Amide proton transfer (APT) contrast imaging is based on the chemical exchange saturation transfer (CEST) of protons between the amide groups and bulk water. Here, we demonstrate the effect of the saturation pulse duration on CEST in APT imaging with use of a clinical MR scanner. Four samples were prepared from chicken egg white diluted with H2O. Experiments were performed on a 3T clinical MR scanner with use of a body coil for two-channel parallel radiofrequency transmission. APT images were acquired at six frequency offsets (± 3.0, ± 3.5, ± 4.0 ppm) with respect to the water resonance as well as one far off-resonant frequency (-160 ppm) for signal normalization. The CEST effect was defined as asymmetry of the magnetization transfer ratio at 3.5 ppm. We measured the CEST effects in the egg white samples with different concentrations at seven saturation pulse durations. The influence of the extension of repetition time (TR) on the CEST effect was also evaluated. The CEST effect was not influenced by the change in TR. The CEST effect was increased significantly with the concentration when the duration was ≥1.0 s (P < 0.01). The CEST effect was highly correlated with the concentration at all saturation pulse durations, and its increase ratio was higher at longer saturation pulse durations. In conclusion, a long saturation pulse duration is useful for the sensitive detection of mobile proteins and peptides in APT imaging.

2 J. Magn. Reson. Imaging 2017;45:863-871.

GagCEST imaging could be a reliable and quantitative technique for assessing intervertebral disc degeneration.

The aim of this study was to investigate the impact of pixel-based machine learning (ML) techniques, i.e., fuzzy-c-means clustering method (FCM), and the artificial neural network (ANN) and support vector machine (SVM), on an automated framework for delineation of gross tumor volume (GTV) regions of lung cancer for stereotactic body radiation therapy. The morphological and metabolic features for GTV regions, which were determined based on the knowledge of radiation oncologists, were fed on a pixel-by-pixel basis into the respective FCM, ANN, and SVM ML techniques. Then, the ML techniques were incorporated into the automated delineation framework of GTVs followed by an optimum contour selection (OCS) method, which we proposed in a previous study. The three-ML-based frameworks were evaluated for 16 lung cancer cases (six solid, four ground glass opacity (GGO), six part-solid GGO) with the datasets of planning computed tomography (CT) and F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT images using the three-dimensional Dice similarity coefficient (DSC). DSC denotes the degree of region similarity between the GTVs contoured by radiation oncologists and those estimated using the automated framework. The FCM-based framework achieved the highest DSCs of 0.79±0.06, whereas DSCs of the ANN-based and SVM-based frameworks were 0.76±0.14 and 0.73±0.14, respectively. The FCM-based framework provided the highest segmentation accuracy and precision without a learning process (lowest calculation cost). Therefore, the FCM-based framework can be useful for delineation of tumor regions in practical treatment planning.

Alzheimer's disease (AD) is a dementing disorder and one of the major public health problems in countries with greater longevity. The cerebral cortical thickness and cerebral blood flow (CBF), which are considered as morphological and functional image features, respectively, could be decreased in specific cerebral regions of patients with dementia of Alzheimer type. Therefore, the aim of this study was to develop a computer-aided classification system for AD patients based on machine learning with the morphological and functional image features derived from a magnetic resonance (MR) imaging system. The cortical thicknesses in ten cerebral regions were derived as morphological features by using gradient vector trajectories in fuzzy membership images. Functional CBF maps were measured with an arterial spin labeling technique, and ten regional CBF values were obtained by registration between the CBF map and Talairach atlas using an affine transformation and a free form deformation. We applied two systems based on an arterial neural network (