Invest Radiol

The magnetization-prepared 2 rapid acquisition gradient echo (MP2RAGE) sequence provides quantitative T1 maps in addition to high-contrast morphological images. Advanced acceleration techniques such as compressed sensing (CS) allow its acquisition time to be compatible with clinical applications. To consider its routine use in future neuroimaging protocols, the repeatability of the segmented brain structures was evaluated and compared with the standard morphological sequence (magnetization-prepared rapid gradient echo [MPRAGE]). The repeatability of the T1 measurements was also assessed. Thirteen healthy volunteers were scanned either 3 or 4 times at several days of interval, on a 3 T clinical scanner, with the 2 sequences (CS-MP2RAGE and MPRAGE), set with the same spatial resolution (0.8-mm isotropic) and scan duration (6 minutes 21 seconds). The reconstruction time of the CS-MP2RAGE outputs (including the 2 echo images, the MP2RAGE image, and the T1 map) was 3 minutes 33 seconds, using an open-source in-house algorithm implemented in the Gadgetron framework.Both precision and variability of volume measurements obtained from CAT12 and VolBrain were assessed. The T1 accuracy and repeatability were measured on phantoms and on humans and were compared with literature.Volumes obtained from the CS-MP2RAGE and the MPRAGE images were compared using Student t tests (P < 0.05 was considered significant). The CS-MP2RAGE acquisition provided morphological images of the same quality and higher contrasts than the standard MPRAGE images. Similar intravolunteer variabilities were obtained with the CS-MP2RAGE and the MPRAGE segmentations. In addition, high-resolution T1 maps were obtained from the CS-MP2RAGE. T1 times of white and gray matters and several deep gray nuclei are consistent with the literature and show very low variability (<1%). The CS-MP2RAGE can be used in future protocols to rapidly obtain morphological images and quantitative T1 maps in 3-dimensions while maintaining high repeatability in volumetry and relaxation times.Translational Research and Advanced Imaging LaboratoryDéveloppement de l'IRM ultra-rapide pour la mesure des temps de relaxation : Apllication à la thérapide guidée par IR

Magn Reson Med

To propose a quantitative 3D double-echo steady-state (DESS) sequence that offers rapid and repeatable T mapping of the human brain using different encoding schemes that account for respiratory B variation. A retrospective self-gating module was firstly implemented into the standard DESS sequence in order to suppress the respiratory artifact via data binning. A compressed-sensing trajectory (CS-DESS) was then optimized to accelerate the acquisition. Finally, a spiral Cartesian encoding (SPICCS-DESS) was incorporated to further disrupt the coherent respiratory artifact. These different versions were compared to a standard DESS sequence (fully DESS) by assessing the T distribution and repeatability in different brain regions of eight volunteers at 3 T. The respiratory artifact correction was determined to be optimal when the data was binned into seven respiratory phases. Compared to the fully DESS, T distribution was improved for the CS-DESS and SPICCS-DESS with interquartile ranges reduced significantly by a factor ranging from 2 to 12 in the caudate, putamen, and thalamus regions. In the gray and white matter areas, average absolute test-retest T differences across all volunteers were respectively 3.5 ± 2% and 3.1 ± 2.1% for the SPICCS-DESS, 4.6 ± 4.6% and 4.9 ± 5.1% for the CS-DESS, and 15% ± 13% and 7.3 ± 5.6% for the fully DESS. The SPICCS-DESS sequence's acquisition time could be reduced by half (<4 min) while maintaining its efficient T mapping. The respiratory-resolved SPICCS-DESS sequence offers rapid, robust, and repeatable 3D T mapping of the human brain, which can be especially effective for longitudinal monitoring of cerebral pathologies.Développement de l'IRM ultra-rapide pour la mesure des temps de relaxation : Apllication à la thérapide guidée par IR

Early Achilles Enthesis Involvement in a Murine Model of Spondyloarthropathy: Morphological Imaging with Ultrashort Echo-Time Sequences and Ultrasmall Superparamagnetic Iron Oxide (USPIO) Particle Evaluation in Macrophagic Detection

Purpose. To confirm the interest of 3-dimensional ultrashort echo-time (3D-UTE) sequences to assess morphologic aspects in normal and pathological Achilles entheses in a rat model of spondyloarthropathy (SpA) with histological correlations, in comparison with conventional RARE T2 Fat-Sat sequences, and, furthermore, to evaluate the feasibility of a 3D multiecho UTE sequence performed before and after the intravenous injection of ultrasmall superparamagnetic iron oxide (USPIO) particles to assess macrophagic involvement in the Achilles enthesis in the same rat model of SpA. Materials and Methods. Fourteen rats underwent in vivo MRI of the ankle at 4.7 T, including a 3D RARE T2 Fat-Sat sequence and a 3D ultrashort echo-time (UTE) sequence for morphologic assessment at baseline and day 3 after induction of an SpA model, leading to Achilles enthesopathy in the left paw (right paw serving as a control). A 3D multiecho UTE sequence was also performed at day 3 before and then 24 (4 rats) and 48 (2 rats) hours after intravenous injection of USPIO. Visual analysis and signal intensity measurements of all images were performed at different locations of the Achilles enthesis and preinsertional area. Visual analysis and T2∗ measurements were performed before and after USPIO injection, on the 3D multiecho UTE sequence in the same locations. Normal and pathological values were compared by Wilcoxon signed-rank tests. MR findings were compared against histological data. Results. 3D-UTE sequences enabled morphologic identification of the anterior fibrocartilage and posterior collagenic areas of the Achilles enthesis. Visual analysis and signal intensity measurements distinguished SpA-affected entheses from healthy ones at day 3 (P=0.02). After administration of USPIO, no differences in signals were detected. Similarly, both visual analysis and signal T2∗ measurements in the enthesis were unable to distinguish the SpA-affected tendons from healthy ones (P=0.914). Neither the normal anatomy of the enthesis nor its pathological pattern could be distinguished using the standard RARE sequence. Histology confirmed the absence of USPIO in Achilles entheses, despite marked signs of inflammation. Conclusion. Unlike conventional RARE T2 Fat-Sat sequences, 3D-UTE sequences enable morphologic assessment of normal enthesis anatomy and early detection of abnormalities in pathological conditions. However, 3D multiecho UTE sequences combined with USPIO injections with T2∗ measurements were unable to detect macrophagic involvement in these pathological conditions

Deep learning model for automatic segmentation of lungs and pulmonary metastasis in small animal MR images

Lungs are the most frequent site of metastases growth. The amount and size of pulmonary metastases acquired from MRI imaging data are the important criteria to assess the efficacy of new drugs in preclinical models. While efficient solutions both for MR imaging and the downstream automatic segmentation have been proposed for human patients, both MRI lung imaging and segmentation in preclinical animal models remains challenging due to the physiological motion (respiratory and cardiac movements), to the low amount of protons in this organ and to the particular challenge of precise segmentation of metastases. As a consequence post-mortem analysis is currently required to obtain information on metastatic volume. In this work, we have developed a complete methodological pipeline for automated analysis of lungs and metastases in mice, consisting of an MR sequence for image acquisition and a deep learning method for automatic segmentation of both lungs and metastases. On one hand, we optimized an MR sequence for mouse lung imaging with high contrast for high detection sensitivity. On the other hand we developed DeepMeta, a multiclass U-Net 3+ deep learning model to automatically segment the images. To assess if the proposed deep learning pipeline is able to provide an accurate segmentation of both lungs and pulmonary metastases, we have longitudinally imaged mice with fast- and slow-growing metastasis. Fifty-five balb/c mice were injected with two different derivatives of renal carcinoma cells. Mice were imaged with a SG-bSSFP (self-gated balanced steady state free precession) sequence at different time points after the injection of cancer cells. Both lung and metastases segmentations were manually performed by experts. DeepMeta was trained to perform lung and metastases segmentation based on the resulting ground truth annotations. Volumes of lungs and of pulmonary metastases as well as the number of metastases per mouse were measured on a separate test dataset of MR images. Thanks to the SG method, the 3D bSSFP images of lungs were artifact-free, enabling the downstream detection and serial follow-up of metastases. Moreover, both lungs and metastases segmentation was accurately performed by DeepMeta as soon as they reached the volume of ∼ 0.02 m m 3 . Thus we were able to distinguish two groups of mice in terms of number and volume of pulmonary metastases as well as in terms of the slow versus fast patterns of growth of metastases. We have shown that our methodology combining SG-bSSFP with deep learning, enables processing of the whole animal lungs and is thus a viable alternative to histology alone