Linearity and bias of proton density fat fraction across the full dynamic range of 0-100%: a multiplatform, multivendor phantom study using 1.5T and 3T MRI at two sites.

OBJECTIVE: Performance assessments of quantitative determinations of proton density fat fraction (PDFF) have largely focused on the range between 0 and 50%. We evaluate PDFF in a two-site phantom study across the full 0-100% PDFF range. MATERIALS AND METHODS: We used commercially available 3D chemical-shift-encoded water-fat MRI sequences from three MRI system vendors at 1.5T and 3T and conducted the study across two sites. A spherical phantom housing 18 vials spanning the full 0-100% PDFF range was used. Data at each site were acquired using default parameters to determine same-day and different-day intra-scanner repeatability, and inter-system and inter-site reproducibility, in addition to linear regression between reference and measured PDFF values. RESULTS: Across all systems, results demonstrated strong linearity and minimal bias. For 1.5T systems, a pooled slope of 0.99 with a 95% confidence interval (CI) of 0.981-0.997 and a pooled intercept of 0.61% PDFF with a 95% CI of 0.17-1.04 were obtained. Results for pooled 3T data included a slope of 1.00 (95% CI 0.995-1.005) and an intercept of 0.69% PDFF (95% CI 0.39-0.97). Inter-site and inter-system reproducibility coefficients ranged from 2.9 to 6.2 (in units of PDFF), while intra-scanner same-day and different-day repeatability ranged from 0.6 to 7.8. DISCUSSION: PDFF across the 0-100% range can be reliably estimated using current commercial offerings at 1.5T and 3T.

Synthetic generation of DSC-MRI-derived relative CBV maps from DCE MRI of brain tumors.

PURPOSE: Perfusion MRI with gadolinium-based contrast agents is useful for diagnosis and treatment response evaluation of brain tumors. Dynamic susceptibility contrast (DSC) MRI and dynamic contrast enhanced (DCE) MRI are two gadolinium-based contrast agent perfusion imaging techniques that provide complementary information about the tumor vasculature. However, each requires a separate administration of a gadolinium-based contrast agent. The purpose of this retrospective study was to determine the feasibility of synthesizing relative cerebral blood volume (rCBV) maps, as computed from DSC MRI, from DCE MRI of brain tumors. METHODS: One hundred nine brain-tumor patients underwent both DCE and DSC MRI. Relative CBV maps were computed from the DSC MRI, and blood plasma volume fraction maps were computed from the DCE MRIs. Conditional generative adversarial networks were developed to synthesize rCBV maps from the DCE MRIs. Tumor-to-white matter ratios were calculated from real rCBV, synthetic rCBV, and plasma volume fraction maps and compared using correlation analysis. Real and synthetic rCBV in white and gray matter regions were also compared. RESULTS: Pearson correlation analysis showed that both the tumor rCBV and tumor-to-white matter ratios in the synthetic and real rCBV maps were strongly correlated (ρ = 0.87, P < .05 and ρ = 0.86, P < .05, respectively). Tumor plasma volume fraction and real rCBV were not strongly correlated (ρ = 0.47). Bland-Altman analysis showed a mean difference between the synthetic and real rCBV tumor-to-white matter ratios of 0.20 with a 95% confidence interval of ±0.47. CONCLUSION: Realistic rCBV maps can be synthesized from DCE MRI and contain quantitative information, enabling robust brain-tumor perfusion imaging of DSC and DCE parameters with a single gadolinium-based contrast agent administration.

Effect of brain normalization methods on the construction of functional connectomes from resting-state functional MRI in patients with gliomas.

PURPOSE: Spatial normalization is an essential step in resting-state functional MRI connectomic analysis with atlas-based parcellation, but brain lesions can confound it. Cost-function masking (CFM) is a popular compensation approach, but may not benefit modern normalization methods. This study compared three normalization methods with and without CFM and determined their impact on connectomic measures in patients with glioma. METHODS: Fifty patients with glioma were included. T1 -weighted images were normalized using three different methods in SPM12, with and without CFM, which were then overlaid on the ICBM152 template and scored by two neuroradiologists. The Dice coefficient of gray-matter correspondence was also calculated. Normalized resting-state functional MRI data were parcellated using the AAL90 atlas to construct an individual connectivity matrix and calculate connectomic measures. The R2 among the different normalization methods was calculated for the connectivity matrices and connectomic measures. RESULTS: The older method (Original) performed significantly worse than the modern methods (Default and DARTEL; P < .005 in observer ranking). The use of CFM did not significantly improve the normalization results. The Original method had lower correlation with the Default and DARTEL methods (R2 = 0.71-0.74) than Default with DARTEL (R2 = 0.96) in the connectivity matrix. The clustering coefficient appears to be the most, and modularity the least, sensitive connectomic measures to normalization performance. CONCLUSION: The spatial normalization method can have an impact on resting-state functional MRI connectome and connectomic measures derived using atlas-based brain parcellation. In patients with glioma, this study demonstrated that Default and DARTEL performed better than the Original method, and that CFM made no significant difference.

Presurgical resting-state functional MRI language mapping with seed selection guided by regional homogeneity.

PURPOSE: Resting-state functional MRI (rs-FMRI) has shown potential for presurgical mapping of eloquent cortex when a patient's performance on task-based FMRI is compromised. The seed-based analysis is a practical approach for detecting rs-FMRI functional networks; however, seed localization remains challenging for presurgical language mapping. Therefore, we proposed a data-driven approach to guide seed localization for presurgical rs-FMRI language mapping. METHODS: Twenty-six patients with brain tumors located in left perisylvian regions had undergone task-based FMRI and rs-FMRI before tumor resection. For the seed-based rs-FMRI language mapping, a seeding approach that integrates regional homogeneity and meta-analysis maps (RH+MA) was proposed to guide the seed localization. Canonical and task-based seeding approaches were used for comparison. The performance of the 3 seeding approaches was evaluated by calculating the Dice coefficients between each rs-FMRI language mapping result and the result from task-based FMRI. RESULTS: With the RH+MA approach, selecting among the top 6 seed candidates resulted in the highest Dice coefficient for 81% of patients (21 of 26) and the top 9 seed candidates for 92% of patients (24 of 26). The RH+MA approach yielded rs-FMRI language mapping results that were in greater agreement with the results of task-based FMRI, with significantly higher Dice coefficients (P < .05) than that of canonical and task-based approaches within putative language regions. CONCLUSION: The proposed RH+MA approach outperformed the canonical and task-based seed localization for rs-FMRI language mapping. The results suggest that RH+MA is a robust and feasible method for seed-based functional connectivity mapping in clinical practice.

Validating myelin water imaging with transmission electron microscopy in a rat spinal cord injury model.

Myelin content is an important marker for neuropathology and MRI generated myelin water fraction (MWF) has been shown to correlate well with myelin content. However, because MWF is based on the amount of signal from myelin water, that is, the water trapped between the myelin lipid bilayers, the reading may depend heavily on myelin morphology. This is of special concern when there is a mix of intact myelin and myelin debris, as in the case of injury. To investigate what MWF measures in the presence of debris, we compared MWF to transmission electron microscopy (TEM) derived myelin fraction that measures the amount of compact appearing myelin. A rat spinal cord injury model was used with time points at normal (normal myelin), 3 weeks post-injury (myelin debris), and 8 weeks post-injury (myelin debris, partially cleared). The myelin period between normal and 3 or 8 weeks post-injury cords did not differ significantly, suggesting that as long as the bilayer structure is intact, myelin debris has the same water content as intact myelin. The MWF also correlated strongly with the TEM-derived myelin fraction, suggesting that MWF measures the amount of compact appearing myelin in both intact myelin and myelin debris. From the TEM images, it appears that as myelin degenerates, it tends to form large watery spaces within the myelin sheaths that are not classified as myelin water. The results presented in this study improve our understanding and allows for better interpretation of MWF in the presence of myelin debris.

A dynamic susceptibility contrast MRI digital reference object for testing software with leakage correction: Effect of background simulation.

PURPOSE: Dynamic susceptibility contrast (DSC)-MRI is a perfusion imaging technique from which useful quantitative imaging biomarkers can be derived. Relative cerebral blood volume (rCBV) is such a biomarker commonly used for evaluating brain tumors. To account for the extravasation of contrast agents in tumors, post-processing leakage correction is often applied to improve rCBV accuracy. Digital reference objects (DRO) are ideal for testing the post-processing software, because the biophysical model used to generate the DRO can be matched to the one that the software uses. This study aims to develop DROs to validate the leakage correction of software using Weisskoff model and to examine the effect of background signal time curves that are required by the model. METHODS: Three DROs were generated using the Weisskoff model, each composed of nine foreground lesion objects with combinations of different levels of rCBV and contrast leakage parameter (K2). Three types of background were implemented for these DROs: (1) a multi-compartment brain-like background, (2) a sphere background with a constant signal time curve, and (3) a sphere background with signal time curve identical to that of the brain-like DRO's white matter (WM). The DROs were then analyzed with an FDA-cleared software with and without leakage correction. Leakage correction was tested with and without brain segmentation. RESULTS: Accuracy of leakage correction was able to be verified using the brain-like phantom and the sphere phantom with WM background. The sphere with constant background did not perform well with leakage correction with or without brain segmentation. The DROs were able to verify that for the particular software tested, leakage correction with brain segmentation achieved the lowest error. CONCLUSIONS: DSC-MRI DROs with biophysical model matched to that of the post-processing software can be well used for the software's validation, provided that the background signals are also properly simulated for generating the reference time curve required by the model. Care needs to be taken to consider the interaction of the design of the DRO with the software's implementation of brain segmentation to extract the reference time curve.

Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging.

Herein we introduce a deep learning (DL) application engine (DLAE) system concept, present potential uses of it, and describe pathways for its integration in clinical workflows. An open-source software application was developed to provide a code-free approach to DL for medical imaging applications. DLAE supports several DL techniques used in medical imaging, including convolutional neural networks, fully convolutional networks, generative adversarial networks, and bounding box detectors. Several example applications using clinical images were developed and tested to demonstrate the capabilities of DLAE. Additionally, a model deployment example was demonstrated in which DLAE was used to integrate two trained models into a commercial clinical software package.