Recommended Resting-State fMRI Acquisition and Preprocessing Steps for Preoperative Mapping of Language and Motor and Visual Areas in Adult and Pediatric Patients with Brain Tumors and Epilepsy

Resting-state (rs) fMRI has been shown to be useful for preoperative mapping of functional areas in patients with brain tumors and epilepsy. However, its lack of standardization limits its widespread use and hinders multicenter collaboration. The American Society of Functional Neuroradiology, American Society of Pediatric Neuroradiology, and the American Society of Neuroradiology Functional and Diffusion MR Imaging Study Group recommend specific rs-fMRI acquisition approaches and preprocessing steps that will further support rs-fMRI for future clinical use. A task force with expertise in fMRI from multiple institutions provided recommendations on the rs-fMRI steps needed for mapping of language, motor, and visual areas in adult and pediatric patients with brain tumor and epilepsy. These were based on an extensive literature review and expert consensus.Following rs-fMRI acquisition parameters are recommended: minimum 6-minute acquisition time; scan with eyes open with fixation; obtain rs-fMRI before both task-based fMRI and contrast administration; temporal resolution of ≤2 seconds; scanner field strength of 3T or higher. The following rs-fMRI preprocessing steps and parameters are recommended: motion correction (seed-based correlation analysis [SBC], independent component analysis [ICA]); despiking (SBC); volume censoring (SBC, ICA); nuisance regression of CSF and white matter signals (SBC); head motion regression (SBC, ICA); bandpass filtering (SBC, ICA); and spatial smoothing with a kernel size that is twice the effective voxel size (SBC, ICA).The consensus recommendations put forth for rs-fMRI acquisition and preprocessing steps will aid in standardization of practice and guide rs-fMRI program development across institutions. Standardized rs-fMRI protocols and processing pipelines are essential for multicenter trials and to implement rs-fMRI as part of standard clinical practice.

Investigating Simultaneity for Deep Learning-Enhanced Actual Ultra-Low-Dose Amyloid PET/MR Imaging

BACKGROUND AND PURPOSE

Diagnostic-quality amyloid PET images can be created with deep learning using actual ultra-low-dose PET images and simultaneous structural MR imaging. Here, we investigated whether simultaneity is required; if not, MR imaging-assisted ultra-low-dose PET imaging could be performed with separate PET/CT and MR imaging acquisitions.

MATERIALS AND METHODS

We recruited 48 participants: Thirty-two (20 women; mean, 67.7 [SD, 7.9] years) were used for pretraining; 328 (SD, 32) MBq of [¹⁸F] florbetaben was injected. Sixteen participants (6 women; mean, 71.4 [SD. 8.7] years of age) were scanned in 2 sessions, with 6.5 (SD, 3.8) and 300 (SD, 14) MBq of [¹⁸F] florbetaben injected, respectively. Structural MR imaging was acquired simultaneously with PET (90-110 minutes postinjection) on integrated PET/MR imaging in 2 sessions. Multiple U-Net-based deep networks were trained to create diagnostic PET images. For each method, training was done with the ultra-low-dose PET as input combined with MR imaging from either the ultra-low-dose session (simultaneous) or from the standard-dose PET session (nonsimultaneous). Image quality of the enhanced and ultra-low-dose PET images was evaluated using quantitative signal-processing methods, standardized uptake value ratio correlation, and clinical reads.

RESULTS

Qualitatively, the enhanced images resembled the standard-dose image for both simultaneous and nonsimultaneous conditions. Three quantitative metrics showed significant improvement for all networks and no differences due to simultaneity. Standardized uptake value ratio correlation was high across different image types and network training methods, and 31/32 enhanced image pairs were read similarly.

CONCLUSIONS

This work suggests that accurate amyloid PET images can be generated using enhanced ultra-low-dose PET and either nonsimultaneous or simultaneous MR imaging, broadening the utility of ultra-low-dose amyloid PET imaging.

Comparison of diffusion MRI and CLARITY fiber orientation estimates in both gray and white matter regions of human and primate brain

Diffusion MRI (dMRI) represents one of the few methods for mapping brain fiber orientations non-invasively. Unfortunately, dMRI fiber mapping is an indirect method that relies on inference from measured diffusion patterns. Comparing dMRI results with other modalities is a way to improve the interpretation of dMRI data and help advance dMRI technologies. Here, we present methods for comparing dMRI fiber orientation estimates with optical imaging of fluorescently labeled neurofilaments and vasculature in 3D human and primate brain tissue cuboids cleared using CLARITY. The recent advancements in tissue clearing provide a new opportunity to histologically map fibers projecting in 3D, which represents a captivating complement to dMRI measurements. In this work, we demonstrate the capability to directly compare dMRI and CLARITY in the same human brain tissue and assess multiple approaches for extracting fiber orientation estimates from CLARITY data. We estimate the three-dimensional neuronal fiber and vasculature orientations from neurofilament and vasculature stained CLARITY images by calculating the tertiary eigenvector of structure tensors. We then extend CLARITY orientation estimates to an orientation distribution function (ODF) formalism by summing multiple sub-voxel structure tensor orientation estimates. In a sample containing part of the human thalamus, there is a mean angular difference of 19o±15o between the primary eigenvectors of the dMRI tensors and the tertiary eigenvectors from the CLARITY neurofilament stain. We also demonstrate evidence that vascular compartments do not affect the dMRI orientation estimates by showing an apparent lack of correspondence (mean angular difference = 49o±23o) between the orientation of the dMRI tensors and the structure tensors in the vasculature stained CLARITY images. In a macaque brain dataset, we examine how the CLARITY feature extraction depends on the chosen feature extraction parameters. By varying the volume of tissue over which the structure tensor estimates are derived, we show that orientation estimates are noisier with more spurious ODF peaks for sub-voxels below 30 µm3 and that, for our data, the optimal gray matter sub-voxel size is between 62.5 µm3 and 125 µm3. The example experiments presented here represent an important advancement towards robust multi-modal MRI-CLARITY comparisons.

Resting-State Functional MRI: Everything That Nonexperts Have Always Wanted to Know

Resting-state fMRI was first described by Biswal et al in 1995 and has since then been widely used in both healthy subjects and patients with various neurologic, neurosurgical, and psychiatric disorders. As opposed to paradigm- or task-based functional MR imaging, resting-state fMRI does not require subjects to perform any specific task. The low-frequency oscillations of the resting-state fMRI signal have been shown to relate to the spontaneous neural activity. There are many ways to analyze resting-state fMRI data. In this review article, we will briefly describe a few of these and highlight the advantages and limitations of each. This description is to facilitate the adoption and use of resting-state fMRI in the clinical setting, helping neuroradiologists become familiar with these techniques and applying them for the care of patients with neurologic and psychiatric diseases.

Non-Relative Value Unit-Generating Activities Represent One-Fifth of Academic Neuroradiologist Productivity

BACKGROUND AND PURPOSE

A neuroradiologist's activity includes many tasks beyond interpreting relative value unit-generating imaging studies. Our aim was to test a simple method to record and quantify the non-relative value unit-generating clinical activity represented by consults and clinical conferences, including tumor boards.

MATERIALS AND METHODS

Four full-time neuroradiologists, working an average of 50% clinical and 50% academic activity, systematically recorded all the non-relative value unit-generating consults and conferences in which they were involved during 3 months by using a simple, Web-based, computer-based application accessible from smartphones, tablets, or computers. The number and type of imaging studies they interpreted during the same period and the associated relative value units were extracted from our billing system.

RESULTS

During 3 months, the 4 neuroradiologists working an average of 50% clinical activity interpreted 4241 relative value unit-generating imaging studies, representing 8152 work relative value units. During the same period, they recorded 792 non-relative value unit-generating study reviews as part of consults and conferences (not including reading room consults), representing 19% of the interpreted relative value unit-generating imaging studies.

CONCLUSIONS

We propose a simple Web-based smartphone app to record and quantify non-relative value unit-generating activities including consults, clinical conferences, and tumor boards. The quantification of non-relative value unit-generating activities is paramount in this time of a paradigm shift from volume to value. It also represents an important tool for determining staffing levels, which cannot be performed on the basis of relative value unit only, considering the importance of time spent by radiologists on non-relative value unit-generating activities. It may also influence payment models from medical centers to radiology departments or practices.