Impact of arterial input function selection on the accuracy of dynamic contrast-enhanced MRI quantitative analysis for the diagnosis of clinically significant prostate cancer.

PURPOSE: Using a limited temporal resolution dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) dataset to assess the impact of the arterial input function (AIF) choice on the transfer constant (K(trans) ) to distinguish prostate carcinoma (PCa) from benign tissue. MATERIALS AND METHODS: Thirty-eight patients with clinically important peripheral PCa (≥0.5 cc) were retrospectively studied. These patients underwent 1.5T multiparametric prostate MR with PCa and benign regions of interest (ROIs) selected using a visual registration with morphometric reconstruction obtained from radical prostatectomy. Using three pharmacokinetic (PK) analysis software programs, the mean K(trans) of ROIs was computed using three AIFs: an individual AIF (Ind-AIF) and two literature population average AIFs of Weinmann (W-AIF) and of Fritz-Hansen (FH-AIF). A pairwise comparison of the area under the receiver operating characteristic curves (AUROCC) obtained with different AIFs was performed. RESULTS: AUROCCs obtained with W-AIF (ranging from 0.801 to 0.843) were significantly higher than FH-AIF (ranging from 0.698 to 0.780, 0.002 ≤ P ≤ 0.045) and similar to or higher than Ind-AIF (ranging from 0.591 to 0.839, 0.014 ≤ P ≤ 0.9). Ind-AIF and FH-AIF provided similar AUROCC (0.34 ≤ P ≤ 0.81). The pairwise correlation of K(trans) values was moderate to very strong when comparing W-AIF with FH-AIF (the Spearman's correlation coefficients [SCCs] ranged from 0.55 to 0.93) and very weak to moderate when comparing W-AIF with Ind-AIF (the SCCs ranged from 0.018 to 0.59) or FH-AIF with Ind-AIF (the SCCs ranged from 0.30 to 0.51). CONCLUSION: W-AIF yielded a higher performance than FH-AIF and a similar or higher performance than Ind-AIF in distinguishing PCa from benign tissue.

Measurement of retinal vessel widths from fundus images based on 2-D modeling.

Changes in retinal vessel diameter are an important sign of diseases such as hypertension, arteriosclerosis and diabetes mellitus. Obtaining precise measurements of vascular widths is a critical and demanding process in automated retinal image analysis as the typical vessel is only a few pixels wide. This paper presents an algorithm to measure the vessel diameter to subpixel accuracy. The diameter measurement is based on a two-dimensional difference of Gaussian model, which is optimized to fit a two-dimensional intensity vessel segment. The performance of the method is evaluated against Brinchmann-Hansen's half height, Gregson's rectangular profile and Zhou's Gaussian model. Results from 100 sample profiles show that the presented algorithm is over 30% more precise than the compared techniques and is accurate to a third of a pixel.