MRI Findings in Parkinson’s Disease: Radiologic Assessment of Nigrostriatal Degeneration

Parkinson’s disease (PD) is a movement disorder that develops due to degenerative loss of dopaminergic cells in the substantia nigra of the midbrain. Recent advances in MRI techniques have demonstrated various imaging findings that can reflect the underlying pathophysiological processes occurring in Parkinson’s disease. Many imaging studies have shown that such findings can assist in the diagnosis of Parkinson’s disease and its differentiation from atypical parkinsonism. In this review, we present MRI techniques that can be used in clinical assessment, such as nigrosome imaging and neuromelanin imaging, and we provide the detailed imaging features of Parkinson’s disease reflecting nigrostriatal degeneration.

Brain Iron Imaging in Aging and Cognitive Disorders: MRI Approaches

Iron has a vital role in the human body, including the central nervous system. Increased deposition of iron in the brain has been reported in aging and important neurodegenerative diseases. Owing to the unique magnetic resonance properties of iron, MRI has great potential for in vivo assessment of iron deposition, distribution, and non-invasive quantification. In this paper, we will review the MRI methods for iron assessment and their changes in aging and neurodegenerative diseases, focusing on Alzheimer’s disease. In addition, we will summarize the limitations of current approaches and introduce new areas and MRI methods for iron imaging that are expected in the future.

Clinical Implications of Focal Mineral Deposition in the Globus Pallidus on CT and Quantitative Susceptibility Mapping of MRI

Objective@#To assess focal mineral deposition in the globus pallidus (GP) by CT and quantitative susceptibility mapping (QSM) of MRI scans and evaluate its clinical significance, particularly cerebrovascular degeneration. @*Materials and Methods@#This study included 105 patients (66.1 ± 13.7 years; 40 male and 65 female) who underwent both CT and MRI with available QSM data between January 2017 and December 2019. The presence of focal mineral deposition in the GP on QSM (GPQSM) and CT (GPCT) was assessed visually using a three-point scale. Cerebrovascular risk factors and small vessel disease (SVD) imaging markers were also assessed. The clinical and radiological findings were compared between the different grades of GPQSM and GPCT. The relationship between GP grades and cerebrovascular risk factors and SVD imaging markers was assessed using univariable and multivariable linear regression analyses. @*Results@#GPCT and GPQSM were significantly associated (p < 0.001) but were not identical. Higher GPCT and GPQSM grades showed smaller gray matter (p = 0.030 and p = 0.025, respectively) and white matter (p = 0.013 and p = 0.019, respectively) volumes, as well as larger GP volumes (p < 0.001 for both). Among SVD markers, white matter hyperintensity was significantly associated with GPCT (p = 0.006) and brain atrophy was significantly associated with GPQSM (p = 0.032) in at univariable analysis. In multivariable analysis, the normalized volume of the GP was independently positively associated with GPCT (p < 0.001) and GPQSM (p = 0.002), while the normalized volume of the GM was independently negatively associated with GPCT (p = 0.040) and GPQSM (p = 0.035). @*Conclusion@#Focal mineral deposition in the GP on CT and QSM might be a potential imaging marker of cerebral vascular degeneration. Both were associated with increased GP volume.

Convolutional Neural Network-Based Automatic Segmentation of Substantia Nigra on Nigrosome and Neuromelanin Sensitive MR Images

Recently, neuromelanin and nigrosome imaging techniques have been developed to evaluate the substantia nigra in Parkinson’s disease. Previous studies have shown potential benefits of quantitative analysis of neuromelanin and nigrosome images in the substantia nigra, although visual assessments have been performed to evaluate structures in most studies. In this study, we investigate the potential of using deep learning based automatic region segmentation techniques for quantitative analysis of the substantia nigra. The deep convolutional neural network was trained to automatically segment substantia nigra regions on 3D nigrosome and neuromelanin sensitive MR images obtained from 30 subjects. With a 5-fold cross-validation, the mean calculated dice similarity coefficient between manual and deep learning was 0.70 ± 0.11. Although calculated dice similarity coefficients were relatively low due to empirically drawn margins, selected slices were overlapped for more than two slices of all subjects. Our results demonstrate that deep convolutional neural network-based method could provide reliable localization of substantia nigra regions on neuromelanin and nigrosome sensitive MR images.

Convolutional Neural Network-Based Automatic Segmentation of Substantia Nigra on Nigrosome and Neuromelanin Sensitive MR Images

Recently, neuromelanin and nigrosome imaging techniques have been developed to evaluate the substantia nigra in Parkinson’s disease. Previous studies have shown potential benefits of quantitative analysis of neuromelanin and nigrosome images in the substantia nigra, although visual assessments have been performed to evaluate structures in most studies. In this study, we investigate the potential of using deep learning based automatic region segmentation techniques for quantitative analysis of the substantia nigra. The deep convolutional neural network was trained to automatically segment substantia nigra regions on 3D nigrosome and neuromelanin sensitive MR images obtained from 30 subjects. With a 5-fold cross-validation, the mean calculated dice similarity coefficient between manual and deep learning was 0.70 ± 0.11. Although calculated dice similarity coefficients were relatively low due to empirically drawn margins, selected slices were overlapped for more than two slices of all subjects. Our results demonstrate that deep convolutional neural network-based method could provide reliable localization of substantia nigra regions on neuromelanin and nigrosome sensitive MR images.

Paramagnetic Rims in Multiple Sclerosis and Neuromyelitis Optica Spectrum Disorder: A Quantitative Susceptibility Mapping Study with 3-T MRI

Background@#and Purpose: Iron retained by activated microglia and macrophages in multiple sclerosis (MS) lesions may serve as a marker of innate immune system activation. Among several magnetic resonance imaging (MRI) methods, there has been recent interest in using quantitative susceptibility mapping (QSM) as a potential tool for assessing iron levels in the human brain. This study examined QSM findings in MS and neuromyelitis optica spectrum disorder (NMOSD) lesions obtained with 3-T MRI to assess imaging characteristics related to paramagnetic rims around brain lesions in MS and NMOSD. @*Methods@#This study included 32 MS and 21 seropositive NMOSD patients. MRI images were obtained using two 3-T MRI devices (Ingenia, Philips Healthcare; and Magnetom Verio, Siemens Healthineers) during routine diagnosis and treatment procedures. Multi and single echo gradient echo magnitude and phase images were obtained for QSM reconstruction.QSM images were used to characterize the detected lesions, and the findings were compared between MS and NMOSD. @*Results@#Totals of 71 and 35 MRI scans were performed during the study period in MS and NMOSD patients, respectively. In QSM images, paramagnetic rims were found in 26 (81.2%) MS patients and 1 (4.8%) NMOSD patient. Eight of the 22 MS patients and only 1 of the 10 NMOSD patients who underwent follow-up MRI showed new paramagnetic rims. The paramagnetic rim lesions appeared after enhancement or in new T2-weighted lesions without enhancement. @*Conclusions@#Paramagnetic rims might be a characteristic MRI finding for MS, and therefore they have potential as an imaging marker for differentially diagnosing MS from NMOSD using 3-T MRI.

Seed-Based Resting-State Functional MRI for Presurgical Localization of the Motor Cortex: A Task-Based Functional MRI-Determined Seed Versus an Anatomy-Determined Seed

OBJECTIVE: For localization of the motor cortex, seed-based resting-state functional MRI (rsfMRI) uses the contralateral motor cortex as a seed. However, research has shown that the location of the motor cortex could differ according to anatomical variations. The purpose of this study was to compare the results of rsfMRI using two seeds: a template seed (the anatomically expected location of the contralateral motor cortex) and a functional seed (the actual location of the contralateral motor cortex determined by task-based functional MRI [tbfMRI]). MATERIALS AND METHODS: Eight patients (4 with glioma, 3 with meningioma, and 1 with arteriovenous malformation) and 9 healthy volunteers participated. For the patients, tbfMRI was performed unilaterally to activate the healthy contralateral motor cortex. The affected ipsilateral motor cortices were mapped with rsfMRI using seed-based and independent component analysis (ICA). In the healthy volunteer group, both motor cortices were mapped with both-hands tbfMRI and rsfMRI. We compared the results between template and functional seeds, and between the seed-based analysis and ICA with visual and quantitative analysis. RESULTS: For the visual analysis, the functional seed showed significantly higher scores compared to the template seed in both the patients (p = 0.002) and healthy volunteers (p < 0.001). Although no significant difference was observed between the functional seed and ICA, the ICA results showed significantly higher scores than the template seed in both the patients (p = 0.01) and healthy volunteers (p = 0.005). In the quantitative analysis, the functional seed exhibited greater similarity to tbfMRI than the template seed and ICA. CONCLUSION: Using the contralateral motor cortex determined by tbfMRI as a seed could enhance visual delineation of the motor cortex in seed-based rsfMRI.

Deep Learning in MR Image Processing

Recently, deep learning methods have shown great potential in various tasks that involve handling large amounts of digital data. In the field of MR imaging research, deep learning methods are also rapidly being applied in a wide range of areas to complement or replace traditional model-based methods. Deep learning methods have shown remarkable improvements in several MR image processing areas such as image reconstruction, image quality improvement, parameter mapping, image contrast conversion, and image segmentation. With the current rapid development of deep learning technologies, the importance of the role of deep learning in MR imaging research appears to be growing. In this article, we introduce the basic concepts of deep learning and review recent studies on various MR image processing applications.

Relationship between Abnormal Hyperintensity on T2-Weighted Images Around Developmental Venous Anomalies and Magnetic Susceptibility of Their Collecting Veins: In-Vivo Quantitative Susceptibility Mapping Study

OBJECTIVE: A developmental venous anomaly (DVA) is a vascular malformation of ambiguous clinical significance. We aimed to quantify the susceptibility of draining veins (χvein) in DVA and determine its significance with respect to oxygen metabolism using quantitative susceptibility mapping (QSM). MATERIALS AND METHODS: Brain magnetic resonance imaging of 27 consecutive patients with incidentally detected DVAs were retrospectively reviewed. Based on the presence of abnormal hyperintensity on T2-weighted images (T2WI) in the brain parenchyma adjacent to DVA, the patients were grouped into edema (E+, n = 9) and non-edema (E−, n = 18) groups. A 3T MR scanner was used to obtain fully flow-compensated gradient echo images for susceptibility-weighted imaging with source images used for QSM processing. The χvein was measured semi-automatically using QSM. The normalized χvein was also estimated. Clinical and MR measurements were compared between the E+ and E− groups using Student's t-test or Mann-Whitney U test. Correlations between the χvein and area of hyperintensity on T2WI and between χvein and diameter of the collecting veins were assessed. The correlation coefficient was also calculated using normalized veins. RESULTS: The DVAs of the E+ group had significantly higher χvein (196.5 ± 27.9 vs. 167.7 ± 33.6, p = 0.036) and larger diameter of the draining veins (p = 0.006), and patients were older (p = 0.006) than those in the E− group. The χvein was also linearly correlated with the hyperintense area on T2WI (r = 0.633, 95% confidence interval 0.333–0.817, p < 0.001). CONCLUSION: DVAs with abnormal hyperintensity on T2WI have higher susceptibility values for draining veins, indicating an increased oxygen extraction fraction that might be associated with venous congestion.

Analysis of Apparent Diffusion Coefficients of the Brain in Healthy Controls: A Comparison Study between Single-Shot Echo-Planar Imaging and Read-out-Segmented Echo-Planar Imaging

OBJECTIVE: To compare apparent diffusion coefficients (ADCs) of brain segments by using two diffusion-weighted imaging acquisition modes, single-shot echo-planar imaging (ss-EPI) and read-out-segmented echo-planar imaging (rs-EPI), and to assess their correlation and agreement in healthy controls. MATERIALS AND METHODS: T2-weighted (T2W) images, rs-EPI, and ss-EPI of 30 healthy subjects were acquired using a 3T magnetic resonance scanner. The T2W images were co-registered to the rs-EPI and ss-EPI, which were then segmented into the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) to generate masking templates. ADC maps of rs-EPI and ss-EPI were also segmented into the GM, WM, and CSF by using the generated templates. ADCs of rs-EPI and ss-EPI were compared using Student's t tests and correlated using Pearson's correlation coefficients. Bland-Altman plots were used to assess the agreement between acquisitions.

Early Prediction of Response to Neoadjuvant Chemotherapy Using Dynamic Contrast-Enhanced MRI and Ultrasound in Breast Cancer

OBJECTIVE: To determine the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and DCE ultrasound (DCE-US) for predicting response to neoadjuvant chemotherapy (NAC) in breast cancer patients. MATERIALS AND METHODS: This Institutional Review Board-approved prospective study was performed between 2014 and 2016. Thirty-nine women with breast cancer underwent DCE-US and DCE-MRI before the NAC, follow-up DCE-US after the first cycle of NAC, and follow-up DCE-MRI after the second cycle of NAC. DCE-MRI parameters (transfer constant [Ktrans], reverse constant [kep], and leakage space [Ve]) were assessed with histograms. From DCE-US, peak-enhancement, the area under the curve, wash-in rate, wash-out rate, time to peak, and rise time (RT) were obtained. After surgery, all the imaging parameters and their changes were compared with histopathologic response using the Miller-Payne Grading (MPG) system. Data from minor and good responders were compared using Wilcoxon rank sum test, chi-square test, or Fisher's exact test. Receiver operating characteristic curve analysis was used for assessing diagnostic performance to predict good response. RESULTS: Twelve patients (30.8%) showed a good response (MPG 4 or 5) and 27 (69.2%) showed a minor response (MPG 1–3). The mean, 25th, 50th, and 75th percentiles of Ktrans and Kep of post-NAC DCE-MRI differed between the two groups. These parameters showed fair to good diagnostic performance for the prediction of response to NAC (AUC 0.76–0.81, p ≤ 0.007). Among DCE-US parameters, the percentage change in RT showed fair prediction (AUC 0.71, p = 0.023). CONCLUSION: Quantitative analysis of DCE-MRI and DCE-US was helpful for early prediction of response to NAC.

Susceptibility Weighted Imaging of the Cervical Spinal Cord with Compensation of Respiratory-Induced Artifact

PURPOSE: The objective of this study was to obtain improved susceptibility weighted images (SWI) of the cervical spinal cord using respiratory-induced artifact compensation. MATERIALS AND METHODS: The artifact from B0 fluctuations by respiration could be compensated using a double navigator echo approach. The two navigators were inserted in an SWI sequence before and after the image readouts. The B0 fluctuation was measured by each navigator echoes, and the inverse of the fluctuation was applied to eliminate the artifact from fluctuation. The degree of compensation was quantified using a quality index (QI) term for compensated imaging using each navigator. Also, the effect of compensation was analyzed according to the position of the spinal cord using QI values. RESULTS: Compensation using navigator echo gave the improved visualization of SWI in cervical spinal cord compared to non-compensated images. Before compensation, images were influenced by artificial noise from motion in both the superior (QI = 0.031) and inferior (QI = 0.043) regions. In most parts of the superior regions, the second navigator resulted in better quality (QI = 0.024, P < 0.01) compared to the first navigator, but in the inferior regions the first navigator showed better quality (QI = 0.033, P < 0.01) after correction. CONCLUSION: Motion compensation using a double navigator method can increase the improvement of the SWI in the cervical spinal cord. The proposed method makes SWI a useful tool for the diagnosis of spinal cord injury by reducing respiratory-induced artifact.

Measurement and Compensation of Respiration-Induced B0 Variations in Lumbar Spine Bone Marrow Fat Quantification

PURPOSE: To investigate and compensate the effects of respiration-induced B0 variations on fat quantification of the bone marrow in the lumbar spine. MATERIALS AND METHODS: Multi-echo gradient echo images with navigator echoes were obtained from eight healthy volunteers at 3T clinical scanner. Using navigator echo data, respiration-induced B0 variations were measured and compensated. Fat fraction maps were estimated using T2*-IDEAL algorithm from the uncompensated and compensated images. For manually drawn bone marrow regions, the estimated B0 variations and the calculated fat fractions (before and after compensations) were analyzed. RESULTS: An increase of temporal B0 variations from inferior level to superior levels was observed for all subjects. After compensation using navigator echo data, the effects of the B0 variations were reduced in gradient echo images. The calculated fat fractions show significant differences (P < 0.05) in L1 and L3 between the uncompensated and the compensated. CONCLUSION: The results of this study raise the need for considering respiration-induced B0 variations for accurate fat quantification using gradient echo images in the lumbar spine. The use of navigator echo data can be an effective way for the reduction of the effects of respiratory motion on the quantification.

Quantification of Gadolinium Concentration Using GRE and UTE Sequences

PURPOSE: To compare different MR sequences for quantification of gadolinium concentration. MATERIALS AND METHODS: Gadolinium contrast agents were diluted into 36 different concentrations. They were scanned using gradient echo (GRE) and ultrashort echo time (UTE) and R1, R2* and phase values were estimated from collected data. For analysis, ROI masks were made for each concentration and then ROI value was measured by mean and standard deviation from the estimated quantitative maps. Correlation analysis was performed and correlation coefficient was calculated. RESULTS: Using GRE sequence, R1 showed a strong linear correlation at concentrations of 10 mM or less, and R2* showed a strong linear correlation between 10 to 100 mM. The phase of GRE generally exhibited a negative linear relationship for concentrations of 100 mM or less. In the case of UTE, the phase had a strong negative linear relationship at concentrations 100 mM or above. CONCLUSION: R1, which was calculated by conventional GRE, showed a high performance of quantification for lower concentrations, with a correlation coefficient of 0.966 (10 mM or less). R2* showed stronger potential for higher concentrations with a correlation coefficient of 0.984 (10 to 100 mM), and UTE phase showed potential for even higher concentrations with a correlation coefficient of 0.992 (100 mM or above).

Background Gradient Correction using Excitation Pulse Profile for Fat and T2* Quantification in 2D Multi-Slice Liver Imaging

PURPOSE: The objective of this study was to develop background gradient correction method using excitation pulse profile compensation for accurate fat and T2* quantification in the liver. MATERIALS AND METHODS: In liver imaging using gradient echo, signal decay induced by linear background gradient is weighted by an excitation pulse profile and therefore hinders accurate quantification of T2* and fat. To correct this, a linear background gradient in the slice-selection direction was estimated from a B0 field map and signal decays were corrected using the excitation pulse profile. Improved estimation of fat fraction and T2* from the corrected data were demonstrated by phantom and in vivo experiments at 3 Tesla magnetic field. RESULTS: After correction, in the phantom experiments, the estimated T2* and fat fractions were changed close to that of a well-shimmed condition while, for in vivo experiments, the background gradients were estimated to be up to approximately 120 microT/m with increased homogeneity in T2* and fat fractions obtained. CONCLUSION: The background gradient correction method using excitation pulse profile can reduce the effect of macroscopic field inhomogeneity in signal decay and can be applied for simultaneous fat and iron quantification in 2D gradient echo liver imaging.

Cortical Thickness Estimation Using DIR Imaging with GRAPPA Factor 2

PURPOSE: DIR image is relatively free from susceptibility artifacts therefore, DIR image can make it possible to reliably measure cortical thickness/volume. One drawback of the DIR acquisition is the long scan time to acquire the fully sampled 3D data set. To solve this problem, we applied a parallel imaging method (GRAPPA) and verify the reliability of using the volumetric study. MATERIALS AND METHODS: Six healthy volunteers (3 males and 3 females; age 25.33+/-2.25 years) underwent MRI using the 3D DIR sequence at a 3.0T Siemens Tim Trio MRI scanner. GRAPPA simulation was performed from the fully sampled data set for reduction factor 2. Data reconstruction was performed using MATLAB R2009b. Freesurfer v.4.3.0 was used to evaluate the cortical thickness of the entire brain, and to extract white matter information from the DIR image, Analyze 9.0 was used. The global cortical thickness estimated from the reconstructed image was compared with reference image by using a T-test in SPSS. RESULTS: Although reduced SNR and blurring are observed from the reconstructed image, in terms of segmentation the effect was not so significant. The volumetric result was validated that there were no significant differences in many cortical regions. CONCLUSION: This study was performed with DIR image for a volumetric MRI study. To solve the long scan time of 3D DIR imaging, we applied GRAPPA algorithm. According to the results, fast imaging can be done with reduction factor 2 with little loss of image quality at 3.0T.

Cortical Thickness Estimation Using DIR Imaging with GRAPPA Factor 2

PURPOSE: DIR image is relatively free from susceptibility artifacts therefore, DIR image can make it possible to reliably measure cortical thickness/volume. One drawback of the DIR acquisition is the long scan time to acquire the fully sampled 3D data set. To solve this problem, we applied a parallel imaging method (GRAPPA) and verify the reliability of using the volumetric study. MATERIALS AND METHODS: Six healthy volunteers (3 males and 3 females; age 25.33+/-2.25 years) underwent MRI using the 3D DIR sequence at a 3.0T Siemens Tim Trio MRI scanner. GRAPPA simulation was performed from the fully sampled data set for reduction factor 2. Data reconstruction was performed using MATLAB R2009b. Freesurfer v.4.3.0 was used to evaluate the cortical thickness of the entire brain, and to extract white matter information from the DIR image, Analyze 9.0 was used. The global cortical thickness estimated from the reconstructed image was compared with reference image by using a T-test in SPSS. RESULTS: Although reduced SNR and blurring are observed from the reconstructed image, in terms of segmentation the effect was not so significant. The volumetric result was validated that there were no significant differences in many cortical regions. CONCLUSION: This study was performed with DIR image for a volumetric MRI study. To solve the long scan time of 3D DIR imaging, we applied GRAPPA algorithm. According to the results, fast imaging can be done with reduction factor 2 with little loss of image quality at 3.0T.