NEURORADIOLOGY Parkinson disease (PD) is a neurodegenerative disorder characterized by progressive degeneration of dopaminergic neurons in the substantia nigra (SN), leading to abnormalities of movement and other functions. Motor symptoms in PD onset and progression have exhibited lateral asymmetry (1), which suggests that the neuronal loss in the SN can be asymmetric, as reported in a neuropathologic study (2). In a majority of patients, symptoms begin on the side of the dominant hand (1). SPECT has demonstrated left hemispheric predominance of nigrostriatal dysfunction in patients with PD who are right handed, providing further evidence that dopaminergic denervation in the SN can be asymmetric (3). Furthermore, signal lateral asymmetry in the SN was indicated in an anatomic MRI study at 7.0 T (4) and considered in a susceptibility-weighted imaging study at 3.0 T (5). Neuroimaging evidence of underlying tissue Purpose: To investigate lateral asymmetry in the SN of patients with PD by using diffusion MRI with both Gaussian and non-Gaussian models. Materials and Methods:In this cross-sectional study conducted from March 2015 to March 2017, 27 participants with PD and 27 age-matched healthy control (HC) participants, all right handed, underwent MRI at 3.0 T. High-spatial-resolution diffusion images were acquired with a reduced field of view by using seven b values up to 3000 sec/mm 2 . A continuous-time random-walk (CTRW) non-Gaussian diffusion model was used to produce anomalous diffusion coefficient (D m ) and temporal (a) and spatial (b) diffusion heterogeneity indexes followed by a Gaussian diffusion model to yield an apparent diffusion coefficient (ADC). Individual or linear combinations of diffusion parameters in the SN were unilaterally and bilaterally compared between the PD and HC groups. Results:In the bilateral comparison between the PD and HC groups, differences were observed in b (0.67 6 0.06 [standard deviation] vs 0.64 6 0.04, respectively; P = .016), ADC (0.48 mm 2 /msec 6 0.08 vs 0.53 mm 2 /msec 6 0.06, respectively; P = .03), and the combination of CTRW parameters (P = .02). In the unilateral comparison, differences were observed in all diffusion parameters on the left SN (P , .03), but not on the right (P . .20). In a receiver operating characteristic (ROC) analysis to delineate left SN abnormality in PD, the combination of D m , a, and b produced the best sensitivity (sensitivity, 0.78); the combination of D m and b produced the best specificity (specificity, 0.85); and the combination of a and b produced the largest area under the ROC curve (area under the ROC curve, 0.73). Conclusion:These results suggest that quantitative diffusion MRI is sensitive to brain tissue changes in participants with Parkinson disease and provide evidence of substantia nigra lateral asymmetry in this disease.
Objectives: To investigate factors associated with residual sleepiness in patients who were highly adherent to continuous positive airway pressure (CPAP). Nocturnal inactivity, comorbidities, concomitant medications, and, in particular, white matter (WM) differences using diffusion magnetic resonance imaging (MRI) were explored using a continuous-time random-walk (CTRW) model. Methods: Twenty-seven male patients (30–55 years of age) with obstructive sleep apnea (OSA) received CPAP as the only treatment (CPAP ≥ 6hr./night) for at least 30 days. Based on the Psychomotor Vigilance Task (PVT) results, participants were divided into a non-sleepy group (lapses ≤ 5; n = 18) and a sleepy group (lapses > 5; n = 9). Mean nocturnal inactivity (sleep proxy) was measured using actigraphy for one week. Diffusion-weighted imaging (DWI) with high b-values, as well as diffusion tensor imaging (DTI), was performed on a 3 Tesla MRI scanner. The DWI dataset was analyzed using the CTRW model that yielded three parameters: temporal diffusion heterogeneity α, spatial diffusion heterogeneity β, and an anomalous diffusion coefficient Dm. The differences in α, β, Dm, and the combination of α and β between the two groups were investigated by a whole-brain analysis using tract-based spatial statistics (TBSS), followed by a regional analysis on individual fiber tracts using a standard parcellation template. Results from the CTRW model were compared with those obtained from DTI. The three CTRW parameters were also correlated with the clinical assessment scores, Epworth Sleepiness Scale (ESS), PVT lapses, and PVT mean reaction time (MRT) in specific fiber tracts. Results: There were no differences between groups in mean sleep duration, comorbidities, and the number or type of medications, including alerting and sedating medications. In the whole-brain DWI analysis, the sleepy group showed higher α (17.27% of the WM voxels) and Dm (17.14%) when compared to the non-sleepy group (P < 0.05), whereas no significant difference in β was observed. In the regional fiber analysis, the sleepy and non-sleepy groups showed significant differences in α, β, or their combinations in a total of twelve fiber tracts, whereas similar differences were not observed in DTI parameters, when age was used as a covariate. Additionally, moderate to strong correlations between the CTRW parameters (α, β, or Dm) and the sleepiness assessment scores (ESS, PVT lapses, or PVT MRT) were observed in specific fiber tracts (|R| = 0.448–0.654, P = 0.0003–0.019). Conclusions: The observed differences in the CTRW parameters between the two groups indicate that WM alterations can be a possible mechanism to explain reversible versus residual sleepiness observed in OSA patients with identical high level of CPAP use. The moderate to strong correlations between the CTRW parameters and the clinical scores suggest the possibility of developing objective and quantitative imaging markers to complement clinical assessment of OSA patients.
The non-Gaussian FROC diffusion model showed clinical value in early prediction of gastrointestinal stromal tumor response to second-line sunitinib targeted therapy. The pretreatment FROC parameter β can increase the predictive accuracy when combined with the change in diffusion coefficient during treatment. Magn Reson Med 79:1399-1406, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
Purpose To demonstrate an MRI technique—Submillisecond Periodic Event Encoded Dynamic Imaging (SPEEDI)—for capturing cyclic dynamic events with submillisecond temporal resolution. Methods The SPEEDI technique is based on an FID or an echo signal in which each time point in the signal is used to sample a distinct k‐space raster, followed by repeated FIDs or echoes to produce the remaining k‐space data in each k‐space raster. All acquisitions are synchronized with a cyclic event, resulting in a set of time‐resolved images of the cyclic event with a temporal resolution determined by the dwell time. In SPEEDI, spatial encoding is accomplished by phase encoding. The SPEEDI technique was demonstrated in two experiments at 3 T to (1) visualize fast‐changing electric currents that mimicked the waveform of an action potential, and (2) characterize rapidly decaying eddy currents in an MRI system, with a temporal resolution of 0.2 ms and 0.4 ms, respectively. In both experiments, compressed sensing was incorporated to reduce the scan times. Phase difference maps related to the dynamics of electric currents or eddy currents were then obtained. Results In the first experiment, time‐resolved phase maps resulting from the action potential–mimicking current waveform were successfully obtained and agreed well with theoretical calculations (normalized RMS error = 0.07). In the second experiment, spatially resolved eddy current phase maps revealed time constants (27.1 ± 0.2 ms, 41.1 ± 3.5 ms, and 34.8 ± 0.7 ms) that matched well with those obtained from an established method using point sources (26.4 ms, 41.2 ms and 34.8 ms). For both experiments, phase maps from fully sampled and compressed‐sensing–accelerated k‐space data exhibited a high structural similarity (> 0.8) despite a two‐fold to three‐fold acceleration. Conclusions We have illustrated that SPEEDI can provide submillisecond temporal resolution. This capability will likely lead to future exploration of ultrafast, cyclic biomedical processes using MRI.
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