Utility of cardiac MRI to diagnose myocardial ischemia and fibrosis in dogs with cardiomegaly secondary to myxomatous mitral valve disease.

OBJECTIVE: To assess whether cardiac MRI or various biomarkers can be used to detect myocardial ischemia and fibrosis in dogs with cardiomegaly secondary to myxomatous mitral valve disease (MMVD). ANIMALS: 6 dogs with cardiomegaly secondary to naturally occurring stage B2 MMVD being treated only with pimobendan with or without enalapril and 6 control dogs with no cardiac disease. All dogs were ≥ 5 years old with no systemic illness. PROCEDURES: Serum cardiac troponin I and concentrations were measured, and dogs were anesthetized for cardiac MRI with ECG-triggered acquisition of native T1- and T2-weighted images. Gadolinium contrast was administered to evaluate myocardial perfusion and late gadolinium enhancement (LGE). Mean T1 and T2 values and regions of LGE were measured with dedicated software. Extracellular volume (ECV) was estimated on the basis of Hct and T1 values of myocardium and surrounding blood. Subjective analysis for myocardial perfusion deficits was performed. RESULTS: Dogs with MMVD had significantly (P = .013) higher cardiac troponin I concentrations than control dogs, but galectin-3 concentrations did not differ (P = .08) between groups. Myocardial fibrosis was detected in 4 dogs with MMVD and 3 control dogs; no dogs had obvious myocardial perfusion deficits. Native T1 and T2 values, postcontrast T1 values, and ECV values were not significantly different between groups (all P > .3). CLINICAL RELEVANCE: Results suggest that some dogs with cardiomegaly secondary to MMVD may not have clinically relevant myocardial fibrosis.

Early Stopping in Experimentation With Real-Time Functional Magnetic Resonance Imaging Using a Modified Sequential Probability Ratio Test.

Introduction: Functional magnetic resonance imaging (fMRI) often involves long scanning durations to ensure the associated brain activity can be detected. However, excessive experimentation can lead to many undesirable effects, such as from learning and/or fatigue effects, discomfort for the subject, excessive motion artifacts and loss of sustained attention on task. Overly long experimentation can thus have a detrimental effect on signal quality and accurate voxel activation detection. Here, we propose dynamic experimentation with real-time fMRI using a novel statistically driven approach that invokes early stopping when sufficient statistical evidence for assessing the task-related activation is observed. Methods: Voxel-level sequential probability ratio test (SPRT) statistics based on general linear models (GLMs) were implemented on fMRI scans of a mathematical 1-back task from 12 healthy teenage subjects and 11 teenage subjects born extremely preterm (EPT). This approach is based on likelihood ratios and allows for systematic early stopping based on target statistical error thresholds. We adopt a two-stage estimation approach that allows for accurate estimates of GLM parameters before stopping is considered. Early stopping performance is reported for different first stage lengths, and activation results are compared with full durations. Finally, group comparisons are conducted with both early stopped and full duration scan data. Numerical parallelization was employed to facilitate completion of computations involving a new scan within every repetition time (TR). Results: Use of SPRT demonstrates the feasibility and efficiency gains of automated early stopping, with comparable activation detection as with full protocols. Dynamic stopping of stimulus administration was achieved in around half of subjects, with typical time savings of up to 33% (4 min on a 12 min scan). A group analysis produced similar patterns of activity for control subjects between early stopping and full duration scans. The EPT group, individually, demonstrated more variability in location and extent of the activations compared to the normal term control group. This was apparent in the EPT group results, reflected by fewer and smaller clusters. Conclusion: A systematic statistical approach for early stopping with real-time fMRI experimentation has been implemented. This dynamic approach has promise for reducing subject burden and fatigue effects.

Transforming UTE-mDixon MR Abdomen-Pelvis Images Into CT by Jointly Leveraging Prior Knowledge and Partial Supervision.

Computed tomography (CT) provides information for diagnosis, PET attenuation correction (AC), and radiation treatment planning (RTP). Disadvantages of CT include poor soft tissue contrast and exposure to ionizing radiation. While MRI can overcome these disadvantages, it lacks the photon absorption information needed for PET AC and RTP. Thus, an intelligent transformation from MR to CT, i.e., the MR-based synthetic CT generation, is of great interest as it would support PET/MR AC and MR-only RTP. Using an MR pulse sequence that combines ultra-short echo time (UTE) and modified Dixon (mDixon), we propose a novel method for synthetic CT generation jointly leveraging prior knowledge as well as partial supervision (SCT-PK-PS for short) on large-field-of-view images that span abdomen and pelvis. Two key machine learning techniques, i.e., the knowledge-leveraged transfer fuzzy c-means (KL-TFCM) and the Laplacian support vector machine (LapSVM), are used in SCT-PK-PS. The significance of our effort is threefold: 1) Using the prior knowledge-referenced KL-TFCM clustering, SCT-PK-PS is able to group the feature data of MR images into five initial clusters of fat, soft tissue, air, bone, and bone marrow. Via these initial partitions, clusters needing to be refined are observed and for each of them a few additionally labeled examples are given as the partial supervision for the subsequent semi-supervised classification using LapSVM; 2) Partial supervision is usually insufficient for conventional algorithms to learn the insightful classifier. Instead, exploiting not only the given supervision but also the manifold structure embedded primarily in numerous unlabeled data, LapSVM is capable of training multiple desired tissue-recognizers; 3) Benefiting from the joint use of KL-TFCM and LapSVM, and assisted by the edge detector filter based feature extraction, the proposed SCT-PK-PS method features good recognition accuracy of tissue types, which ultimately facilitates the good transformation from MR images to CT images of the abdomen-pelvis. Applying the method on twenty subjects' feature data of UTE-mDixon MR images, the average score of the mean absolute prediction deviation (MAPD) of all subjects is 140.72 ± 30.60 HU which is statistically significantly better than the 241.36 ± 21.79 HU obtained using the all-water method, the 262.77 ± 42.22 HU obtained using the four-cluster-partitioning (FCP, i.e., external-air, internal-air, fat, and soft tissue) method, and the 197.05 ± 76.53 HU obtained via the conventional SVM method. These results demonstrate the effectiveness of our method for the intelligent transformation from MR to CT on the body section of abdomen-pelvis.

mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification.

We propose a new method for generating synthetic CT images from modified Dixon (mDixon) MR data. The synthetic CT is used for attenuation correction (AC) when reconstructing PET data on abdomen and pelvis. While MR does not intrinsically contain any information about photon attenuation, AC is needed in PET/MR systems in order to be quantitatively accurate and to meet qualification standards required for use in many multi-center trials. Existing MR-based synthetic CT generation methods either use advanced MR sequences that have long acquisition time and limited clinical availability or use matching of the MR images from a newly scanned subject to images in a library of MR-CT pairs which has difficulty in accounting for the diversity of human anatomy especially in patients that have pathologies. To address these deficiencies, we present a five-phase interlinked method that uses mDixon MR acquisition and advanced machine learning methods for synthetic CT generation. Both transfer fuzzy clustering and active learning-based classification (TFC-ALC) are used. The significance of our efforts is fourfold: 1) TFC-ALC is capable of better synthetic CT generation than methods currently in use on the challenging abdomen using only common Dixon-based scanning. 2) TFC partitions MR voxels initially into the four groups regarding fat, bone, air, and soft tissue via transfer learning; ALC can learn insightful classifiers, using as few but informative labeled examples as possible to precisely distinguish bone, air, and soft tissue. Combining them, the TFC-ALC method successfully overcomes the inherent imperfection and potential uncertainty regarding the co-registration between CT and MR images. 3) Compared with existing methods, TFC-ALC features not only preferable synthetic CT generation but also improved parameter robustness, which facilitates its clinical practicability. Applying the proposed approach on mDixon-MR data from ten subjects, the average score of the mean absolute prediction deviation (MAPD) was 89.78±8.76 which is significantly better than the 133.17±9.67 obtained using the all-water (AW) method (p=4.11E-9) and the 104.97±10.03 obtained using the four-cluster-partitioning (FCP, i.e., external-air, internal-air, fat, and soft tissue) method (p=0.002). 4) Experiments in the PET SUV errors of these approaches show that TFC-ALC achieves the highest SUV accuracy and can generally reduce the SUV errors to 5% or less. These experimental results distinctively demonstrate the effectiveness of our proposed TFCALC method for the synthetic CT generation on abdomen and pelvis using only the commonly-available Dixon pulse sequence.

UTE-mDixon-based thorax synthetic CT generation.

PURPOSE: Accurate photon attenuation assessment from MR data remains an unmet challenge in the thorax due to tissue heterogeneity and the difficulty of MR lung imaging. As thoracic tissues encompass the whole physiologic range of photon absorption, large errors can occur when using, for example, a uniform, water-equivalent or a soft-tissue-only approximation. The purpose of this study was to introduce a method for voxel-wise thoracic synthetic CT (sCT) generation from MR data attenuation correction (AC) for PET/MR or for MR-only radiation treatment planning (RTP). METHODS: Acquisition: A radial stack-of-stars combining ultra-short-echo time (UTE) and modified Dixon (mDixon) sequence was optimized for thoracic imaging. The UTE-mDixon pulse sequence collects MR signals at three TE times denoted as UTE, Echo1, and Echo2. Three-point mDixon processing was used to reconstruct water and fat images. Bias field correction was applied in order to avoid artifacts caused by inhomogeneity of the MR magnetic field. ANALYSIS: Water fraction and R2* maps were estimated using the UTE-mDixon data to produce a total of seven MR features, that is UTE, Echo1, Echo2, Dixon water, Dixon fat, Water fraction, and R2*. A feature selection process was performed to determine the optimal feature combination for the proposed automatic, 6-tissue classification for sCT generation. Fuzzy c-means was used for the automatic classification which was followed by voxel-wise attenuation coefficient assignment as a weighted sum of those of the component tissues. Performance evaluation: MR data collected using the proposed pulse sequence were compared to those using a traditional two-point Dixon approach. Image quality measures, including image resolution and uniformity, were evaluated using an MR ACR phantom. Data collected from 25 normal volunteers were used to evaluate the accuracy of the proposed method compared to the template-based approach. Notably, the template approach is applicable here, that is normal volunteers, but may not be robust enough for patients with pathologies. RESULTS: The free breathing UTE-mDixon pulse sequence yielded images with quality comparable to those using the traditional breath holding mDixon sequence. Furthermore, by capturing the signal before T2* decay, the UTE-mDixon image provided lung and bone information which the mDixon image did not. The combination of Dixon water, Dixon fat, and the Water fraction was the most robust for tissue clustering and supported the classification of six tissues, that is, air, lung, fat, soft tissue, low-density bone, and dense bone, used to generate the sCT. The thoracic sCT had a mean absolute difference from the template-based (reference) CT of less than 50 HU and which was better agreement with the reference CT than the results produced using the traditional Dixon-based data. CONCLUSION: MR thoracic acquisition and analyses have been established to automatically provide six distinguishable tissue types to generate sCT for MR-based AC of PET/MR and for MR-only RTP.

Perforator Phase Contrast Angiography of Deep Inferior Epigastric Perforators: A Better Preoperative Imaging Tool for Flap Surgery than Computed Tomographic Angiography?

OBJECTIVE: The aim of this study was to demonstrate the feasibility of in vivo perforator visualization by a newly proposed magnetic resonance-based perforator phase contrast angiography (pPCA) technique for deep inferior epigastric perforator (DIEP) flap surgery and to prospectively compare its image quality and clinical value with computed tomographic angiography (CTA), the state-of-the-art perforator imaging technique. MATERIALS AND METHODS: Institutional review board approval and informed consent were obtained. DIEP pPCA and CTA data were acquired in 10 female patients before DIEP flap surgery. Image findings were compared between the two techniques and with literature reports. RESULTS: The overall image quality is negatively correlated with patient BMI for CTA, but positively correlated with BMI for pPCA. Compared with CTA, pPCA has significantly better image quality (P = 0.005), signal-to-noise ratio (P < 0.001), and contrast-to-noise ratio (perforator-to-muscle, P < 0.001; perforator-to-fat, P = 0.014). It also has preferable clinical value ratings, although not statistically significant (P = 0.388). There is a good agreement (84%) between perforators detected by pPCA and CTA. Perforator location deviations between pPCA and CTA are compatible with the precision required for plastic surgery. Perforator size measured by pPCA seems to be more accurate than CTA, as it is 0.8 ± 0.3 mm smaller (P < 0.001), consistent with the reported 0.5 to 1.2 mm overestimation by CTA. There is no significant difference in perforator intramuscular course assessment (P = 0.415). CONCLUSIONS: The developed magnetic resonance-based pPCA technique presents superior image quality, better vessel contrast, and more accurate perforator anatomy than the x-ray-based CTA. pPCA has the potential to emerge as the preferred preoperative planning tool for perforator flap reconstructive surgery.

Effect of parallel radiofrequency transmission on arterial input function selection in dynamic contrast-enhanced 3 Tesla pelvic MRI.

BACKGROUND: To evaluate whether parallel radiofrequency transmission (mTX) can improve the symmetry of the left and right femoral arteries in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of prostate and bladder cancer. METHODS: Eighteen prostate and 24 bladder cancer patients underwent 3.0 Tesla DCE-MRI scan with a single transmission channel coil. Subsequently, 21 prostate and 21 bladder cancer patients were scanned using the dual channel mTX upgrade. The precontrast signal ( S0) and the maximum enhancement ratio (MER) were measured in both the left and the right femoral arteries. Within the patient cohort, the ratio of S0 and MER in the left artery to that in the right artery ( S0_LR, MER_LR) was calculated with and without the use of mTX. Left to right asymmetry indices for S0 ( S0_LRasym) and MER ( MER_LRasym) were defined as the absolute values of the difference between S0_LR and 1, and the difference between MER_LR and 1, respectively. RESULTS: S0_LRasym, and MER_LRasym were 0.21 and 0.19 for prostate cancer patients with mTX, and 0.43 and 0.45 for the ones imaged without it (P < 0.001). Also, for the bladder cancer patients, S0_LRasym, and MER_LRasym were 0.11 and 0.9 with mTX, while imaging without it yielded 0.52 and 0.39 (P < 0.001). CONCLUSION: mTX can significantly improve left-to-right symmetry of femoral artery precontrast signal and contrast enhancement.

Fast T2*-weighted MRI of the prostate at 3 Tesla.

PURPOSE: To describe a rapid T2*-weighted (T2*W), three-dimensional (3D) echo planar imaging (EPI) sequence and its application in mapping local magnetic susceptibility variations in 3 Tesla (T) prostate MRI. To compare the sensitivity of T2*W EPI with routinely used T1-weighted turbo-spin echo sequence (T1W TSE) in detecting hemorrhage and the implications on sequences sensitive to field inhomogeneities such as MR spectroscopy (MRS). MATERIALS AND METHODS: B(0) susceptibility weighted mapping was performed using a 3D EPI sequence featuring a 2D spatial excitation pulse with gradients of spiral k-space trajectory. A series of 11 subjects were imaged using 3T MRI and combination endorectal (ER) and six-channel phased array cardiac coils. T1W TSE and T2*W EPI sequences were analyzed quantitatively for hemorrhage contrast. Point resolved spectroscopy (PRESS MRS) was performed and data quality was analyzed. RESULTS: Two types of susceptibility variation were identified: hemorrhagic and nonhemorrhagic T2*W-positive areas. Post-biopsy hemorrhage lesions showed on average five times greater contrast on the T2*W images than T1W TSE images. Six nonhemorrhage regions of severe susceptibility artifact were apparent on the T2*W images that were not seen on standard T1W or T2W images. All nonhemorrhagic susceptibility artifact regions demonstrated compromised spectral quality on 3D MRS. CONCLUSION: The fast T2*W EPI sequence identifies hemorrhagic and nonhemorrhagic areas of susceptibility variation that may be helpful in prostate MRI planning at 3.0T.

Approach to abdominal imaging at 1.5 Tesla and optimization at 3 Tesla.

This article reviews fundamental principles and sequence techniques that have been used successfully for imaging diseases of the abdomen and pelvis at 1.5 Tesla. This article also introduces concepts and the specific alteration of sequence parameters for optimization of abdominal-pelvic imaging at 3 Tesla.