A unifying view on extended phase graphs and Bloch simulations for quantitative MRI

Quantitative MRI methods and learning-based algorithms require exact forward simulations. One critical factor to correctly describe magnetization dynamics is the effect of slice-selective RF pulses. While contemporary simulation techniques correctly capture their influence, they only provide final magnetization distributions, require to be run for each parameter set separately, and make it hard to derive general theoretical conclusions and to generate a fundamental understanding of echo formation in the presence of slice-profile effects. This work aims to provide a mathematically exact framework, which is equally intuitive as extended phase graphs (EPGs), but also considers slice-profiles through their natural spatial representation. We show, through an analytical, hybrid Bloch-EPG formalism, that the spatially-resolved EPG approach allows to exactly predict the signal dependency on off-resonance, spoiling moment, microscopic dephasing, and echo time. We also demonstrate that our formalism allows to use the same phase graph to simulate both gradient-spoiled and balanced SSFP-based MR sequences. We present a derivation of the formalism and identify the connection to existing methods, i.e. slice-selective Bloch, slice-selective EPG, and the partitioned EPG. As a use case, the proposed hybrid Bloch-EPG framework is applied to MR Fingerprinting.

Measurement of Slice Thickness of a Medical Image using a Wedge Phantom

Slice thickness measurement is an essential parameter of performance evaluation for the medical imaging system. This study demonstrates the characteristics of slice thickness measurement for medical images using a wedge digital phantom. A wedge-shaped digital phantom was generated and the ideal edge response function (ERF) was extracted from line profile in single slice. The corresponding slice profile was calculated by the derivative of ERF. The wedge phantom obtained by applying gaussian convolving to a digital phantom was also generated to produce similarities to real medical images. Unlike an ideal slice profile, it was estimated by the full width half maximum (FWHM) of the Gaussian function fitting. In addition, we evaluate the effect of background noise and wedge angle for the wedge phantom. The estimated FWHM of the image with noise added was increased by 10.4% compared to the image without noise. However, the FWHM from the line profiles averaging on the noise-added image was estimated by 0.2% reduction than the noise-free image. The line profiles averaging improves the accurate measurement of slice thickness by decreasing the noise. Despite the wedge angle changing from 45 to 30 degrees, the resulting FWHM was estimated to have less than 1% difference. However, the length of the line profile to be acquired should be increased as the wedge angle increases.

Overall Bone Structure as Assessed by Slice-by-Slice Profile

Quantifying the inner structure of bones is central to various analyses dealing with the phenotypic evolution of animals with an ossified skeleton. Computed tomography allows to assess the repartition of bone tissue within an entire skeletal element. Two parameters of importance for such analyses are the global compactness (Cg) and total cross-sectional area (Tt.Ar). However, no open-source, time-efficient methods are available to acquire these parameters for whole bones. A methodology to assess the variation of these parameters along a profile following one of the studied bone’s anatomical axes is also wanting. Here I present an ImageJ macro and associated R script to automatically acquire Cg and Tt.Ar along an axis of the skeletal element of interest using a slice-by-slice approach. No manual segmentation is required and several bones can be present on the analysed scan, as long as the bone of interest is isolated and the largest element on each slice. While some bias might be involved by the automatic acquisition, semi-automatic slice exclusion and correction procedures can be used to efficiently account for it. As a test case, µCT-data was gathered for the mid-lumbar vertebra of over 70 mammals. The two evaluated correction procedures proved to perform equally well, with a slight advantage for the one relying on the exclusion of local outliers. The presented macro allows to efficiently build a dataset concerned with the quantification of bone inner structure. The code being readily available, further improvement of the methodology and adjustment to particular needs can be easily performed.