Description: Publication date: Available online 11 June 2018 Source: Magnetic Resonance Imaging Author(s): Caroline Bund, François Lefebvre, Roland Schott, Marie-Pierre Chenard, Benoît Lhermitte, Hélène Cebula, Stéphane Kremer, François Proust, Izzie-Jacques Namer Purpose The aim of this study was to study the relationship between MRSI, before and after surgery, and patient survival. The accuracy of pre-operative MRSI in differentiating low- from high-grade oligodendrogliomas (ODGs) was also studied. Methods Two hundred patients with ODG were retrospectively included in this study between 2000 and 2016. All patients underwent MRSI before any treatment or biopsy and/or after surgery for an intra-axial brain tumour. The R software was used for statistical data analysis. p  〈 0.05 was considered statistically significant. Kaplan-Meier curves were calculated for patients with low-grade ODG and high-grade ODG pre- and post-operatively, to study survival (overall survival, OS). The best threshold of each MRSI metabolite ratio was obtained using receiver operating characteristic curves (ROCs). Results One hundred patients underwent pre-operative MRSI and 170 post-operative MRSI. N -acetylaspartate (NAA), lactate (Lac), choline (Cho) and creatine (Cr) were measured. Kapan-Meier curves showed that survival was poorer for a nCho/Cr > 3.02 in the pre-operative and nCho/Cr > 2.04, Lac/Cr > 0.743 and nCho/NAA > 3.63 in the post-operative period. Post-operative MRSI predicts survival better than pre-operative MRSI. nCho/Cr and Lac/Cr distinguished low- from high-grade ODG with a good positive predictive value. Conclusion MRSI is associated with survival. It is a non-invasive tool which completes histopathology and can predict patients' prognosis, thus improving patient management.

Description: Publication date: October 2018 Source: Magnetic Resonance Imaging, Volume 52 Author(s): Vamshi K. Kotha, Djeven P. Deva, Kim A. Connelly, Michael R. Freeman, Raymond T. Yan, Iqwal Mangat, Anish Kirpalani, Joseph J. Barfett, Joanna Sloninko, Hui Ming Lin, John J. Graham, Andrew M. Crean, Laura Jimenez-Juan, Paul Dorian, Andrew T. Yan Objective Current guidelines provide left ventricular ejection fraction (LVEF) criterion for use of implantable cardioverter defibrillators (ICD) but do not specify which modality to use for measurement. We compared LVEF measurements by radionuclide ventriculography (RNV) vs cardiac MRI (CMR) in ICD candidates to assess impact on clinical decision making. Methods This single-centre study included 124 consecutive patients referred for assessment of ICD implantation who underwent RNV and CMR within 30 days for LVEF measurement. RNV and CMR were interpreted independently by experienced readers. Results Among 124 patients (age 64 ± 11 years, 77% male), median interval between CMR and RNV was 1 day; mean LVEF was 32 ± 12% by CMR and 33 ± 11% by RNV ( p  = 0.60). LVEF by CMR and RNV showed good correlation, but Bland-Altman analysis showed relatively wide limits of agreement (−12.1 to 11.4). CMR LVEF reclassified 26 (21%) patients compared to RNV LVEF (kappa = 0.58). LVEF by both modalities showed good interobserver reproducibility (ICC 0.96 and 0.94, respectively) (limits of agreement −7.27 to 5.75 and −8.63 to 6.34, respectively). Conclusion Although LVEF measurements by CMR and RNV show moderate agreement, there is frequent reclassification of patients for ICD placement based on LVEF between these modalities. Future studies should determine if a particular imaging modality for LVEF measurement may enhance ICD decision making and treatment benefit.

Description: Publication date: October 2018 Source: Magnetic Resonance Imaging, Volume 52 Author(s): Evanthia Kousi, Elizabeth A.M. O'Flynn, Marco Borri, Veronica A. Morgan, Nandita M. deSouza, Maria A. Schmidt Purpose Baseline T2* relaxation time has been proposed as an imaging biomarker in cancer, in addition to Dynamic Contrast-Enhanced (DCE) MRI and diffusion-weighted imaging (DWI) parameters. The purpose of the current work is to investigate sources of error in T2* measurements and the relationship between T2* and DCE and DWI functional parameters in breast cancer. Methods Five female volunteers and thirty-two women with biopsy proven breast cancer were scanned at 3 T, with Research Ethics Committee approval. T2* values of the normal breast were acquired from high-resolution, low-resolution and fat-suppressed gradient-echo sequences in volunteers, and compared. In breast cancer patients, pre-treatment T2*, DCE MRI and DWI were performed at baseline. Pathologically complete responders at surgery and non-responders were identified and compared. Principal component analysis (PCA) and cluster analysis (CA) were performed. Results There were no significant differences between T2* values from high-resolution, low-resolution and fat-suppressed datasets ( p  > 0.05). There were not significant differences between baseline functional parameters in responders and non-responders ( p  > 0.05). However, there were differences in the relationship between T2* and contrast-agent uptake in responders and non-responders. Voxels of similar characteristics were grouped in 5 clusters, and large intra-tumoural variations of all parameters were demonstrated. Conclusion Breast T2* measurements at 3 T are robust, but spatial resolution should be carefully considered. T2* of breast tumours at baseline is unrelated to DCE and DWI parameters and contribute towards describing functional heterogeneity of breast tumours.

Description: Publication date: Available online 7 June 2018 Source: Magnetic Resonance Imaging Author(s): Wei Wang, Ning Cao In order to accelerate magnetic resonance imaging (MRI) scanning, fast MRI technique based on compressed sensing (CS) was proposed. The shrinkage thresholding algorithm (STA) is an efficient method in related algorithms to decrease the incoherent artifacts produced by the undersampling in k-space directly. The traditional STA uses the fixed iteration step size during the reconstruction progress, and it is not conducive to accelerate the convergence speed. In order to improve global iteration efficiency, in this paper, step adaptive fast iterative shrinkage thresholding algorithm (SAFISTA) was proposed for MRI reconstruction based on STA. It used a feedback to dynamically adjust the iteration step size. The feedback parameter was calculated from the total variations (TV) of two previous iterations. It can effectively improve the efficiency of iteration. Experiments over three kinds of MR images (human head, blood vessels and knee) under different sample ratios indicated that the proposed algorithm SAFISTA showed better reconstruction performance than iterative shrinkage thresholding algorithm (ISTA), fast iterative shrinkage thresholding algorithm (FISTA) and generalized thresholding iterative algorithm (GTIA) in terms of mean square error (MSE), peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM).

Description: Publication date: October 2018 Source: Magnetic Resonance Imaging, Volume 52 Author(s): Yueqiang Zhu, Xubin Li, Fengkui Wang, Jun Zhang, Wei Li, Yan Ma, Jin Qi, Song Ren, Zhaoxiang Ye Purpose To investigate the diagnostic value of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) for discriminating axillary metastatic from non-metastatic lymph nodes (LNs) in rabbit models. Materials and methods The institutional animal care and use committee approved this study. Forty New Zealand white rabbits were randomly divided into two groups. The axillary LN models were created by inoculating VX2 cell suspension and complete Freund's adjuvant in the mammary glands of 20 female rabbits of each group, respectively. Conventional MRI and IVIM DWI were performed after animal models successfully established. Images of axillary LNs were analyzed with regard to long-axis diameter (L), short-axis diameter (S), apparent diffusion coefficient (ADC) and IVIM parameters (D, D*, f). Receiver operating characteristic analyses were conducted to determine the diagnostic performance of aforementioned criteria. Results A total of 42 metastatic and 30 non-metastatic LNs were successfully isolated. ADC and D of metastatic LNs were significantly lower than those of non-metastatic ones (all P  〈 0.001), whereas D* was statistically higher ( P  = 0.033). L, S, and f showed no significant difference between the two groups ( P  = 0.089, 0.058, 0.054, respectively). Optimal cutoff values, area under the curve, sensitivity, and specificity for differentiation were as follows: ADC = 1.101 × 10 −3  mm 2 /s, 0.886, 78.6%, 90.0%; D = 0.938 × 10 −3  mm 2 /s, 0.927, 83.3%, 93.3%; and D* = 12.635 × 10 −3  mm 2 /s, 0.657, 52.4%, 80.0%. Conclusion IVIM DWI is useful to distinguish metastatic from non-metastatic LNs in axilla. D was the most discriminative variable for predicting metastatic LNs.

Description: Publication date: Available online 6 June 2018 Source: Magnetic Resonance Imaging Author(s): Antonella Costa, Anna Ronchi, Paolo D. Pigatto, Gianpaolo Guzzi

Description: Publication date: Available online 5 June 2018 Source: Magnetic Resonance Imaging Author(s): Angshul Majumdar This work proposes a new formulation for image reconstruction based on the autoencoder framework. The work follows the adaptive approach used in prior dictionary and transform learning based reconstruction techniques. Existing autoencoder based reconstructions are non-adaptive; they are trained on a separate training set and applied on another. In this work, the autoencoder is learnt from the patches of the image it is reconstructing. Experimental studies on MRI reconstruction shows that the proposed method outperforms state-of-the-art methods in dictionary learning, transform learning and (non-adaptive) autoencoder based approaches.

Description: Publication date: October 2018 Source: Magnetic Resonance Imaging, Volume 52 Author(s): R. Kamberger, K. Göbel-Guéniot, J. Gerlach, O.G. Gruschke, J. Hennig, P. LeVan, C. Haas, J.G. Korvink MR in microscopy can non-invasively image the morphology of living tissue, which is of particular interest in studying the mammalian brain. Many studies use live animals for basic research on brain functions, disease pathogenesis, and drug development. However, in vitro systems are on the rise, due to advantages such as the absence of a blood-brain barrier, predictable pharmacokinetics, and reduced ethical restrictions. Hence, they present an inexpensive and adequate technique to answer scientific questions and to perform drug screenings. Some publications report the use of acute brain slices for MR microscopy studies, but these only permit single measurements over several hours. Repetitive MR measurements in longitudinal studies demand an MR-compatible setup which allows cultivation for several days or weeks, and hence properly functioning in vitro systems. Organotypic hippocampal slice cultures (OHSC) are a well-established and robust in vitro system which still exhibits most histological hallmarks of the hippocampal network in vivo . An MR compatible incubation platform is introduced in which OHSC are cultivated according to the interface method following Stoppini et al. In this cultivation method a tissue slice is placed onto a membrane with nutrition medium underneath and a gas atmosphere above, where the air-tissue interface perpendicular to the B 0 field induces strong artefacts. We introduce a handling protocol that suppresses these artefacts and increases signal quality significantly to acquire high resolution images of tissue slices. An additional challenge is the lack of available of MR microscopy equipment suitable for small animal scanners. A Lenz lens with an attached capacitor can dramatically increase the SNR in these cases, and wirelessly bring the detection system in close proximity to the sample without compromising the OHSC system through the introduction of wired detectors. The resultant signal gain is demonstrated by imaging a PFA-fixed brain slice with a 72 mm diameter volume coil without a Lenz lens, and with a broadband and a self-resonant Lenz lens. In our setting, the self-resonant Lenz lens increases the SNR 10-fold over using the volume coil only.

Description: Publication date: October 2018 Source: Magnetic Resonance Imaging, Volume 52 Author(s): Nicole Wake, Hersh Chandarana, Henry Rusinek, Koji Fujimoto, Linda Moy, Daniel K. Sodickson, Sungheon Gene Kim Introduction Pharmacokinetic parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) data are sensitive to acquisition and post-processing techniques, which makes it difficult to compare results obtained using different methods. In particular, one of the most important factors affecting estimation of model parameters is how to convert MRI signal intensities to contrast agent concentration. The purpose of our study was to quantitatively compare a linear signal-to-concentration conversion (LC) as an approximation and a non-linear conversion (NLC) based on the MRI signal equation, in terms of the accuracy and precision of the pharmacokinetic parameters in T 1 -weighted DCE-MRI. Materials and methods Numerical simulation studies were conducted to compare LC and NLC in terms of the accuracy and precision in contrast kinetic parameter estimation, and to evaluate their dependency on flip angle (FA), pre-contrast T 1 (T 10 ) and arterial input function (AIF). In addition, the effect of the conversion method on the diagnostic accuracy was evaluated with 36 breast lesions (19 benign and 17 malignant). Results The transfer rate (K trans ) estimated using LC and measured AIF (mAIF) were up to 38% higher than the true K trans values, while the LC K trans estimates with the presumed AIF (pAIF) were up to 7% lower than the true K trans values, when FA = 45°. When using a small FA, such as 12°, the LC K trans with pAIF had least sensitivity to the error in T 10 compared to the K trans estimated using LC with mAIF, and NLC with pAIF or mAIF. The breast DCE-MRI study showed that both LC and NLC K trans were significantly different (p 〈 0.05) between the malignant and benign lesions. The effect size between benign and malignant values as measured by Cohen's d was 1.06 for LC K trans and 1.02 for NLC K trans . Conclusion The present study results show that, when precontrast T 1 measurement is not available and a low FA is used for DCE-MRI, the uncertainty in the contrast kinetic parameter estimation can be reduced by using the LC method with pAIF, without compromising the diagnostic accuracy.

Description: Publication date: October 2018 Source: Magnetic Resonance Imaging, Volume 52 Author(s): Keerthi Sravan Ravi, Sneha Potdar, Pavan Poojar, Ashok Kumar Reddy, Stefan Kroboth, Jon-Fredrik Nielsen, Maxim Zaitsev, Ramesh Venkatesan, Sairam Geethanath Purpose To provide a single open-source platform for comprehensive MR algorithm development inclusive of simulations, pulse sequence design and deployment, reconstruction, and image analysis. Methods We integrated the “Pulseq” platform for vendor-independent pulse programming with Graphical Programming Interface (GPI), a scientific development environment based on Python. Our integrated platform, Pulseq-GPI, permits sequences to be defined visually and exported to the Pulseq file format for execution on an MR scanner. For comparison, Pulseq files using either MATLAB only (“MATLAB-Pulseq”) or Python only (“Python-Pulseq”) were generated. We demonstrated three fundamental sequences on a 1.5 T scanner. Execution times of the three variants of implementation were compared on two operating systems. Results In vitro phantom images indicate equivalence with the vendor supplied implementations and MATLAB-Pulseq. The examples demonstrated in this work illustrate the unifying capability of Pulseq-GPI. The execution times of all the three implementations were fast (a few seconds). The software is capable of user-interface based development and/or command line programming. Conclusion The tool demonstrated here, Pulseq-GPI, integrates the open-source simulation, reconstruction and analysis capabilities of GPI Lab with the pulse sequence design and deployment features of Pulseq. Current and future work includes providing an ISMRMRD interface and incorporating Specific Absorption Ratio and Peripheral Nerve Stimulation computations.