To analyse delayed contrast dynamics of fibrotic lesions in interstitial lung disease (ILD) using five dimensional (5D) MRI and to correlate contrast dynamics with disease severity.

20 patients (mean age: 71 years; M:F, 13:7), with chronic fibrosing ILD: n = 12 idiopathic pulmonary fibrosis (IPF) and n = 8 non-IPF, underwent thin-section multislice CT as part of the standard diagnostic workup and additionally MRI of the lung. 2 min after contrast injection, a radial gradient echo sequence with golden-angle spacing was acquired during 5 min of free-breathing, followed by 5D image reconstruction. Disease was categorized as severe or non-severe according to CT morphological regional severity. For each patient, 10 lesions were analysed.

IPF lesions showed later peak enhancement compared to non-IPF (severe: p = 0.01, non-severe: p = 0.003). Severe lesions showed later peak enhancement compared to non-severe lesions, in non-IPF (p = 0.04), but not in IPF (p = 0.35). There was a tendency towards higher accumulation and washout rates in IPF compared to non-IPF in non-severe disease. Severe lesions had lower washout rate than non-severe ones in both IPF (p = 0.003) and non-IPF (p = 0.005). Continuous contrast agent accumulation, without washout, was found only in IPF lesions.

Contrast agent dynamics are influenced by type and severity of pulmonary fibrosis, which might enable a more thorough characterisation of disease burden. The regional impairment is of particular interest in the context of antifibrotic treatments and was characterised using a non-invasive, non-irradiating, free-breathing method.

Delayed contrast enhancement patterns allow the assessment of regional lung impairment which could represent different disease stages or phenotypes in ILD.

To develop a fast three-dimensional (3D) k-space encoding method based on spiral projection imaging (SPI) with an interleaved golden-angle approach and to validate this novel sequence on small animal models.

A disk-like trajectory, in which each disk contained spirals, was developed. The 3D encoding was performed by tilting the disks with a golden angle. The sharpness was first calculated at different T2* values. Then, the sharpness was measured on phantom using variable undersampling ratios. Finally, the sampling method was validated by whole brain time-of-flight angiography and ultrasmall superparamagnetic iron oxide (USPIO) enhanced free-breathing liver angiography on mouse.

The in vitro results demonstrated the robustness of the method for short T2* and high undersampling ratios. In vivo experiments showed the ability to properly detect small vessels in the brain with an acquisition time shorter than 1 min. Free-breathing mice liver angiography showed the insensitivity of this protocol toward motions and flow artifacts, and enabled the visualization of liver motion during breathing.

The method implemented here allowed fast 3D k-space sampling with a high undersampling ratio. Combining the advantages of center-out spirals with the flexibility of the golden angle approach could have major implications for real-time imaging. Magn Reson Med 77:1831-1840, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Accurate three-dimensional (3D) reconstruction of myocardial infarct geometry is crucial to patient-specific modeling of the heart aimed at providing therapeutic guidance in ischemic cardiomyopathy. However, myocardial infarct imaging is clinically performed using two-dimensional (2D) late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) techniques, and a method to build accurate 3D infarct reconstructions from the 2D LGE-CMR images has been lacking. The purpose of this study was to address this need.

The authors developed a novel methodology to reconstruct 3D infarct geometry from segmented low-resolution (Lo-res) clinical LGE-CMR images. Their methodology employed the so-called logarithm of odds (LogOdds) function to implicitly represent the shape of the infarct in segmented image slices as LogOdds maps. These 2D maps were then interpolated into a 3D image, and the result transformed via the inverse of LogOdds to a binary image representing the 3D infarct geometry. To assess the efficacy of this method, the authors utilized 39 high-resolution (Hi-res) LGE-CMR images, including 36 in vivo acquisitions of human subjects with prior myocardial infarction and 3 ex vivo scans of canine hearts following coronary ligation to induce infarction. The infarct was manually segmented by trained experts in each slice of the Hi-res images, and the segmented data were downsampled to typical clinical resolution. The proposed method was then used to reconstruct 3D infarct geometry from the downsampled images, and the resulting reconstructions were compared with the manually segmented data. The method was extensively evaluated using metrics based on geometry as well as results of electrophysiological simulations of cardiac sinus rhythm and ventricular tachycardia in individual hearts. Several alternative reconstruction techniques were also implemented and compared with the proposed method.

The accuracy of the LogOdds method in reconstructing 3D infarct geometry, as measured by the Dice similarity coefficient, was 82.10% ± 6.58%, a significantly higher value than those of the alternative reconstruction methods. Among outcomes of electrophysiological simulations with infarct reconstructions generated by various methods, the simulation results corresponding to the LogOdds method showed the smallest deviation from those corresponding to the manual reconstructions, as measured by metrics based on both activation maps and pseudo-ECGs.

The authors have developed a novel method for reconstructing 3D infarct geometry from segmented slices of Lo-res clinical 2D LGE-CMR images. This method outperformed alternative approaches in reproducing expert manual 3D reconstructions and in electrophysiological simulations.

To develop a three-dimensional, free-breathing, late gadolinium enhancement (3D FB-LGE) cardiovascular magnetic resonance (CMR) technique, and to compare it with clinically used two-dimensional breath-hold LGE (2D BH-LGE).

The proposed 3D FB-LGE method consisted of inversion preparation, inversion delay, fat saturation, outer volume suppression, one-dimensional projection navigators, and a segmented stack of spirals acquisition. The 3D FB-LGE and 2D BH-LGE scans were performed on 29 cardiac patients. Qualitative analysis and quantitative analysis (in patients with scar) were performed.

No significant differences were noted between the 3D FB-LGE and 2D BH-LGE data sets in terms of overall image quality score (2D: 4.69 ± 0.60 versus 3D: 4.55 ± 0.51, P = 0.46) and image artifact score (2D: 1.10 ± 0.31 versus 3D: 1.17 ± 0.38; P = 0.63). The average difference in fractional scar volume between the 3D and 2D methods was 1.9% (n = 5). Acquisition time was significantly shorter for the 3D FB-LGE over 2D BH-LGE by a factor of 2.83 ± 0.77 (P < 0.0001).

The 3D FB-LGE is a viable option for patients, particularly in acute settings or in patients who are unable to comply with breath-hold instructions. Magn Reson Med 77:1533-1543, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

To develop a 3D sampling strategy based on a stack of variable density spirals for compressive sensing MRI.

A random sampling pattern was obtained by rotating each spiral by a random angle and by delaying for few time steps the gradient waveforms of the different interleaves. A three-dimensional (3D) variable sampling density was obtained by designing different variable density spirals for each slice encoding. The proposed approach was tested with phantom simulations up to a five-fold undersampling factor. Fully sampled 3D dataset of a human knee, and of a human brain, were obtained from a healthy volunteer. The proposed approach was tested with off-line reconstructions of the knee dataset up to a four-fold acceleration and compared with other noncoherent trajectories.

The proposed approach outperformed the standard stack of spirals for various undersampling factors. The level of coherence and the reconstruction quality of the proposed approach were similar to those of other trajectories that, however, require 3D gridding for the reconstruction.

The variable density randomized stack of spirals (VDR-SoS) is an easily implementable trajectory that could represent a valid sampling strategy for 3D compressive sensing MRI. It guarantees low levels of coherence without requiring 3D gridding. Magn Reson Med 76:59-69, 2016. © 2015 Wiley Periodicals, Inc.

Accurate reconstruction of the three-dimensional (3D) geometry of a myocardial infarct from two-dimensional (2D) multi-slice image sequences has important applications in the clinical evaluation and treatment of patients with ischemic cardiomyopathy. However, this reconstruction is challenging because the resolution of common clinical scans used to acquire infarct structure, such as short-axis, late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) images, is low, especially in the out-of-plane direction. In this study, we propose a novel technique to reconstruct the 3D infarct geometry from low resolution clinical images. Our methodology is based on a function called logarithm of odds (LogOdds), which allows the broader class of linear combinations in the LogOdds vector space as opposed to being limited to only a convex combination in the binary label space. To assess the efficacy of the method, we used high-resolution LGE-CMR images of 36 human hearts in vivo, and 3 canine hearts ex vivo. The infarct was manually segmented in each slice of the acquired images, and the manually segmented data were downsampled to clinical resolution. The developed method was then applied to the downsampled image slices, and the resulting reconstructions were compared with the manually segmented data. Several existing reconstruction techniques were also implemented, and compared with the proposed method. The results show that the LogOdds method significantly outperforms all the other tested methods in terms of region overlap.