolescent population. Methods: In a population‐based prospective cohort study, among 3258 adolescents aged 13 years, physical activity and screen time were assessed via self‐report questionnaires. Body mass index, dual‐energy X‐ray absorptiometry‐based measures (i.e. fat mass and lean body mass) and magnetic resonance imaging–based measures (i.e. abdominal subcutaneous and visceral fat mass) were obtained. Results: After adjusting for social‐demographic and growth‐related factors, each additional hour of daily physical activity was associated with lower fat mass, abdominal visceral fat mass and higher lean body mass (all p < 0.05). However, these associations were not observed in the longitudinal analyses. Each additional hour of daily screen time was associated with higher body mass index, fat mass, abdominal subcutaneous and visceral fat mass (all p < 0.05), which were consistent with the longitudinal analyses. Conclusion: Adolescents with higher physical activity and lower screen time had lower levels of adiposity both at the general and visceral levels. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is among the top 10 burdensome diseases, with the knee the most affected joint. Magnetic resonance imaging (MRI) allows whole-knee assessment, making it ideally suited for imaging OA, considered a multitissue disease. Three-dimensional (3D) MRI enables the comprehensive assessment of OA, including quantitative morphometry of various joint tissues. Manual tissue segmentation on 3D MRI is challenging but may be overcome by advanced automated image analysis methods including artificial intelligence (AI). This review presents examples of the utility of 3D MRI for knee OA, focusing on the articular cartilage, bone, meniscus, synovium, and infrapatellar fat pad, and it highlights several applications of AI that facilitate segmentation, lesion detection, and disease classification. [ABSTRACT FROM AUTHOR]

Objective To assess the association between meniscal volume, its change over time and the development of knee OA after 30 months in overweight/obese women. Methods Data from the PRevention of knee Osteoarthritis in Overweight Females study were used. This cohort included 407 women with a BMI ≥ 27 kg/m2, free of OA-related symptoms. The primary outcome measure was incident OA after 30 months, defined by one out of the following criteria: medial or lateral joint space narrowing (JSN)  ≥ 1.0 mm, incident radiographic OA [Kellgren and Lawrence (K&L)  ≥ 2], or incident clinical OA. The secondary outcomes were either of these items separately. Menisci at both baseline and follow-up were automatically segmented to obtain meniscal volume and delta-volumes. Generalized estimating equations were used to evaluate associations between the volume measures and the outcomes. Results Medial and lateral baseline and delta-volumes were not significantly associated to the primary outcome. Lateral meniscal baseline volume was significantly associated to lateral JSN [odds ratio (OR) = 0.87; 95% CI: 0.75, 0.99], while other measures were not. Medial and lateral baseline volume were positively associated to K&L incidence (OR = 1.32 and 1.22; 95% CI: 1.15, 1.50 and 1.03, 1.45, respectively), while medial and lateral delta-volume were negatively associated to K&L incidence (OR = 0.998 and 0.997; 95% CI: 0.997, 1.000 and 0.996, 0.999, respectively). None of the meniscal measures were significantly associated to incident clinical OA. Conclusion Larger baseline meniscal volume and the decrease of meniscal volume over time were associated to the development of structural OA after 30 months in overweight and obese women. [ABSTRACT FROM AUTHOR]

Background: Identifying persons at risk for cognitive decline may aid in early detection of persons at risk of dementia and to select those that would benefit most from therapeutic or preventive measures for dementia. Objective: In this study we aimed to validate whether cognitive decline in the general population can be predicted with multivariate data using a previously proposed supervised classification method: Disease State Index (DSI). Methods: We included 2,542 participants, non-demented and without mild cognitive impairment at baseline, from the population-based Rotterdam Study (mean age 60.9 ± 9.1 years). Participants with significant global cognitive decline were defined as the 5% of participants with the largest cognitive decline per year. We trained DSI to predict occurrence of significant global cognitive decline using a large variety of baseline features, including magnetic resonance imaging (MRI) features, cardiovascular risk factors, APOE-ε4 allele carriership, gait features, education, and baseline cognitive function as predictors. The prediction performance was assessed as area under the receiver operating characteristic curve (AUC), using 500 repetitions of 2-fold cross-validation experiments, in which (a randomly selected) half of the data was used for training and the other half for testing. Results: A mean AUC (95% confidence interval) for DSI prediction was 0.78 (0.77–0.79) using only age as input feature. When using all available features, a mean AUC of 0.77 (0.75–0.78) was obtained. Without age, and with age-corrected features and feature selection on MRI features, a mean AUC of 0.70 (0.63–0.76) was obtained, showing the potential of other features besides age. Conclusion: The best performance in the prediction of global cognitive decline in the general population by DSI was obtained using only age as input feature. Other features showed potential, but did not improve prediction. Future studies should evaluate whether the performance could be improved by new features, e.g., longitudinal features, and other prediction methods. [ABSTRACT FROM AUTHOR]