Metastatic mesenteric masses of small intestinal neuroendocrine tumors (SI-NETs) are known to often cause intestinal complications. The aim of this study was to identify patients at risk to develop these complications based on routinely acquired CT scans using a standardized set of clinical criteria and radiomics. Retrospectively, CT scans of SI-NET patients with a mesenteric mass were included and systematically evaluated by five clinicians. For the radiomics approach, 1128 features were extracted from segmentations of the mesenteric mass and mesentery, after which radiomics models were created using a combination of machine learning approaches. The performances were compared to a multidisciplinary tumor board (MTB). The dataset included 68 patients (32 asymptomatic, 36 symptomatic). The clinicians had AUCs between 0.62 and 0.85 and showed poor agreement. The best radiomics model had a mean AUC of 0.77. The MTB had a sensitivity of 0.64 and specificity of 0.68. We conclude that systematic clinical evaluation of SI-NETs to predict intestinal complications had a similar performance than an expert MTB, but poor inter-observer agreement. Radiomics showed a similar performance and is objective, and thus is a promising tool to correctly identify these patients. However, further validation is needed before the transition to clinical practice.

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Data-driven disease progression models have provided important insight into the timeline of brain changes in AD phenotypes. However, their utility in predicting the progression of pre-symptomatic AD in a population-based setting has not yet been investigated. In this study, we investigated if the disease timelines constructed in a case-controlled setting, with subjects stratified according to APOE status, are generalizable to a population-based cohort, and if progression along these disease timelines is predictive of AD. Seven volumetric biomarkers derived from structural MRI were considered. We estimated APOE-specific disease timelines of changes in these biomarkers using a recently proposed method called co-initialized discriminative event-based modeling (co-init DEBM). This method can also estimate a disease stage for new subjects by calculating their position along the disease timelines. The model was trained and cross-validated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and tested on the population-based Rotterdam Study (RS) cohort. We compared the diagnostic and prognostic value of the disease stage in the two cohorts. Furthermore, we investigated if the rate of change of disease stage in RS participants with longitudinal MRI data was predictive of AD. In ADNI, the estimated disease timeslines for ϵ4 non-carriers and carriers were found to be significantly different from one another (p<0.001). The estimate disease stage along the respective timelines distinguished AD subjects from controls with an AUC of 0.83 in both APOE ϵ4 non-carriers and carriers. In the RS cohort, we obtained an AUC of 0.83 and 0.85 in ϵ4 non-carriers and carriers, respectively. Progression along the disease timelines as estimated by the rate of change of disease stage showed a significant difference (p<0.005) for subjects with pre-symptomatic AD as compared to the general aging population in RS. It distinguished pre-symptomatic AD subjects with an AUC of 0.81 in APOE ϵ4 non-carriers and 0.88 in carriers, which was better than any individual volumetric biomarker, or its rate of change, could achieve. Our results suggest that co-init DEBM trained on case-controlled data is generalizable to a population-based cohort setting and that progression along the disease timelines is predictive of the development of AD in the general population. We expect that this approach can help to identify at-risk individuals from the general population for targeted clinical trials as well as to provide biomarker based objective assessment in such trials.

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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.

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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.

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Alzheimer's disease (AD) is the most common form of dementia and is phenotypically heterogeneous. APOE is a triallelic gene which correlates with phenotypic heterogeneity in AD. In this work, we determined the effect of APOE alleles on the disease progression timeline of AD using a discriminative event-based model (DEBM). Since DEBM is a data-driven model, stratification into smaller disease subgroups would lead to more inaccurate models as compared to fitting the model on the entire dataset. Hence our secondary aim is to propose and evaluate novel approaches in which we split the different steps of DEBM into group-aspecific and group-specific parts, where the entire dataset is used to train the group-aspecific parts and only the data from a specific group is used to train the group-specific parts of the DEBM. We performed simulation experiments to benchmark the accuracy of the proposed approaches and to select the optimal approach. Subsequently, the chosen approach was applied to the baseline data of 417 cognitively normal, 235 mild cognitively impaired who convert to AD within 3 years, and 342 AD patients from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset to gain new insights into the effect of APOE carriership on the disease progression timeline of AD. In the ε4 carrier group, the model predicted with high confidence that CSF Amyloidβ₄₂ and the cognitive score of Alzheimer's Disease Assessment Scale (ADAS) are early biomarkers. Hippocampus was the earliest volumetric biomarker to become abnormal, closely followed by the CSF Phosphorylated Tau₁₈₁ (PTAU) biomarker. In the homozygous ε3 carrier group, the model predicted a similar ordering among CSF biomarkers. However, the volume of the fusiform gyrus was identified as one of the earliest volumetric biomarker. While the findings in the ε4 carrier and the homozygous ε3 carrier groups fit the current understanding of progression of AD, the finding in the ε2 carrier group did not. The model predicted, with relatively low confidence, CSF Neurogranin as one of the earliest biomarkers along with cognitive score of Mini-Mental State Examination (MMSE). Amyloid β₄₂ was found to become abnormal after PTAU. The presented models could aid understanding of the disease, and in selecting homogeneous group of presymptomatic subjects at-risk of developing symptoms for clinical trials.

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Quantitative MRI methods that estimate multiple physical parameters simultaneously often require the fitting of a computational complex signal model defined through the Bloch equations. Repeated Bloch simulations can be avoided by matching the measured signal with a precomputed signal dictionary on a discrete parameter grid (i.e. lookup table) as used in MR Fingerprinting. However, accurate estimation requires discretizing each parameter with a high resolution and consequently high computational and memory costs for dictionary generation, storage, and matching. Here, we reduce the required parameter resolution by approximating the signal between grid points through B-spline interpolation. The interpolant and its gradient are evaluated efficiently which enables a least-squares fitting method for parameter mapping. The resolution of each parameter was minimized while obtaining a user-specified interpolation accuracy. The method was evaluated by phantom and in-vivo experiments using fully-sampled and undersampled unbalanced (FISP) MR fingerprinting acquisitions. Bloch simulations incorporated relaxation effects $(\boldsymbol {T}_{\boldsymbol {1}},\boldsymbol {T}_{\boldsymbol {2}})$ , proton density $\left ({\boldsymbol {PD} }\right)$ , receiver phase ( $\boldsymbol {\varphi }_{\boldsymbol {0}}$ ), transmit field inhomogeneity ( $\boldsymbol {B}_{\boldsymbol {1}}^{\boldsymbol {+}}$ ), and slice profile. Parameter maps were compared with those obtained from dictionary matching, where the parameter resolution was chosen to obtain similar signal (interpolation) accuracy. For both the phantom and the in-vivo acquisition, the proposed method approximated the parameter maps obtained through dictionary matching while reducing the parameter resolution in each dimension ( $\boldsymbol {T}_{\boldsymbol {1}},\boldsymbol {T}_{\boldsymbol {2}},\boldsymbol {B}_{\boldsymbol {1}}^{\boldsymbol {+}}$ ) by - on average - an order of magnitude. In effect, the applied dictionary was reduced from .47GB$ to $464KB$. Furthermore, the proposed method was equally robust against undersampling artifacts as dictionary matching. Dictionary fitting with B-spline interpolation reduces the computational and memory costs of dictionary-based methods and is therefore a promising method for multi-parametric mapping.

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Multichannel image registration is an important challenge in medical image analysis. Multichannel images result from modalities such as dual-energy CT or multispectral microscopy. Besides, multichannel feature images can be derived from acquired images, for instance, by applying multiscale feature banks to the original images to register. Multichannel registration techniques have been proposed, but most of them are applicable to only two multichannel images at a time. In the present study, we propose to formulate multichannel registration as a groupwise image registration problem. In this way, we derive a method that allows the registration of two or more multichannel images in a fully symmetric manner (i.e., all images play the same role in the registration procedure), and therefore, has transitive consistency by definition. The method that we introduce is applicable to any number of multichannel images, any number of channels per image, and it allows to take into account correlation between any pair of images and not just corresponding channels. In addition, it is fully modular in terms of dissimilarity measure, transformation model, regularisation method, and optimisation strategy. For two multimodal datasets, we computed feature images from the initially acquired images, and applied the proposed registration technique to the newly created sets of multichannel images. MIND descriptors were used as feature images, and we chose total correlation as groupwise dissimilarity measure. Results show that groupwise multichannel image registration is a competitive alternative to the pairwise multichannel scheme, in terms of registration accuracy and insensitivity towards registration reference spaces.

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Event-based models (EBM) are a class of disease progression models that can be used to estimate temporal ordering of neuropathological changes from cross-sectional data. Current EBMs only handle scalar biomarkers, such as regional volumes, as inputs. However, regional aggregates are a crude summary of the underlying high-resolution images, potentially limiting the accuracy of EBM. Therefore, we propose a novel method that exploits high-dimensional voxel-wise imaging biomarkers: n-dimensional discriminative EBM (nDEBM). nDEBM is based on an insight that mixture modeling, which is a key element of conventional EBMs, can be replaced by a more scalable semi-supervised support vector machine (SVM) approach. This SVM is used to estimate the degree of abnormality of each region which is then used to obtain subject-specific disease progression patterns. These patterns are in turn used for estimating the mean ordering by fitting a generalized Mallows model. In order to validate the biomarker ordering obtained using nDEBM, we also present a framework for Simulation of Imaging Biomarkers’ Temporal Evolution (SImBioTE) that mimics neurodegeneration in brain regions. SImBioTE trains variational auto-encoders (VAE) in different brain regions independently to simulate images at varying stages of disease progression. We also validate nDEBM clinically using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). In both experiments, nDEBM using high-dimensional features gave better performance than state-of-the-art EBM methods using regional volume biomarkers. This suggests that nDEBM is a promising approach for disease progression modeling.

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Alzheimer's Disease (AD) is characterized by a cascade of biomarkers becoming abnormal, the pathophysiology of which is very complex and largely unknown. Event-based modeling (EBM) is a data-driven technique to estimate the sequence in which biomarkers for a disease become abnormal based on cross-sectional data. It can help in understanding the dynamics of disease progression and facilitate early diagnosis and prognosis by staging patients. In this work we propose a novel discriminative approach to EBM, which is shown to be more accurate than existing state-of-the-art EBM methods. The method first estimates for each subject an approximate ordering of events. Subsequently, the central ordering over all subjects is estimated by fitting a generalized Mallows model to these approximate subject-specific orderings based on a novel probabilistic Kendall's Tau distance. We also introduce the concept of relative distance between events which helps in creating a disease progression timeline. Subsequently, we propose a method to stage subjects by placing them on the estimated disease progression timeline. We evaluated the proposed method on Alzheimer's Disease Neuroimaging Initiative (ADNI) data and compared the results with existing state-of-the-art EBM methods. We also performed extensive experiments on synthetic data simulating the progression of Alzheimer's disease. The event orderings obtained on ADNI data seem plausible and are in agreement with the current understanding of progression of AD. The proposed patient staging algorithm performed consistently better than that of state-of-the-art EBM methods. Event orderings obtained in simulation experiments were more accurate than those of other EBM methods and the estimated disease progression timeline was observed to correlate with the timeline of actual disease progression. The results of these experiments are encouraging and suggest that discriminative EBM is a promising approach to disease progression modeling.

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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.

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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.

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In image registration the optimal transformation parameters of a given transformation model are typically obtained by minimizing a cost function. Stochastic gradient descent (SGD) is an efficient optimization algorithm for image registration. In SGD optimization, stochastic approximations of the cost function derivative are used in each iteration to update the transformation parameters. The stochastic approximation error leads to large variance in the parameters. To enforce convergence nonetheless, SGD methods are typically implemented in combination with a gradually decreasing update step size. However, selecting a proper sequence of step sizes is a major challenge in practice. An alternative strategy in numerical optimization is to use a constant step size and enforce convergence by averaging the parameters obtained by SGD over several iterations. It was proven mathematically that the highest possible rate of convergence is achieved in this way. Inspired by this work, we propose an averaged SGD (Avg-SGD) method for efficient image registration. In the Avg-SGD approach, a constant step size is used, in combination with an exponentially weighted iterate averaging scheme. Experiments on 3D lung CT scans demonstrate the effectiveness of the Avg-SGD method in terms of convergence rate, accuracy and precision.@en

Both normal aging and neurodegenerative disorders such as Alzheimer's disease (AD) cause morphological changes of the brain. It is generally difficult to distinguish these two causes of morphological change by visual inspection of magnetic resonance (MR) images. To facilitate making this distinction and thus aid the diagnosis of neurodegenerative disorders, we propose a method for developing a spatio-temporal model of morphological differences in the brain due to normal aging. The method utilizes groupwise image registration to characterize morphological variation across brain scans of people with different ages. To extract the deformations that are due to normal aging we use partial least squares regression, which yields modes of deformations highly correlated with age, and corresponding scores for each input subject. Subsequently, we determine a distribution of morphologies as a function of age by fitting smooth percentile curves to these scores. This distribution is used as a reference to which a person's morphology score can be compared. We validate our method on two different datasets, using images from both cognitively normal subjects and patients with Alzheimer disease (AD). Results show that the proposed framework extracts the expected atrophy patterns. Moreover, the morphology scores of cognitively normal subjects are on average lower than the scores of AD subjects, indicating that morphology differences between AD subjects and healthy subjects can be partly explained by accelerated aging. With our methods we are able to assess accelerated brain aging on both population and individual level. A spatio-temporal aging brain model derived from 988 T1-weighted MR brain scans from a large population imaging study (age range 45.9–91.7y, mean age 68.3y) is made publicly available at www.agingbrain.nl.@en

Multimodal image fusion for image guidance in minimally invasive liver interventions generally requires the registration of pre-operatively acquired images with interventional images of the patient. Whereas rigid registration approaches are fast and can be used in an interventional setting, the actual liver deformation may be nonrigid. The purpose of this paper is to assess the magnitude of nonrigid deformation of the liver between pre-operative and interventional CT images in the case of tumor ablations, over the full liver and over parts of the liver that match the volumes typically imaged by a 3D ultrasound transducer. We acquired 3D abdominal CT scans of 38 patients that had undergone the radiofrequency ablation of liver tumors, pre-operative CT images as well as intraoperative CT images. To determine the magnitude of liver deformation due to pose changes and respiration, we nonrigidly registered the pre-operative CT scan with the intraoperative CT scan. By fitting the deformation to a rigid transformation in the region of interest and computing the residual displacements, the nonrigid deformation part can be quantified. We performed quantifications over the complete liver, as well as for two volumes of interest representative of sub-xiphoidal and intercostal 3D ultrasound acquisitions. The results showed that a substantial amount of nonrigid deformation was found, and rotation of the patient's pose and deep inhalation caused significant liver deformation. Hence we concluded that nonrigid motion correction in the interventions should be taken into account.

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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.

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Image registration is an important task in medical image analysis. Whereas most methods are designed for the registration of two images (pairwise registration), there is an increasing interest in simultaneously aligning more than two images using groupwise registration. Multimodal registration in a groupwise setting remains difficult, due to the lack of generally applicable similarity metrics. In this work, a novel similarity metric for such groupwise registration problems is proposed. The metric calculates the sum of the conditional entropy between each image in the group and a representative template image constructed iteratively using principal component analysis. The proposed metric is validated in extensive experiments on synthetic and intrasubject clinical image data. These experiments showed equivalent or improved registration accuracy compared to other state-of-the-art (dis)similarity metrics and improved transformation consistency compared to pairwise mutual information.

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Correct diagnosis of the liver tumor phenotype is crucial for treatment planning, especially the distinction between malignant and benign lesions. Clinical practice includes manual scoring of the tumors on Magnetic Resonance (MR) images by a radiologist. As this is challenging and subjective, it is often followed by a biopsy. In this study, we propose a radiomics approach as an objective and non-invasive alternative for distinguishing between malignant and benign phenotypes. T2-weighted (T2w) MR sequences of 119 patients from multiple centers were collected. We developed an efficient semi-automatic segmentation method, which was used by a radiologist to delineate the tumors. Within these regions, features quantifying tumor shape, intensity, texture, heterogeneity and orientation were extracted. Patient characteristics and semantic features were added for a total of 424 features. Classification was performed using Support Vector Machines (SVMs). The performance was evaluated using internal random-split cross-validation. On the training set within each iteration, feature selection and hyperparameter optimization were performed. To this end, another cross validation was performed by splitting the training sets in training and validation parts. The optimal settings were evaluated on the independent test sets. Manual scoring by a radiologist was also performed. The radiomics approach resulted in 95% confidence intervals of the AUC of [0.75, 0.92], specificity [0.76, 0.96] and sensitivity [0.52, 0.82]. These approach the performance of the radiologist, which were an AUC of 0.93, specificity 0.70 and sensitivity 0.93. Hence, radiomics has the potential to predict the liver tumor benignity in an objective and non-invasive manner.

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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.

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The most widespread technique used to register sets of medical images consists of selecting one image as fixed reference, to which all remaining images are successively registered. This pairwise scheme requires one optimization procedure per pair of images to register. Pairwise mutual information is a common dissimilarity measure applied to a large variety of datasets. Alternative methods, called groupwise registrations, have been presented to register two or more images in a single optimization procedure, without the need of a reference image. Given the success of mutual information in pairwise registration, we adapt one of its multivariate versions, called total correlation, in a groupwise context. We justify the choice of total correlation among other multivariate versions of mutual information, and provide full implementation details. The resulting total correlation measure is remarkably close to measures previously proposed by Huizinga et al. based on principal component analysis. Our experiments, performed on five quantitative imaging datasets and on a dynamic CT imaging dataset, show that total correlation yields registration results that are comparable to Huizinga’s methods. Total correlation has the advantage of being theoretically justified, while the measures of Huizinga et al. were designed empirically. Additionally, total correlation offers an alternative to pairwise mutual information on quantitative imaging datasets.

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Multimodal groupwise registration has been of growing interest to the image processing community due to developments in scanner technologies (e.g. multiparametric MRI, DCE-CT or PET-MR) that increased both the number of modalities and number of images under consideration. In this work a novel methodology is presented for multimodal groupwise registration that is based on Laplacian eigenmaps, a nonlinear dimensionality reduction technique. Compared to recently proposed dissimilarity metrics based on principal component analysis, the proposed metric should enable a better capture of the intensity relationships between different images in the group. The metric is constructed to be the second smallest eigenvalue from the eigenvector problem defined in Laplacian eigenmaps. The method was validated in three distinct experiments: a non-linear synthetic registration experiment, the registration of quantitative MRI data of the carotid artery, and the registration of multimodal data of the brain (RIRE). The results show increased accuracy and robustness compared to other state-of-the-art groupwise registration methodologies.

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