The objectives of this study were (a) to determine the concurrent validity of the flight time (FT) and double integration of vertical reaction force (DIF) methods in the estimation of vertical jump height with the video method (VID) as reference; (b) to verify the degree of agreement among the 3 methods; (c) to propose regression equations to predict the jump height using the FT and DIF. Twenty healthy male and female nonathlete college students participated in this study. The experiment involved positioning a contact mat (CTM) on the force platform (FP), with a video camera 3 m from the FP and perpendicular to the sagittal plane of the subject being assessed. Each participant performed 15 countermovement jumps with 60-second intervals between the trials. Significant differences were found between the jump height obtained by VID and the results with FT (p ≤ 0.01) and DIF (p ≤ 0.01), showing that the methods are not valid. Additionally, the DIF showed a greater degree of agreement with the reference method than the FT did, and both presented a systematic error. From the linear regression test was determined the prediction equations with a high degree of linearity between the methods VID vs. DIF (R = 0.988) and VID vs. FT (R = 0.979). Therefore, the prediction equations suggested may allow coaches to measure the vertical jump performance of athletes by the FT and DIF, using a CTM or an FP, which represents more practical and viable approaches in the sports field; comparisons can then be made with the results of other athletes evaluated by VID.

This paper proposes a new method of simulating dose reduction in digital breast tomosynthesis, starting from a clinical image acquired with a standard radiation dose. It considers both signal-dependent quantum and signal-independent electronic noise. Furthermore, the method accounts for pixel crosstalk, which causes the noise to be frequency-dependent, thus increasing the simulation accuracy. For an objective assessment, simulated and real images were compared in terms of noise standard deviation, signal-to-noise ratio (SNR) and normalized noise power spectrum (NNPS). A two-alternative forced-choice (2-AFC) study investigated the similarity between the noise strength of low-dose simulated and real images. Six experienced medical physics specialists participated on the study, with a total of 2 160 readings. Objective assessment showed no relevant trends with the simulated noise. The relative error in the standard deviation of the simulated noise was less than 2% for every projection angle. The relative error of the SNR was less than 1.5%, and the NNPS of the simulated images had errors less than 2.5%. The 2-AFC human observer experiment yielded no statistically significant difference ( =0.84) in the perceived noise strength between simulated and real images. Furthermore, the observer study also allowed the estimation of a dose difference at which the observer perceived a just-noticeable difference (JND) in noise levels. The estimated JND value indicated that a change of 17% in the current-time product was sufficient to cause a noticeable difference in noise levels. The observed high accuracy, along with the flexible calibration, make this method an attractive tool for clinical image-based simulations of dose reduction.

In breast cancer screening, the radiation dose must be kept to the minimum necessary to achieve the desired diagnostic objective, thus minimizing risks associated with cancer induction. However, decreasing the radiation dose also degrades the image quality. In this work we restore digital breast tomosynthesis (DBT) projections acquired at low radiation doses with the goal of achieving a quality comparable to that obtained from current standard full-dose imaging protocols. A multiframe denoising algorithm was applied to low-dose projections, which are filtered jointly. Furthermore, a weighted average was used to inject a varying portion of the noisy signal back into the denoised one, in order to attain a signal-to-noise ratio comparable to that of standard full-dose projections. The entire restoration framework leverages a signal-dependent noise model with quantum gain which varies both upon the projection angle and on the pixel position. A clinical DBT system and a 3D anthropomorphic breast phantom were used to validate the proposed method, both on DBT projections and slices from the 3D reconstructed volume. The framework is shown to attain the standard full-dose image quality from data acquired at 50% lower radiation dose, whereas progressive loss of relevant details compromises the image quality if the dosage is further decreased.

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