Purpose: This study aimed to evaluate comprehensively; accuracy, repeatability and reproducibility of T1 and T2 relaxation times measured by magnetic resonance fingerprinting using B1+-corrected fast imaging with steady-state precession (FISP–MRF).
Methods: The International Society of Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) phantom was scanned for 100 days, and six healthy volunteers for 5 days using a FISP–MRF prototype sequence. Accuracy was evaluated on the phantom by comparing relaxation times measured by FISP–MRF with the reference values provided by the phantom manufacturer. Daily repeatability was characterized as the coefficient of variation (CV) of the measurements over 100 days for the phantom and over 5 days for volunteers. In addition, the cross-scanner reproducibility was evaluated in volunteers.
Results: In the phantom study, T1 and T2 values from FISP–MRF showed a strong linear correlation with the reference values of the phantom (R2 = 0.9963 for T1; R2 = 0.9966 for T2). CVs were <1.0% for T1 values larger than 300 ms, and <3.0% for T2 values across a wide range. In the volunteer study, CVs for both T1 and T2 values were <5.0%, except for one subject. In addition, all T2 values estimated by FISP–MRF in vivo were lower than those measured with conventional mapping sequences reported in previous studies. The cross-scanner variation of T1 and T2 showed good agreement between two different scanners in the volunteers.
Conclusion: B1+-corrected FISP-MRF showed an acceptable accuracy, repeatability and reproducibility in the phantom and volunteer studies.
Purpose: To evaluate the feasibility of an empirical mathematical model (EMM) to fit dynamic contrast-enhanced MRI (DCE-MRI) data of hand and wrist synovitis and whether parameters of EMM are significantly correlated with clinical disease activity in patients with rheumatoid arthritis (RA).
Methods: Thirty-one consecutive patients with RA prospectively underwent Institutional Review Board (IRB)-approved DCE-MRI scans with temporal resolution of 20 s using a 1.5T system. ROIs were placed where the highest signal increase was observed and the kinetic curves were analyzed using an EMM: ΔS(t) = A(1 − e−α t) e−βt, where ΔS is relative enhancement, t is time from when the signal increase was first observed, starting from baseline (ΔS = 0), A is the upper limit of signal intensity, α (s−1) is the rate of signal increase, and β (s−1) is the rate of signal decrease during washout. The initial slope of the kinetic curve (Aα), the initial area under the curve (AUC30), the time at which the kinetic curve reached its peak (Tpeak) and the signal enhancement ratio (SER) defined as the change in signal intensity between the initial and delayed time points (t = 60 and 300 s, respectively) were calculated. RA magnetic resonance imaging scores (RAMRIS) with and without contrast media were evaluated. These parameters or scores were compared with the Disease Activity Score (DAS) 28-erythrocyte sedimentation rate (ESR).
Results: A showed a significant correlation with DAS28-ESR (r = 0.58; P = 0.0005). β, AUC30 and Tpeak were also significantly correlated with DAS28-ESR with a lesser degree (r = 0.49; P = 0.0051, r = 0.50; P = 0.0038 and r = −0.51; P = 0.0028, respectively), whereas α, Aα, SER and RAMRIS were not.
Conclusion: EMM could fit the DCE-MRI data of hand and wrist synovitis. AUC30 obtained from the uptake phase of the kinetic curve as well as A, β and Tpeak obtained throughout the kinetic curve might be effective to predict the clinical disease activity.
Purpose: Increased use of deep convolutional neural networks (DCNNs) in medical imaging diagnosis requires determinate evaluation of diagnostic performance. We performed the fundamental investigation of diagnostic performance of DCNNs using the detection task of brain metastasis.
Methods: We retrospectively investigated AlexNet and GoogLeNet using 3117 positive and 37961 negative MRI images with and without metastasis regarding (1) diagnostic biases, (2) the optimal K number of K-fold cross validations (K-CVs), (3) the optimal positive versus negative image ratio, (4) the accuracy improvement curves, (5) the accuracy range prediction by the bootstrap method, and (6) metastatic lesion detection by regions with CNNs (R-CNNs).
Results: Respectively, AlexNet and GoogLeNet had (1) 50 ± 4.6% and 50 ± 4.9% of the maximal mean ± 95% confidence intervals (95% CIs) measured with equal-sized negative versus negative image datasets and positive versus positive image datasets, (2) no less than 10 and 4 of K number in K-CVs fell within the respective maximum biases of 4.6% or 4.9%, (3) 74% of the highest accuracy with equal positive versus negative image ratio dataset and 91% of that with four times of negative-to-positive image ratio dataset, (4) the accuracy improvement curves increasing from 69% to 74% and 73% to 88% as positive versus negative pairs of the training images increased from 500 to 2495, (5) at least nine and six out of 10-CV result sets essential to predict the accuracy ranges by the bootstrap method, and (6) 50% and 45% of metastatic lesion detection accuracies by R-CNNs.
Conclusions: Our research presented methodological fundamentals to evaluate diagnostic features in the visual recognition of DCNNs. Our series will help to conduct the accuracy investigation of computer diagnosis in medical imaging.
Purpose: To test whether our proposed denoising approach with deep learning-based reconstruction (dDLR) can effectively denoise brain MR images.
Methods: In an initial experimental study, we obtained brain images from five volunteers and added different artificial noise levels. Denoising was applied to the modified images using a denoising convolutional neural network (DnCNN), a shrinkage convolutional neural network (SCNN), and dDLR. Using these brain MR images, we compared the structural similarity (SSIM) index and peak signal-to-noise ratio (PSNR) between the three denoising methods. Two neuroradiologists assessed the image quality of the three types of images. In the clinical study, we evaluated the denoising effect of dDLR in brain images with different levels of actual noise such as thermal noise. Specifically, we obtained 2D-T2-weighted image, 2D-fluid-attenuated inversion recovery (FLAIR) and 3D-magnetization-prepared rapid acquisition with gradient echo (MPRAGE) from 15 healthy volunteers at two different settings for the number of image acquisitions (NAQ): NAQ2 and NAQ5. We reconstructed dDLR-processed NAQ2 from NAQ2, then compared with SSIM and PSNR. Two neuroradiologists separately assessed the image quality of NAQ5, NAQ2 and dDLR-NAQ2. Statistical analysis was performed in the experimental and clinical study. In the clinical study, the inter-observer agreement was also assessed.
Results: In the experimental study, PSNR and SSIM for dDLR were statistically higher than those of DnCNN and SCNN (P < 0.001). The image quality of dDLR was also superior to DnCNN and SCNN. In the clinical study, dDLR-NAQ2 was significantly better than NAQ2 images for SSIM and PSNR in all three sequences (P < 0.05), except for PSNR in FLAIR. For all qualitative items, dDLR-NAQ2 had equivalent or better image quality than NAQ5, and superior quality to that of NAQ2 (P < 0.05), for all criteria except artifact. The inter-observer agreement ranged from substantial to near perfect.
Conclusion: dDLR reduces image noise while preserving image quality on brain MR images.
Purpose: Numerous classification systems have been proposed to analyze lumbar spine MRI scans. When evaluating these systems, most studies draw their conclusions from measurements of experienced clinicians. The aim of this study was to evaluate the impact of specific measurement training on interobserver reliability in MRI classification of the lumbar spine.
Methods: Various measurement and classification systems were assessed for their interobserver reliability in 30 MRIs from patients with chronic lumbar back and sciatic pain. Two observers were experienced spine surgeons. The third observer was an inexperienced medical student who, prior to the study measurements, in addition to being given the detailed written instructions also given to the surgeons, obtained a list of 20 reference measurements in MRI scans from other patients to practice with.
Results: Excellent agreement was observed between the medical student and the spine surgeon who had also created the reference measurements. Between the two spine surgeons, agreement was markedly lower in all systems investigated (e.g., antero-posterior spinal canal diameter intraclass correlation coefficient [ICC] [3.1] = 0.979 vs. ICC [3.1] = 0.857).
Conclusion: These data warrant the creation of publicly available standardised measurement examples of accepted classification systems to increase reliability of the interpretation of MR images.
Purpose: Intravoxel incoherent motion (IVIM) analysis has attracted the interest of the clinical community due to its close relationship with microperfusion. Nevertheless, there is no clear reference protocol for its implementation; one of the questions being which b-value distribution to use. This study aimed to stress the importance of the sampling scheme and to show that an optimized b-value distribution decreases the variance associated with IVIM parameters in the brain with respect to a regular distribution in healthy volunteers.
Methods: Ten volunteers were included in this study; images were acquired on a 1.5T MR scanner. Two distributions of 16 b-values were used: one considered ‘regular’ due to its close association with that used in other studies, and the other considered ‘optimized’ according to previous studies. IVIM parameters were adjusted according to the bi-exponential model, using two-step method. Analysis was undertaken in ROI defined using in the Automated Anatomical Labeling atlas, and parameters distributions were compared in a total of 832 ROI.
Results: Maps with fewer speckles were obtained with the ‘optimized’ distribution. Coefficients of variation did not change significantly for the estimation of the diffusion coefficient D but decreased by approximately 39% for the pseudo-diffusion coefficient estimation and by 21% for the perfusion fraction. Distributions of adjusted parameters were found significantly different in 50% of the cases for the perfusion fraction, in 80% of the cases for the pseudo-diffusion coefficient and 17% of the cases for the diffusion coefficient. Observations across brain areas show that the range of average values for IVIM parameters is smaller in the ‘optimized’ case.
Conclusion: Using an optimized distribution, data are sampled in a way that the IVIM signal decay is better described and less variance is obtained in the fitted parameters. The increased precision gained could help to detect small variations in IVIM parameters.
Purpose: The aim of this study was to generate a multivariate model using various MRI markers of blood flow and vascular permeability and accumulation of 18F-fluorodeoxyglucose (FDG) to predict the extent of hypoxia in an 18F-fluoromisonidazole (FMISO)-positive region.
Methods: Fifteen patients aged 27–74 years with brain tumors (glioma, n = 13; lymphoma, n = 1; germinoma, n = 1) were included. MRI scans were performed using a 3T scanner, and dynamic contrast-enhanced (DCE) perfusion and arterial spin labeling images were obtained. Ktrans and Vp maps were generated using the DCE images. FDG and FMISO positron emission tomography scans were also obtained. A model for predicting FMISO positivity was generated on a voxel-by-voxel basis by a multivariate logistic regression model using all the MRI parameters with and without FDG. Receiver-operating characteristic curve analysis was used to detect FMISO positivity with multivariate and univariate analysis of each parameter. Cross-validation was performed using the leave-one-out method.
Results: The area under the curve (AUC) was highest for the multivariate prediction model with FDG (0.892) followed by the multivariate model without FDG and univariate analysis with FDG and Ktrans (0.844 for all). In cross-validation, the multivariate model with FDG had the highest AUC (0.857 ± 0.08) followed by the multivariate model without FDG (0.834 ± 0.119).
Conclusion: A multivariate prediction model created using blood flow, vascular permeability, and glycometabolism parameters can predict the extent of hypoxia in FMISO-positive areas in patients with brain tumors.
Purpose: To characterize the non-laminar flow dynamics and resultant decreased wall shear stress (WSS) and high oscillatory shear index (OSI) of the infrarenal abdominal aortic dilatation, cardiac phase-resolved 3D phase-contrast MRI (4D-flow MRI) was performed.
Methods: The prospective single-arm study was approved by the Institutional Review Board and included 18 subjects (median 67.5 years) with the dilated infrarenal aorta (median diameter 35 mm). 4D-flow MRI was conducted on a 1.5T MRI system. On 3D streamline images, laminar and non-laminar (i.e., vortex or helical) flow patterns were visually assessed both for the dilated aorta and for the undilated upstream aorta. Cardiac phase-resolved flow velocities, WSS and OSI, were also measured for the dilated aorta and the upstream undilated aorta.
Results: Non-laminar flow represented by vortex or helical flow was more frequent and overt in the dilated aorta than in the undilated upstream aorta (P < 0.0156) with a very good interobserver agreement (weighted kappa: 0.82–1.0). The WSS was lower, and the OSI was higher on the dilated aortic wall compared with the proximal undilated segments. In mid-systole, mean spatially-averaged WSS was 0.20 ± 0.016 Pa for the dilated aorta vs. 0.68 ± 0.071 Pa for undilated upstream aorta (P < 0.0001), and OSI on the dilated aortic wall was 0.093 ± 0.010 vs. 0.041 ± 0.0089 (P = 0.013). The maximum values and the amplitudes of the WSS at the dilated aorta were inversely proportional to the ratio of dilated/undilated aortic diameter (r = −0.694, P = 0.0014).
Conclusion: 4D-flow can characterize abnormal non-laminar flow dynamics within the dilated aorta in vivo. The wall of the infrarenal aortic dilatation is continuously and increasingly affected by atherogenic stimuli due to the flow disturbances represented by vortex or helical flow, which is reflected by lower WSS and higher OSI.
This study proposes an accurate method for creating a dictionary for magnetic resonance fingerprinting (MRF) using a fast Bloch image simulator. An MRF sequence based on a fast imaging with steady precession sequence and a numerical phantom were used for dictionary generation. Cartesian and spiral readout gradients were used for the Bloch image simulation. The validity and usefulness of the method for accurate dictionary creation were demonstrated by MRF parameter maps obtained by pattern matching with the dictionaries generated by the proposed method.
We compared 3 Tesla (3T) compressed sensing (CS)-MRI of different pulse sequences with various acceleration factors to standard fast spin-echo (FSE) sequences in terms of time, quality, and inter-reader agreement. Each sequence was qualitatively ranked and then qualitatively scored for blurring, artifact, low contrast detection, noise pattern, signal-to-noise ratio, and overall quality. The CS-MRI sequences demonstrated very good overall quality compared with routine FSE sequences with overall good inter-reader agreement.
In this article, a new method of information extraction on the basis of the differentiation of T1- and T2-weighted MR images is proposed. It relies on a technique of superposition of T1- and T2-weighted MR images with use of statistical dominance algorithm. On the basis of implemented image analysis, a reproducible extraction of growth zone of adolescent boys’ wrists is possible.
We analyzed the correlations between the T2 shift and integrated electromyographic (iEMG) values in the masseter and temporal muscles. Six healthy adults engaged in a clenching task over two durations at various bite forces. We evaluated the mean T2 shift per voxel and assessed their correlations with iEMG using a linear mixed model. The regression coefficients were different for each muscle type, similar for the left and right sides, and decreased upon doubling duration.
We investigated the usefulness of diffusion-weighted imaging (DWI) for detecting changes in the structure of hypoxic cells by evaluating the correlation between 18F-fluoroazomycin arabinoside (FAZA) positron emission tomography activity and DWI parameters in head and neck carcinoma. The diffusion coefficient corresponding to the slow compartment of a two-compartment model had a significant positive correlation with FAZA activity (ρ = 0.58, P = 0.016), whereas the diffusional kurtosis from diffusion kurtosis imaging had a significant negative correlation (ρ = −0.62, P = 0.008), which suggests that those DWI parameters might be useful as indicators for changes in cell structure.
Few studies had been published regarding imaging findings of skin adnexal tumors. We experienced two giant cases of them with a characteristic mushroom-like growth pattern. MRI showed a circumscribed mushroom-like shaped mass extruding from the subcutaneous tissue with microcystic lesions. Although differentiation between benignancy and malignancy may be difficult by radiological examinations, MRI may be helpful to identify its origin and differentiate soft tissue tumors with skin adnexal tumors in having these imaging findings.