Prediction of amyloid positron emission tomography positivity using multiple regression analysis of quantitative susceptibility mapping.

PURPOSE: To develop a method for predicting amyloid positron emission tomography (PET) positivity based on multiple regression analysis of quantitative susceptibility mapping (QSM). MATERIALS AND METHODS: This prospective study included 39 patients with suspected dementia from four centers. QSM images were obtained through a 3-T, three-dimensional radiofrequency-spoiled gradient-echo sequence with multiple echoes. The cortical standard uptake value ratio (SUVR) was obtained using amyloid PET with 18F-flutemetamol, and susceptibility in the brain regions was obtained using QSM. A multiple regression model to predict cortical SUVR was constructed based on susceptibilities in multiple brain regions, with the constraint that cortical SUVR and susceptibility were positively correlated. The discrimination performance of the Aß-positive and Aß-negative cohorts was evaluated based on the predicted SUVR using the area under the receiver operating characteristic curve (AUC) and Mann-Whitney U test. RESULTS: The correlation coefficients between true and predicted SUVR were increased by incorporating the constraint, and the AUC to discriminate between the Aß-positive and Aß-negative cohorts reached to 0.79 (p < 0.01). CONCLUSION: These preliminary results suggest that a QSM-based multiple regression model can predict amyloid PET positivity with fair accuracy.

Three-dimensional Multi-parameter Mapping of Relaxation Times and Susceptibility Using Partially RF-spoiled Gradient Echo.

PURPOSE: MR parameter mapping is a technique that obtains distributions of parameters such as relaxation time and proton density (PD) and is starting to be used for disease quantification in clinical diagnoses. Quantitative susceptibility mapping is also promising for the early diagnosis of brain disorders such as degenerative neurological disorders. Therefore, we developed an MR quantitative parameter mapping (QPM) method to map four tissue-related parameters (T1, T2*, PD, and susceptibility) and B1 simultaneously by using a 3D partially RF-spoiled gradient echo (pRSGE). We verified the accuracy and repeatability of QPM in phantom and volunteer experiments. METHODS: Tissue-related parameters are estimated by varying four scan parameters of the 3D pRSGE: flip angle, RF-pulse phase increment, TR and TE, performing multiple image scans, and finding a least-squares fit for an intensity function (which expresses the relationship between the scan parameters and intensity values). The intensity function is analytically complex, but by using a Bloch simulation to create it numerically, the least-squares fitting can be used to estimate the quantitative values. This has the advantage of shortening the image-reconstruction processing time needed to estimate the quantitative values than with methods using pattern matching. RESULTS: A 1.1-mm isotropic resolution scan covering the whole brain was completed with a scan time of approximately 12 minutes, and the reconstruction time using a GPU was approximately 1 minute. The phantom experiments confirmed that both the accuracy and repeatability of the quantitative values were high. The volunteer scans also confirmed that the accuracy of the quantitative values was comparable to that of conventional methods. CONCLUSION: The proposed QPM method can map T1, T2*, PD, susceptibility, and B1 simultaneously within a scan time that can be applied to human subjects.

Simultaneous Arterial and Venous Imaging Using 3D Quantitative Parameter Mapping.

PURPOSE: To increase the number of images that can be acquired in MR examinations using quantitative parameters, we developed a method for obtaining arterial and venous images with mapping of proton density (PD), RF inhomogeneity (B1), longitudinal relaxation time (T1), apparent transverse relaxation time (T2*), and magnetic susceptibility through calculation, all with the same spatial resolution. METHODS: The proposed method uses partially RF-spoiled gradient echo sequences to obtain 3D images of a subject with multiple scan parameters. The PD, B1, T1, T2*, and magnetic susceptibility maps are estimated using the quantification method we previously developed. Arterial images are obtained by adding images using optimized weights to emphasize the arteries. A morphology filter is used to obtain venous images from the magnetic susceptibility maps. For evaluation, images obtained from four out of five healthy volunteers were used to optimize the weights used in the arterial-image calculation, and the optimized weights were applied to the images from the fifth volunteer to obtain an arterial image. Arterial images of the five volunteers were calculated using the leave-one-out method, and the contrast between the arterial and background regions defined using the reference time-of-flight (TOF) method was evaluated using the area under the receiver operation characteristic curve (AUC). The contrast between venous and background regions defined by a reference quantitative susceptibility mapping (QSM) method was also evaluated for the venous image. RESULTS: The AUC to discriminate blood vessels and background using the proposed method was 0.905 for the arterial image and 0.920 for the venous image. CONCLUSION: The results indicate that the arterial images and venous images have high signal intensity at the same region as determined from the reference TOF and QSM methods, demonstrating the possibility of acquiring vasculature images with quantitative parameter mapping through calculation in an integrated manner.

A diagnostic index based on quantitative susceptibility mapping and voxel-based morphometry may improve early diagnosis of Alzheimer's disease.

OBJECTIVES: Voxel-based morphometry (VBM) is widely used to quantify the progression of Alzheimer's disease (AD), but improvement is still needed for accurate early diagnosis. We evaluated the feasibility of a novel diagnosis index for early diagnosis of AD based on quantitative susceptibility mapping (QSM) and VBM. METHODS: Thirty-seven patients with AD, 24 patients with mild cognitive impairment (MCI) due to AD, and 36 cognitively normal (NC) subjects from four centers were included. A hybrid sequence was performed by using 3-T MRI with a 3D multi-echo GRE sequence to obtain both a T1-weighted image for VBM and phase images for QSM. The index was calculated from specific voxels in QSM and VBM images by using a linear support vector machine. The method of voxel extraction was optimized to maximize diagnostic accuracy, and the optimized index was compared with the conventional VBM-based index using receiver operating characteristic analysis. RESULTS: The index was optimal when voxels were extracted as increased susceptibility (AD > NC) in the parietal lobe and decreased gray matter volume (AD < NC) in the limbic system. The optimized proposed index showed excellent performance for discrimination between AD and NC (AUC = 0.94, p = 1.1 × 10-10) and good performance for MCI and NC (AUC = 0.87, p = 1.8 × 10-6), but poor performance for AD and MCI (AUC = 0.68, p = 0.018). Compared with the conventional index, AUCs were improved for all cases, especially for MCI and NC (p < 0.05). CONCLUSIONS: In this preliminary study, the proposed index based on QSM and VBM improved the diagnostic performance between MCI and NC groups compared with the VBM-based index. KEY POINTS: • We developed a novel diagnostic index for Alzheimer's disease based on quantitative susceptibility mapping (QSM) and voxel-based morphometry (VBM). • QSM and VBM images can be acquired simultaneously in a single sequence with little increasing scan time. • In this preliminary study, the proposed diagnostic index improved the discriminative performance between mild cognitive impairment and normal control groups compared with the conventional VBM-based index.

Stimulus Phase Locking of Cortical Oscillations for Rhythmic Tone Sequences in Rats.

Humans can rapidly detect regular patterns (i.e., within few cycles) without any special attention to the acoustic environment. This suggests that human sensory systems are equipped with a powerful mechanism for automatically predicting forthcoming stimuli to detect regularity. It has recently been hypothesized that the neural basis of sensory predictions exists for not only what happens (predictive coding) but also when a particular stimulus occurs (predictive timing). Here, we hypothesize that the phases of neural oscillations are critical in predictive timing, and these oscillations are modulated in a band-specific manner when acoustic patterns become predictable, i.e., regular. A high-density microelectrode array (10 × 10 within 4 × 4 mm2) was used to characterize spatial patterns of band-specific oscillations when a random-tone sequence was switched to a regular-tone sequence. Increasing the regularity of the tone sequence enhanced phase locking in a band-specific manner, notwithstanding the type of the regular sound pattern. Gamma-band phase locking increased immediately after the transition from random to regular sequences, while beta-band phase locking gradually evolved with time after the transition. The amplitude of the tone-evoked response, in contrast, increased with frequency separation with respect to the prior tone, suggesting that the evoked-response amplitude encodes sequence information on a local scale, i.e., the local order of tones. The phase locking modulation spread widely over the auditory cortex, while the amplitude modulation was confined around the activation foci. Thus, our data suggest that oscillatory phase plays a more important role than amplitude in the neuronal detection of tone sequence regularity, which is closely related to predictive timing. Furthermore, band-specific contributions may support recent theories that gamma oscillations encode bottom-up prediction errors, whereas beta oscillations are involved in top-down prediction.