Figure 1.
Simulation of intensity scale of T1- and T2-weighted imaging compared to HDR-MRI.
T1 and T2 values cover the physiological range. For comparative purposes, dynamic range was defined to span from 10% to 90% of each intensity scale. Regions of an image with T1 and T2 outside this range would appear featureless as either completely white or black. HDR-MRI improved the dynamic range compared to T1- and T2-weighted imaging. Furthermore, in conventional imaging, dynamic range was sacrificed to visualize short T1 and T2 features whereas HDR-MRI did not suffer from this limitation.
Figure 2.
HDR processing in photography.
(A) LDR photos taken at varying exposure times produce shifted characteristic curves (otherwise known as the digital conversion function f) that cover different ranges of irradiance. HDR processing merges the LDR characteristic curves to produce an HDR characteristic curve that covers a larger range of irradiance. The HDR characteristic curve is used to calculate object illumination to alleviate the issues of over- and under-exposure associated with conventional LDR photography. (B) An example showing the merger of 4 LDR photos (smaller photos) to a HDR photo (larger photo). In the HDR photo, the front wall showed no saturation from over-exposure while the poorly lit hallway remained visible. This was not achieved in any single LDR photo due to the limited dynamic range.
Figure 3.
Legends and characterizations of solution phantom samples 1–12.
Figure 4.
A series of images were acquired with constant TE at 10.635 ms and varying TR at (A) 200 ms (B) 400 ms (C) 800 ms and (D) 1600 ms. Images A–D were computationally merged by the HDR algorithm to generate the (E) HDR-MR image. The HDR image accentuated the contrast between samples 3 and 4 without suppressing samples 5 and 6 into the background. None of the LDR images, A–D, captured both features simultaneously. While image A had the largest normalized contrast, it also had the poorest SNR. Conversely, images with better SNR had worse normalized contrast, most notably between samples 3 and 4. HDR-MRI combined the complementary features of each image. (F) Quantification of contrast normalized against water. Error bars represent standard deviation.
Figure 5.
A series of images were acquired with constant TR at 4000 ms and TE at (A) 63.81 ms (B) 127.62 ms (C) 255.24 ms and (D) 531.75 ms. Images A–D were computationally merged by the HDR algorithm to generate the (E) HDR image. HDR-MRI accurately captured the intensity difference between samples 9, 10, 11, and 12, which was not achieved in any of the source images. This was shown quantitatively by contrasts normalized against water (F). Image A was poor at differentiating samples 10–12, B and C were poor at differentiating samples 11 and 12, and D was poor at differentiating samples 9 and 10. Error bars represent standard deviation.
Figure 6.
(A) Signal intensities normalized against water at various NF concentrations. Sigmoidal curves illustrate trends in the data and can be thought of as characteristic curves. As the EV interval increased, the characteristic curve slope flattened to span over a wider dynamic range. The characteristic curves of the two averaged LDR images (TE11 and TE521) had the steepest slopes and were analogous to the EV interval 0 condition. (B) Comparison between averaged LDR images and HDR images. The HDR image with EV interval 1.4 showed contrast between every sample from 8 to 12, indicating its large dynamic range. N = 4 was used for the averaged images. Error bars represent standard deviation.
Figure 7.
A series of images were acquired with constant TR at 5632 ms and varying TE as indicated. The same four LDR images were used to generate both the T2 map and the HDR-MR image. Masking of the T2 map was done by manual thresholding. In the HDR image, red and yellow outlines highlight features that were not captured in one or more of the individual LDR images. HDR-MRI captures the same features as T2 mapping, but is less noisy in the low signal regions. Low signal features can be accurately depicted in HDR-MRI even when the features are only visible in a single LDR image.
Figure 8.
Recommended protocol for HDR-MRI when utilizing a commercial package.