Pet/mr imaging a case-based approach pdf download

The use of this approach may therefore introduce severe quantitative errors depending clearly on the location of the region of interest. In terms of quantitative accuracy, it is generally accepted that the inclusion of bone in the AC of brain PET images is essential. On the other hand, the exclusion of bone structures in whole-body imaging introduces quantitative errors mostly in the case of osseous lesions.

This result further highlights the need to use a continuous scale in the LACs rather than a fixed value assigned to segmented tissue regions. UTE sequences have been proposed in MRI for the visualization of bone which has a very short spin—spin relaxation time T2. UTE-based AC involves acquisitions at two echo times, one visualizing bone, while the signal for other tissue types is the same in both images.

Different methodologies have been subsequently proposed in order to provide a three-tissue class air, soft tissue, bone segmentation approach for PET AC [ 9 ] or alternatively to use a triple-echo sequence combining UTE and Dixon to distinguish four tissue classes air, soft tissue, bone, fat tissue [ 10 ].

Similarly, a more detailed study on a region by region basis, considering 25 patients undergoing FDG PET of the brain, has shown that despite a decrease in the measured mean activity underestimation resulting from the use of a three-tissue class UTE-based approach compared to Dixon-based AC, substantial underestimations compared to CT-based AC still occur.

This regional variability in the measured differences throughout the brain is a clear issue for neurological applications. Furthermore, there seems to be a lack of standardization with respect to the UTE protocols for AC currently in use both in terms of overall acquisition times and selection of individual parameters.

On the other hand, there are only a few reports on the use of UTE sequences in whole-body MR imaging, since their application is hampered by long acquisition times and field inhomogeneities associated with an extended field of view. Therefore, the extension of this approach to whole-body imaging represents a real challenge. This improvement should be both in terms of the accuracy in determining the spatial extent of the structures of interest and in the use of attenuation maps with continuous LACs.

There are different approaches based on the use of the atlas combined with machine learning techniques that have been proposed in order to improve both of these aspects.

The basic idea behind these approaches is to explore a database of paired CT and MR patient images. These images in combination with the acquired MR datasets for a given patient are subsequently used to derive a patient-specific pseudo-CT map. Another advantage of these approaches is that in principle they can provide attenuation maps with continuous LACs, eliminating issues associated with the use of single tissue values that do not account for tissue heterogeneities.

Different MR sequences can be considered in the MR—CT paired datasets used in the atlas in order to improve the overall accuracy of the identified structures of interest.

One of these approaches uses a combination of atlas-derived information and pattern recognition to obtain patient-specific pseudo-CT maps [ 11 ]. A clear issue with any atlas-based approach for brain and whole-body applications is the accurate handling of pathology, interpatient lung density variations, and the presence of metallic implants. Variants of this approach consider the use of multiple MR sequences to improve the identification of different tissues classes and hence to improve the overall atlas registration process and the subsequent pseudo-CT prediction model [ 13 , 14 ].

What is currently missing is large-scale clinical evaluation studies of these atlas and machine-learning approaches that would clearly demonstrate their robustness with respect to the presence of anatomical abnormalities which are largely patient-specific and as such hard to account for in any atlas-based approach.

One has finally to consider the truncation issues associated with MR-based AC maps which can be important in whole-body imaging given that the patients are scanned with arms down because of multiple practical issues. On the other hand, both approaches will clearly benefit from time of flight ToF information to further improve their accuracy. In order to be able to carry out such simultaneous acquisitions, the PET device requires ToF capability. The second option is based on purely exploring information inherent in the acquired PET emission datasets relating to tissue attenuation without the need for any explicit transmission data acquisition.

If one assumes that the true emission data distribution is known there will be only a single attenuation map that can be consistent with that emission distribution and can be therefore estimated. However, the problem is poorly determined and previous attempts have led to poor results with emission data structures contained in the estimated AC maps.

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