A standard system phantom for magnetic resonance imaging.

PURPOSE: A standard MRI system phantom has been designed and fabricated to assess scanner performance, stability, comparability and assess the accuracy of quantitative relaxation time imaging. The phantom is unique in having traceability to the International System of Units, a high level of precision, and monitoring by a national metrology institute. Here, we describe the phantom design, construction, imaging protocols, and measurement of geometric distortion, resolution, slice profile, signal-to-noise ratio (SNR), proton-spin relaxation times, image uniformity and proton density. METHODS: The system phantom, designed by the International Society of Magnetic Resonance in Medicine ad hoc committee on Standards for Quantitative MR, is a 200 mm spherical structure that contains a 57-element fiducial array; two relaxation time arrays; a proton density/SNR array; resolution and slice-profile insets. Standard imaging protocols are presented, which provide rapid assessment of geometric distortion, image uniformity, T1 and T2 mapping, image resolution, slice profile, and SNR. RESULTS: Fiducial array analysis gives assessment of intrinsic geometric distortions, which can vary considerably between scanners and correction techniques. This analysis also measures scanner/coil image uniformity, spatial calibration accuracy, and local volume distortion. An advanced resolution analysis gives both scanner and protocol contributions. SNR analysis gives both temporal and spatial contributions. CONCLUSIONS: A standard system phantom is useful for characterization of scanner performance, monitoring a scanner over time, and to compare different scanners. This type of calibration structure is useful for quality assurance, benchmarking quantitative MRI protocols, and to transition MRI from a qualitative imaging technique to a precise metrology with documented accuracy and uncertainty.

Quantitative magnetic resonance imaging phantoms: A review and the need for a system phantom.

The MRI community is using quantitative mapping techniques to complement qualitative imaging. For quantitative imaging to reach its full potential, it is necessary to analyze measurements across systems and longitudinally. Clinical use of quantitative imaging can be facilitated through adoption and use of a standard system phantom, a calibration/standard reference object, to assess the performance of an MRI machine. The International Society of Magnetic Resonance in Medicine AdHoc Committee on Standards for Quantitative Magnetic Resonance was established in February 2007 to facilitate the expansion of MRI as a mainstream modality for multi-institutional measurements, including, among other things, multicenter trials. The goal of the Standards for Quantitative Magnetic Resonance committee was to provide a framework to ensure that quantitative measures derived from MR data are comparable over time, between subjects, between sites, and between vendors. This paper, written by members of the Standards for Quantitative Magnetic Resonance committee, reviews standardization attempts and then details the need, requirements, and implementation plan for a standard system phantom for quantitative MRI. In addition, application-specific phantoms and implementation of quantitative MRI are reviewed. Magn Reson Med 79:48-61, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Automatic atlas-based three-label cartilage segmentation from MR knee images.

Osteoarthritis (OA) is the most common form of joint disease and often characterized by cartilage changes. Accurate quantitative methods are needed to rapidly screen large image databases to assess changes in cartilage morphology. We therefore propose a new automatic atlas-based cartilage segmentation method for future automatic OA studies. Atlas-based segmentation methods have been demonstrated to be robust and accurate in brain imaging and therefore also hold high promise to allow for reliable and high-quality segmentations of cartilage. Nevertheless, atlas-based methods have not been well explored for cartilage segmentation. A particular challenge is the thinness of cartilage, its relatively small volume in comparison to surrounding tissue and the difficulty to locate cartilage interfaces - for example the interface between femoral and tibial cartilage. This paper focuses on the segmentation of femoral and tibial cartilage, proposing a multi-atlas segmentation strategy with non-local patch-based label fusion which can robustly identify candidate regions of cartilage. This method is combined with a novel three-label segmentation method which guarantees the spatial separation of femoral and tibial cartilage, and ensures spatial regularity while preserving the thin cartilage shape through anisotropic regularization. Our segmentation energy is convex and therefore guarantees globally optimal solutions. We perform an extensive validation of the proposed method on 706 images of the Pfizer Longitudinal Study. Our validation includes comparisons of different atlas segmentation strategies, different local classifiers, and different types of regularizers. To compare to other cartilage segmentation approaches we validate based on the 50 images of the SKI10 dataset.

Diseased region detection of longitudinal knee MRI data.

Statistical analysis of longitudinal cartilage changes in osteoarthritis (OA) is of great importance and still a challenge in knee MRI data analysis. A major challenge is to establish a reliable correspondence across subjects within the same latent subpopulations. We develop a novel Gaussian hidden Markov model (GHMM) to establish spatial correspondence of cartilage thinning across both time and subjects within the same latent subpopulations and make statistical inference on the detection of diseased regions in each OA patient. A hidden Markov random field (HMRF) is proposed to extract such latent subpopulation structure. The EM algorithm and pseudo-likelihood method are both considered in making statistical inference. The proposed model can effectively detect diseased regions and present a localized analysis of longitudinal cartilage thickness within each latent subpopulation. Simulation studies and diseased region detection on 2D thickness maps extracted from full 3D longitudinal knee MRI Data for Pfizer Longitudinal Dataset are performed, which show that our proposed model outperforms standard voxel-based analysis.

AUTOMATIC MULTI-ATLAS-BASED CARTILAGE SEGMENTATION FROM KNEE MR IMAGES.

In this paper, we propose a multi-atlas-based method to automatically segment the femoral and tibial cartilage from T1 weighted magnetic resonance (MR) knee images. The segmentation result is a joint decision of the spatial priors from a multi-atlas registration and the local likelihoods within a Bayesian framework. The cartilage likelihoods are obtained from a probabilistic k nearest neighbor classification. Validation results on 18 knee MR images against the manual expert segmentations from a dataset acquired for osteoarthritis research show good performance for the segmentation of femoral and tibial cartilage (mean Dice similarity coefficient of 75.2% and 81.7% respectively).

Automatic Atlas-based Three-label Cartilage Segmentation from MR Knee Images.

This paper proposes a method to build a bone-cartilage atlas of the knee and to use it to automatically segment femoral and tibial cartilage from T1 weighted magnetic resonance (MR) images. Anisotropic spatial regularization is incorporated into a three-label segmentation framework to improve segmentation results for the thin cartilage layers. We jointly use the atlas information and the output of a probabilistic k nearest neighbor classifier within the segmentation method. The resulting cartilage segmentation method is fully automatic. Validation results on 18 knee MR images against manual expert segmentations from a dataset acquired for osteoarthritis research show good performance for the segmentation of femoral and tibial cartilage (mean Dice similarity coefficient of 78.2% and 82.6% respectively).

Dynamic contrast-enhanced magnetic resonance imaging as a predictor of clinical outcome in canine spontaneous soft tissue sarcomas treated with thermoradiotherapy.

PURPOSE: This study tests whether dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters obtained from canine patients with soft tissue sarcomas, treated with hyperthermia and radiotherapy, are predictive of therapeutic outcome. EXPERIMENTAL DESIGN: Thirty-seven dogs with soft tissue sarcomas had DCE-MRI done before and following the first hyperthermia. Signal enhancement for tumor and reference muscle were fitted empirically, yielding a washin/washout rate for the contrast agent and tumor area under the signal enhancement curve (AUC) calculated from 0 to 60 seconds, 90 seconds, and the time of maximal enhancement in the reference muscle. These parameters were then compared with local tumor control, metastasis-free survival, and overall survival. RESULTS: Pretherapy rate of contrast agent washout was positively predictive of improved overall and metastasis-free survival with hazard ratio of 0.67 (P = 0.015) and 0.68 (P = 0.012), respectively. After the first hyperthermia washin rate, AUC60, AUC90, and AUCt-max were predictive of improved overall and metastasis-free survival with hazard ratio ranging from 0.46 to 0.53 (P < 0.002) and 0.44 to 0.55 (P < 0.004), respectively. DCE-MRI parameters were compared with extracellular pH and (31)P MR spectroscopy results (previously published) in the same patients showing a correlation. This suggested that an increase in perfusion after therapy was effective in eliminating excess acid from the tumor. CONCLUSIONS: This study shows that DCE-MRI has utility predicting overall and metastasis-free survival in canine patients with soft tissue sarcomas. To our knowledge, this is the first time that DCE-MRI parameters are predictive of clinical outcome for soft tissue sarcomas.

Time-lapse mapping of cortical changes in schizophrenia with different treatments.

Using time-lapse maps, we visualized the dynamics of schizophrenia progression, revealing spreading cortical changes that depend on the type of antipsychotic treatment. Dynamic, 4-dimensional models of disease progression were created from 4 repeated high-resolution brain magnetic resonance imaging scans of 36 first-episode schizophrenia patients (30 men/6 women; mean age: 24.2 +/- 5.1 SD years) randomized to haloperidol (HAL) (n = 15) or olanzapine (OLZ) treatment (n = 21), imaged at baseline, 3, 6, and 12 months (144 scans). Based on surface-based cortical models and point-by-point measures of gray matter volume, we generated time-lapse maps for each treatment. Disease trajectories differed for atypical versus typical neuroleptic drugs. A rapidly advancing parietal-to-frontal deficit trajectory, in HAL-treated patients, mirrored normal cortical maturation but greatly intensified. The disease trajectory advanced even after symptom normalization, involving the frontal cortex within 12 months with typical drug treatment. Areas with fastest tissue loss shifted anteriorly in the first year of psychosis. This trajectory was not seen with OLZ. Whether this association reflects either reduced neurotoxicity or neuroprotection cannot be addressed with neuroimaging; changes may relate to glial rather than neural components. These maps revise current models of schizophrenia progression; due to power limitations, the findings require confirmation in a sample large enough to model group x time interactions.

Antipsychotic drug effects on brain morphology in first-episode psychosis.

BACKGROUND: Pathomorphologic brain changes occurring as early as first-episode schizophrenia have been extensively described. Longitudinal studies have demonstrated that these changes may be progressive and associated with clinical outcome. This raises the possibility that antipsychotics might alter such pathomorphologic progression in early-stage schizophrenia. OBJECTIVE: To test a priori hypotheses that olanzapine-treated patients have less change over time in whole brain gray matter volumes and lateral ventricle volumes than haloperidol-treated patients and that gray matter and lateral ventricle volume changes are associated with changes in psychopathology and neurocognition. DESIGN: Longitudinal, randomized, controlled, multisite, double-blind study. Patients treated and followed up for up to 104 weeks. Neurocognitive and magnetic resonance imaging (MRI) assessments performed at weeks 0 (baseline), 12, 24, 52, and 104. Mixed-models analyses with time-dependent covariates evaluated treatment effects on MRI end points and explored relationships between MRI, psychopathologic, and neurocognitive outcomes. SETTING: Fourteen academic medical centers (United States, 11; Canada, 1; Netherlands, 1; England, 1). PARTICIPANTS: Patients with first-episode psychosis (DSM-IV) and healthy volunteers. INTERVENTIONS: Random allocation to a conventional antipsychotic, haloperidol (2-20 mg/d), or an atypical antipsychotic, olanzapine (5-20 mg/d). MAIN OUTCOME MEASURES: Brain volume changes assessed by MRI. RESULTS: Of 263 randomized patients, 161 had baseline and at least 1 postbaseline MRI evaluation. Haloperidol-treated patients exhibited significant decreases in gray matter volume, whereas olanzapine-treated patients did not. A matched sample of healthy volunteers (n = 58) examined contemporaneously showed no change in gray matter volume. CONCLUSIONS: Patients with first-episode psychosis exhibited a significant between-treatment difference in MRI volume changes. Haloperidol was associated with significant reductions in gray matter volume, whereas olanzapine was not. Post hoc analyses suggested that treatment effects on brain volume and psychopathology of schizophrenia may be associated. The differential treatment effects on brain morphology could be due to haloperidol-associated toxicity or greater therapeutic effects of olanzapine.

Duration of illness and treatment effects on hippocampal volume in male patients with schizophrenia.

BACKGROUND: Reduced hippocampal volume is a consistently described structural abnormality in schizophrenia but its cause and timing are not known. AIMS: To examine the relationship of duration of schizophrenic illness and treatment effects with hippocampal volumes. METHOD: Quantitative 1.5 T magnetic resonance imaging brain scans of young male patients in the early stage of schizophrenic illness were compared with those of chronically ill older patients. Scans were also acquired for controls matched to both patient groups for age and handedness. Duration of illness was recorded and severity of symptoms assessed with the Positive and Negative Syndrome Scale. RESULTS: The patients with schizophrenia had smaller hippocampal volumes than the controls. The volume reduction was larger in older patients than in young, compared with age-matched controls. In the early illness group atypical antipsychotics rather than haloperidol were associated with larger hippocampal volumes even after controlling for differences in illness severity. CONCLUSIONS: The greater reduction of hippocampal volume in people with chronic v. early illness, after controlling for illness severity and age, supports the hypothesis of progressive hippocampal reduction in males with schizophrenia. Atypical antipsychotics early in illness may protect against this.