Introduction

In clinical PET/MR patient examinations, the attenuation and scatter of photons caused by the patient tissue is corrected by using an MR-based µmap. This Dixon-VIBE sequence-based µmap segments several tissue classes and allocates specific linear attenuation coefficient (LAC) to the tissue classes. This is established standard in current PET/MR systems [1]. The 4 tissue classes (background air, fat, lung, and soft tissue) provide an approximation of the patient attenuation, although the high attenuating bone parts are not considered. Additionally, the limited diameter of the MR Field-of-View (FoV) compared to the PET-FoV may lead to truncation artifacts along the patient arms. Recent developments in MR-based attenuation correction (AC) now allow the addition of complementary information to the human µmap. In this context, the HUGE technique allows for extension of the MR-based FoV to eliminate truncation artifacts [2]. Further, a model-based atlas allows for addition of bone attenuation coefficients to the MR-based µmaps [3].

Material and Methods

All measurements were performed on an integrated whole-body PET/MR hybrid system (Biograph mMR, Siemens Healthcare, Erlangen, Germany). 43 patients (22 female and 21 male, mean age 52±15 years) with a wide range of indications were injected with radiotracer 18F-FDG and underwent a whole-body PET/MR examination. For attenuation correction (AC), four different µmaps were generated offline for each patient to evaluate the influence of the additional information on the SUV_max value in PET active lesions. 1. The 4-component-µmap serves as reference standard (Fig. 1A) and was compared individually to 2. the 5-component-µmap including bone (Fig. 1B), 3. the 4-component-µmap+HUGE (Fig. 1C), and 4. the 5-component-µmap+HUGE (Fig. 1D). The four offline-generated patient µmaps are based on a coronal Dixon-VIBE (volumetric interpolated breath-hold examination) sequence with the following scan parameters: parallel imaging factor, 5; matrix 406x450 with 2.3×2.3 mm2 pixel size, 128 slices a 3.2 mm, TR  4.0 ms, TE  1.2 ms, TA  =  13 s per bed position. PET data of the patients were reconstructed iteratively using 3D OP-OSEM (3 subsets, 21 iterations) using the standard reconstruction parameters used for PET/MR imaging: 344×344 with (2.09×2.09) mm2 pixel spacing and 127 slices with 2.03 mm slice thickness. To create four comparable AC image datasets, the PET rawdata and the µmaps were retrospectively reconstructed using the system provided tool ‘RetroRecon’ by Siemens. For each patient, the non-AC PET data was reconstructed four times using each of the 4 different µmaps. To visualize activity differences between the four reconstructed AC PET datasets, the AC PET data using new features (HUGE and bone) was divided by the reference standard 4-component µmap. The four AC-PET image datasets were evaluated and read out by an experienced radiologist and nuclear medicine specialist in consensus. Image reading consisted of identifying the lesions, depicting volume-of-interest and computing the SUV_max four times for each lesion, this was done with the help of syngo.via workstation (Siemens Healthcare, Erlangen, Germany).

Results

Out of the 43 examined patients 26 patients could be identified with overall 57 malignant lesions (Table 1). Using the offline generated human µmaps, the AC PET data were used for lesion detection (Figure 2). The three graphs in Figure 3 show the relation between 2 µmaps each, where the x-axis represents the reference standard with the SUV_max of the 4-component-µmap. The y-axis provides the SUV_max values of identical (congruent) lesions with 4-component-µmap+HUGE (Fig. 3A). Consequently, each lesion above the diagonal line shows an increase of the SUV_max by using additional information. The mean SUV_max of all 57 lesions increases by 14%±12% when comparing the 4-component-µmap with the 4-component-µmap+HUGE. In figure 3B the influence of additional bone information is depicted, the mean SUV_max of all 57 lesions increases by 14%±12% when comparing the 4-component-µmap with the 5-component-µmap. In figure 3C the SUV_max of the 4-component-µmap and the 5-component-µmap+HUGE is depicted. The mean SUV_max of all 57 lesions increases by 24%±19% in total when using all additional information compared to the 4-component-µmap.

Discussion

It was shown that adding new features such as MR-based truncation correction with HUGE and model-based addition of bone to the standard Dixon-VIBE attenuation correction have measurable impact on the PET-quantification in a patient setting. Depending on the relative position of each individual lesion within the body and with regard to truncations and bone, the impact will vary. Accordingly and intuitively, the SUV_max of bone lesions increases comparably stronger than that of soft tissue lesions when adding bone attenuation to the µmap. These results can be seen as an important step towards improved MR-based attenuation correction in whole-body PET/MR hybrid imaging.

Acknowledgements

No acknowledgement found.