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Purpose: To evaluate the influence of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) on proton density fat fraction (PDFF) using multiple vendors’ DIXON methods in a multicenter, multivendor phantom study. Materials and Methods: Pairs of phantoms with varying fat volume percentages (0, 5, 10, 15, 20, 30, 40, and 50) and Gd-EOB-DTPA concentrations (0 and 0.4 mM) were constructed. The phantom without Gd-EOB-DTPA in each pair was defined as the precontrast phantom, and the corresponding phantom with Gd-EOB-DTPA in each pair was defined as the postcontrast phantom. The phantoms were scanned via three vendors’ DIXON techniques to determine PDFFs. Agreement between PDFFs determined in precontrast and postcontrast phantoms was evaluated via Bland–Altman analysis. Results: Mean differences (precontrast PDFF - postcontrast PDFF) and limits of agreement were Philips 5.1% (−0.8% to 11.0%), Siemens 6.1% (−0.9% to 13.1%), and GE 1.3% (−1.2% to 3.9%). Conclusions: PDFFs measured using the three vendors’ DIXON techniques were smaller in the postcontrast phantoms than in the corresponding precontrast phantoms regardless of different scanning parameters, because T1 bias was improved by Gd-EOB-DTPA.

Low-count SPECT images are well known to be smoothed strongly by a Butterworth filter for statistical noise reduction. Reconstructed images have a low signal-to-noise ratio (SNR) and spatial resolution because of the removal of high-frequency signal components. Using the developed robust adaptive bilateral filter (RABF), which was designed as a pre-stage filter of the Butterworth filter, this study was conducted to improve SNR without degrading the spatial resolution for low-count SPECT imaging. The filter can remove noise while preserving spatial resolution. To evaluate the proposed method, we extracted SNR and spatial resolution in a phantom study. We also conducted paired comparison for visual image quality evaluation in a clinical study. Results show that SNR was increased 1.4 times without degrading the spatial resolution. Visual image quality was improved significantly (p < 0.01) for clinical low-count data. Moreover, the accumulation structure became sharper. A structure embedded in noise emerged. Our method, which denoises without degrading the spatial resolution for low-count SPECT images, is expected to increase the effectiveness of diagnosis for low-dose scanning and short acquisition time scanning.

The realization of automatic anomaly detection of respiratory motion could be very useful to prevent accidental damage during radiation therapy. In this paper, we proposed an automatic anomaly detection method using singular value decomposition analysis. Before applying this method, the investigator needs a normal respiratory motion data of a patient. From these data, a trajectory matrix representing normal time-series feature is created. Decomposing the matrix, we obtained the feature of normal time series. Then, we applied the same procedure to real-time data and obtained real-time features. Calculating the similarity of those feature matrixes, an anomaly score was obtained. Patient motion was observed by a depth camera. In our simulation, two types of motion e.g. cough and sudden stop of breathing were successfully detected, while gradual change of respiratory cycle frequency was not detected clearly.