4335

Motion-corrected simultaneous DCE-MRI and PET for hepatic lesion assessment
Christoph Kolbitsch1, Matteo Ippoliti2, Marcus Makowski2, Winfried Brenner3, and Tobias Schaeffter1

1Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany, 2Department of Radiology, ChariteĢ, Berlin, Germany, 3Department of Nuclear Medicine and BERIC - Berlin Experimental Radionuclide Imaging Center, ChariteĢ, Berlin, Germany

Synopsis

Dynamic contrast enhancement (DCE) MRI is an important clinical imaging tool to characterize hepatic tumors. Especially in combination with PET imaging data, it has been shown to improve diagnostic confidence compared to PET/CT. Nevertheless, respiratory motion is still challenging, requiring multiple breathholds and leading to low scan efficiencies for PET and MR. Here we present a motion-corrected simultaneous DCE-MRI/PET approach which enables imaging with 100% scan efficiency using all data, to improve PET quantification and accurate alignment between both modalities. The proposed technique was evaluated in three patients with hepatic lesions showing an increased contrast-to-noise-ratio of PET tracer uptake by more than 80%.

Introduction

Dynamic contrast enhancement (DCE) MRI is an important clinical imaging tool to characterize hepatic tumors1. Using both MR and PET image information obtained with simultaneous PET/MR has been shown to increase the diagnostic confidence both for benign and malignant liver lesions compared to PET/CT2. One of the main challenges both for MR and PET is the respiratory motion of the liver. Juin et al. have demonstrated that motion fields obtained from a motion-compensated DCE-MR reconstruction can be used to improve the simultaneously acquired PET data for liver applications3. Although this approach demonstrated an improvement in PET uptake values, it provided DCE-MR images with relatively low slice resolution (4.6mm) and low temporal resolution (~20s).

Here we utilize a 3D DCE-MR acquisition which provides motion corrected 3D DCE-MR images with a spatial resolution of 1.5mm3 isotropic and a temporal resolution of 6s4. The proposed scheme allows for motion corrected image reconstruction of simultaneously acquired PET data to improve accuracy of tracer quantification in lesions.

Methods

Data acquisition: 3D MR data acquisition was carried out with a Golden Radial Phase Encoding (GRPE) trajectory (FOV: 280mm3, 1.5mm3 image resolution, TR/TE/FA: 3.3ms/1.4ms/12°, partial Fourier encoding, fat suppression)5. PET and MR data was obtained continuously over 5min. Gadolinium-based contrast agent (Gadoxetate disodium) was injected after approximately 1min of scan time (0.1 mmol/kg). Figure 1 gives an overview of the proposed approach.

Patient population: Three patients were scanned on a simultaneous PET-MR scanner (Biograph mMR, Siemens Healthcare) for staging or restaging of neuroendocrine tumors (NET) 112±20min after injection of 158±17MBq of GA68-DOTATOC (1 female, age: 62±5years, weight: 90±7kg).

Image reconstruction: In a first step, a respiratory self-navigator was obtained from the GRPE data6. Based on this signal, the data was split into eight respiratory phases. An iterative non-Cartesian SENSE reconstruction with temporal and spatial regularization was used to reconstruct eight 3D data sets describing the different respiratory motion states7. 3D non-rigid motion fields were obtained with a spline-based image registration method8. In a second step, these motion fields were included in a motion corrected self-regularized kt-SENSE9 reconstruction to obtain 48 3D dynamic DCE-MR images. For the PET image reconstruction, a 3D OSEM algorithm was used (23 subsets, 3 full iterations)10 and images with a resolution of 2mm3 and 127x344x344 voxels were reconstructed. The MR-based motion fields were transformed to the PET orientation and resolution and included in a motion corrected PET reconstruction to minimize respiratory motion artefacts11. Attenuation correction (AC) maps were also adapted to the different respiratory breathing phases to ensure accurate alignment between PET emission data and AC values. Randoms and scatter correction was carried out separately for each motion state.

Evaluation: We compared the contrast-to-noise (CNR) ratio of multiple lesions measured in the PET data with and without respiratory motion correction.

Results

Figure 2, 3 and 4 show an improved visualization of lesions in MR and PET images allowing for a faster diagnosis and making it easier to distinguish and characterize multiple lesions. MCIR reduces the risk of missing a lesion due to strong motion blurring (Fig. 4). Respiratory motion correction led to an increase in measured uptake values in the lesions which is shown in Figure 5. The increase in CNR depends on the size of the lesion, the breathing pattern, the breathing amplitude and the location of the lesion and range from ~10% to more than 80%. The lesions which we analyzed were between 12mm and 20mm in diameter.

Discussion and Conclusion

We have presented a simultaneous PET-MR approach which provides DCE-MR images with high isotropic spatial and high temporal resolution. Respiratory motion correction is used to ensure high image quality for both MR and PET during the free-breathing acquisition. Motion correction leads to an improved lesions visualization and also increased the measured PET uptake, as a result of reduced motion blurring. Because both MR and PET data are acquired simultaneously during free-breathing and corrected for respiratory motion in the same way, MR and PET images are perfectly aligned. In contrast to the standard approach, which could suffer from misalignment between multi-breathhold MR and (gated) free-breathing PET, this would allow for an accurate pixel-by-pixel comparison of MR and PET information.

Acknowledgements

Support of the German Research Foundation (DFG), project number GRK 2260, BIOQIC is acknowledged.

References

1. Maniam S, Szklaruk J. Magnetic resonance imaging: Review of imaging techniques and overview of liver imaging. World J Radiol. 2010;2(8):309-322.

2. Beiderwellen K, Gomez B, Buchbender C, et al. Depiction and characterization of liver lesions in whole body [ 18F]-FDG PET/MRI. Eur J Radiol. 2013;82(11):e669-e675.

3. Fuin N, Catalano OA, Scipioni M, et al. Concurrent Respiratory Motion Correction of Abdominal PET and Dynamic Contrast-Enhanced–MRI Using a Compressed Sensing Approach. J Nucl Med. 2018;59(9):1474-1479.

4. Ippoliti M, Makowski M, Schaeffter T, Kolbitsch C. 3D non-rigid motion-corrected dynamic contrast enhanced MRI of the liver with high isotropic resolution. In: Proceedings of Joint Annual Meeting ISMRM-ESMRMB, Paris, France. ; 2018:476.

5. Prieto C, Uribe S, Razavi R, Atkinson D, Schaeffter T. 3D Undersampled Golden-Radial Phase Encoding for DCE-MRA Using Inherently Regularized Iterative SENSE. Magn Reson Imaging. 2010;64:514-526.

6. Buerger C, Clough RE, King AP, Schaeffter T, Prieto C. Nonrigid Motion Modeling of the Liver From 3-D Undersampled Self-Gated Golden-Radial Phase Encoded MRI. IEEE Trans Med Imaging. 2012;31(3):805-815.

7. Cruz G, Atkinson D, Buerger C, Schaeffter T, Prieto C. Accelerated motion corrected three-dimensional abdominal MRI using total variation regularized SENSE reconstruction. Magn Reson Med. 2016;75(4):1484-1498.

8. Rueckert D, Sonoda LI, Hayes C, Hill DLG, Leach MO, Hawkes DJ. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging. 1999;18(8):712-721.

9. Tsao J, Boesiger P, Pruessmann KP. k-t BLAST and k-t SENSE: dynamic MRI with high frame rate exploiting spatiotemporal correlations. Magn Reson Med. 2003;50(5):1031-1042.

10. Thielemans K, Tsoumpas C, Mustafovic S, et al. STIR: software for tomographic image reconstruction release 2. Phys Med Biol. 2012;57(4):867-883.

11. Qiao F, Pan T, Clark Jr JW, Mawlawi OR. A motion-incorporated reconstruction method for gated PET studies. Phys Med Biol. 2006;51(15):3769-3783.

Figures

Figure 1: Overview of the proposed approach. (a) Free-breathing MR data is acquired using a 3D Golden Radial Phase Encoding (GRPE) scheme simultaneously to a PET data acquisition for 5min. Gadolinium-based contrast agent is injected after 1min of scan time. (b) Based on a MR self-navigator obtained from the GRPE data, eight respiratory phases are reconstructed. A non-rigid image registration approach is used to obtained motion fields. These motion fields are utilized in a motion corrected image reconstruction of the dynamic MR and the static PET images.

Figure 2: Transversal MR and PET images of a 61-year-old male patient with hepatic and orbital metastasized NET. A lesion in the liver (white arrow) can be seen much better on the MCIR MR and PET images with strongly increased PET uptake values and improved contrast to surrounding liver tissue. Uncorr – without motion correction, MCIR – with respiratory motion correction. The MR image shows the last phase (i.e. ~ 4 min after contrast injection) of the DCE data with a high contrast between lesion and liver tissue.

Figure 3: Sagittal MR and PET images of a 58-year-old male patient with multiple liver metastases. Respiratory motion correction allows for a better distinction between multiple metastases at the dome of the liver (white arrows). MCIR also leads to an improved sharpness of the liver in the MR data (red arrows) and reduced blurring of PET uptake in surrounding organs, such as the kidneys (blue arrows). Uncorr – without motion correction, MCIR – with respiratory motion correction. The MR image shows the last phase (i.e. ~ 4 min after contrast injection) of the DCE data with a high contrast between lesion and liver tissue.

Figure 4: Sagittal MR and PET images of a 85-year-old female patient with cancer of the small intestine and hepatic metastases. A small lesion at the dome of the liver was strongly blurred due to respiratory motion which made it challenging to detect (white arrows). MCIR increased the measured uptake values and led to an improved visualization. Uncorr – without motion correction, MCIR – with respiratory motion correction. The MR image shows the last phase (i.e. ~ 4 min after contrast injection) of the DCE data with a high contrast between lesion and liver tissue.

Figure 5: Zoomed PET images of lesions in two different patients ((a) and (b)) comparing uptake without motion correction (Uncorr) and with respiratory motion correction (MCIR). The line profile is drawn in foot-head direction and shows the increase in measured uptake using the proposed motion correction scheme.

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)
4335