Chrysoprase is a popular gemstone with consumers because of its charming apple green colour but a scientific classification of its colour has not yet been achieved. In this research, we determined the most effective background of the Munsell Chart for chrysoprase colour grading under a 6504 K fluorescent lamp and applied an affinity propagation (AP) clustering algorithm to the colour grading of coloured gems for the first time. Forty gem-quality chrysoprase samples from Australia were studied using a UV-VIS spectrophotometer and Munsell neutral grey backgrounds. The results determined the effects of a Munsell neutral grey background on the observed colour. It was found that the Munsell N9.5 background was the most effective for colour grading in this case. The observed chrysoprase colours were classified into five groups: Fancy Light, Fancy, Fancy Intense, Fancy Deep and Fancy Dark. The feasibility of the colour grading scheme was verified using the colour difference formula DE2000.
PurposeWithout explicit coil sensitivity information acquired by means of a reference body coil, multi‐channel signal combination for water‐fat separation (WFS) can be challenging due to channel‐dependent phase offsets and chemical‐shift dependent phase shifts. This study aims to develop a referenceless, robust, accurate, and fast channel combination method for WFS.Theory and MethodsA dual‐step multi‐channel combination method is proposed. In the first step, channel‐dependent phase offsets are estimated with a preliminary WFS estimation. In the second step, the multi‐channel data are combined after removing phase offsets. Thereafter, WFS is performed to obtain final results. Numerical simulations (4–64 coils) and in vivo experiments (8, 16, 28 coils) at 3T field strength are conducted to compare the proposed method to previous methods. Channel combination with a body‐coil scan serves as the reference for in vivo experiments.ResultsThe proposed method estimates channel‐dependent phase offsets accurately. It shows improved robustness to phase singularities than weighted mean and adaptive reconstruction. It is faster than adaptive reconstruction (e.g., 25.45 versus 46.34 s with 28 coils) and the channel‐by‐channel WFS method (e.g., 21.77 versus 50.04 s with 8 coils). It provides comparable fat quantification accuracy to the reference under various reasonable signal‐to‐noise ratio conditions (e.g., Pearson correlation coefficient r = 0.981 with P < 0.01, for in vivo fat fractions using flip angle = 10°).ConclusionThe proposed referenceless channel combination method may be beneficial to both qualitative and quantitative water‐fat imaging.
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