The VERDICT model distinguished tumor from benign areas, while revealing differences in microstructure descriptors such as cellular, vascular, and EES fractions. The parameters of ADC and kurtosis models also discriminated between cancer and benign regions. However, VERDICT provides more specific information that disentangles the various microstructural features underlying the changes in ADC and kurtosis. These results highlight the clinical potential of the VERDICT framework and motivate the construction of a shorter, clinically viable imaging protocol to enable larger trials leading to widespread translation of the method.

Breast tumor diagnosis requires both high spatial resolution to obtain information about tumor morphology and high temporal resolution to probe the kinetics of contrast uptake. Adaptive sampling of k-space allows images in dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) to be reconstructed at various spatial or temporal resolutions from the same dataset. However, conventional radial approaches have limited flexibility that restricts image reconstruction to predetermined resolutions. Golden-angle radial k-space sampling achieves flexibility in-plane with samples that are incremented by the golden angle, which fills two-dimensional (2D) k-space with radial spokes that have a relatively uniform angular distribution for any time interval. We extend this method to threedimensional (3D) radial sampling, or 3D-Projection Reconstruction (3D-PR) using multidimensional golden means, which are derived from modified Fibonacci sequences by an eigenvalue approach. (1) and patients with hereditary breast cancer (HBC) have an 85% lifetime risk of contracting the disease (2). Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a highly sensitive technique for detecting breast cancers (3-5), but specificity is somewhat lower (6). Diagnosis of breast lesions using DCE-MRI could potentially be made more accurate using images that have high spatial resolution to characterize lesion morphology, and high temporal resolution to probe the contrast kinetics of a lesion. However, there is an intrinsic trade-off between temporal and spatial resolution in MRI, and there is currently little agreement on an optimal balance of temporal and spatial resolution.Adaptive sampling of k-space data, first proposed by Song et al. (7), achieves both high spatial or high temporal resolution images from the same dataset by frequently sampling the central part of k-space that gives useful information regarding image contrast. Originally used for the purposes of time-resolved angiography, projection reconstruction-time-resolved imaging of contrast kinetics (PR-TRICKS) (8), is an example of a trajectory that samples 3D k-space in an adaptive manner. PR-TRICKS sampling allows both high temporal resolution (fast) images to be reconstructed from blocks of central k-space data, or high spatial resolution images to be obtained by including data from the periphery of k-space. Ramsay et al. (9) have applied PR-TRICKS imaging to breast DCE-MRI in an adaptive manner to reconstruct images at discrete temporal/spatial resolutions. Although fast images from PR-TRICKS contain useful image contrast, they are corrupted by aliasing artifacts that arise from undersampling. The temporal instability of these streaking artifacts over successive fast images can reduce the accuracy of physiological parameters derived from pharmacokinetic modelling of enhancement curves. Minimizing these undersampling artifacts is important to improving the diagnostic quality of high-temporal-resolution images.It has been shown that if the streaking artifacts are p...

PurposeIn this work we present a dual-phase diffusion tensor imaging (DTI) technique that incorporates a correction scheme for the cardiac material strain, based on 3D myocardial tagging.Methods In vivo dual-phase cardiac DTI with a stimulated echo approach and 3D tagging was performed in 10 healthy volunteers. The time course of material strain was estimated from the tagging data and used to correct for strain effects in the diffusion weighted acquisition. Mean diffusivity, fractional anisotropy, helix, transverse and sheet angles were calculated and compared between systole and diastole, with and without strain correction. Data acquired at the systolic sweet spot, where the effects of strain are eliminated, served as a reference.ResultsThe impact of strain correction on helix angle was small. However, large differences were observed in the transverse and sheet angle values, with and without strain correction. The standard deviation of systolic transverse angles was significantly reduced from 35.9±3.9° to 27.8°±3.5° (p<0.001) upon strain-correction indicating more coherent fiber tracks after correction. Myocyte aggregate structure was aligned more longitudinally in systole compared to diastole as reflected by an increased transmural range of helix angles (71.8°±3.9° systole vs. 55.6°±5.6°, p<0.001 diastole). While diastolic sheet angle histograms had dominant counts at high sheet angle values, systolic histograms showed lower sheet angle values indicating a reorientation of myocyte sheets during contraction.ConclusionAn approach for dual-phase cardiac DTI with correction for material strain has been successfully implemented. This technique allows assessing dynamic changes in myofiber architecture between systole and diastole, and emphasizes the need for strain correction when sheet architecture in the heart is imaged with a stimulated echo approach.

Fast imaging applications in magnetic resonance imaging (MRI) frequently involve undersampling of k-space data to achieve the desired temporal resolution. However, high temporal resolution images generated from undersampled data suffer from aliasing artifacts. In radial k-space sampling, this manifests as undesirable streaks that obscure image detail. Compressed sensing reconstruction has been shown to reduce such streak artifacts, based on the assumption of image sparsity. Here, compressed sensing is implemented with three different radial sampling schemes (golden-angle, bit-reversed, and random sampling), which are compared over a range of spatiotemporal resolutions. The sampling methods are implemented in static scenarios where different undersampling patterns could be compared. Results from point spread function studies, simulations, phantom and in vivo experiments show that the choice of radial sampling pattern influences the quality of the final image reconstructed by the compressed sensing algorithm. While evenly undersampled radial trajectories are best for specific temporal resolutions, golden-angle radial sampling results in the least overall error when various temporal resolutions are considered. Reduced temporal fluctuations from aliasing artifacts in golden-angle sampling translates to improved compressed sensing reconstructions overall. Magn Reson Med 67:363-377,

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