Daphné Wallach

Super-Resolution in Respiratory Synchronized Positron Emission Tomography

Respiratory motion is a major source of reduced quality in positron emission tomography (PET). In order to minimize its effects, the use of respiratory synchronized acquisitions, leading to gated frames, has been suggested. Such frames, however, are of low signal-to-noise ratio (SNR) as they contain reduced statistics. Super-resolution (SR) techniques make use of the motion in a sequence of images in order to improve their quality. They aim at enhancing a low-resolution image belonging to a sequence of images representing different views of the same scene. In this work, a maximum a posteriori (MAP) super-resolution algorithm has been implemented and applied to respiratory gated PET images for motion compensation. An edge preserving Huber regularization term was used to ensure convergence. Motion fields were recovered using a B-spline based elastic registration algorithm. The performance of the SR algorithm was evaluated through the use of both simulated and clinical datasets by assessing image SNR, as well as the contrast, position and extent of the different lesions. Results were compared to summing the registered synchronized frames on both simulated and clinical datasets. The super-resolution image had higher SNR (by a factor of over 4 on average) and lesion contrast (by a factor of 2) than the single respiratory synchronized frame using the same reconstruction matrix size. In comparison to the motion corrected or the motion free images a similar SNR was obtained, while improvements of up to 20% in the recovered lesion size and contrast were measured. Finally, the recovered lesion locations on the SR images were systematically closer to the true simulated lesion positions. These observations concerning the SNR, lesion contrast and size were confirmed on two clinical datasets included in the study. In conclusion, the use of SR techniques applied to respiratory motion synchronized images lead to motion compensation combined with improved image SNR and contrast, without any increase in the overall acquisition times.

Comparison of freehand-navigated and aiming device-navigated targeting of liver lesions

Accurate needle placement is crucial for the success of percutaneous radiological needle interventions. We compared three guiding methods using an optical‐based navigation system: freehand, using a stereotactic aiming device and active depth control, and using a stereotactic aiming device and passive depth control.

Angiographic C-Arm CT– Versus MDCT-Guided Stereotactic Punctures of Liver Lesions: Nonrigid Phantom Study

OBJECTIVE Angiographic C-arm CT may allow performing percutaneous stereotactic tumor ablations in the interventional radiology suite. Our purpose was to evaluate the accuracy of using C-arm CT for single and multimodality image fusions and to compare the targeting accuracy of liver lesions with the reference standard of MDCT. MATERIALS AND METHODS C-arm CT and MDCT scans were obtained of a nonrigid rapid prototyping liver phantom containing five 1-mm targets that were placed under skin-simulating deformable plastic foam. Target registration errors of image fusion were evaluated for single-modality and multimodality image fusions. A navigation system and stereotactic aiming device were used to evaluate target positioning errors on postinterventional scans with the needles in place fused with the C-arm CT or MDCT planning images. RESULTS Target registration error of the image fusion showed no significant difference (p > 0.05) between both modalities. In five series with a total of 25 punctures for each modality, the lateral target positioning error (i.e., the lateral distance between the needle tip and the planned trajectory) was similar for C-arm CT (mean [± SD], 1.6 ± 0.6 mm) and MDCT (1.82 ± .97 mm) (p = 0.33). CONCLUSION In a nonrigid liver phantom, angiographic C-arm CT may provide similar image fusion accuracy for comparison of intra- and postprocedure control images with the planning images and enables stereotactic targeting accuracy similar to that of MDCT.

SU-E-J-113: Impact of 4D PET and Motion Correction in the Delineation of Gross Tumor Volume for Radiotherapy Treatment Planning in Lung Cancer

Purpose: To quantitatively assess the impact of motion correction in 4D‐PET images on the accuracy of automatic lungtumour volume delineation for radiotherapytreatment planning and the resulting dosimetry modifications.Methods: : Simulated 18F‐FDG‐PET data using the NURBS‐based Cardiac‐Torso phantom and Geant4 Application for Tomography Emission were considered. Homogeneous and heterogeneous spherical cases were designed with two tumor‐to‐background (T:B) contrasts (4 and 10) and 3 motion amplitudes (0.5, 1.5 and 2.5cm). Two more realistic cases derived from real clinical PET/CT datasets were also generated. Data were corrected for respiratory motion using two Methods: reconstruction incorporating elastic transformations and super‐resolution. The tumours were segmented with the Fuzzy Locally Adaptive Bayesian algorithm on each respiratory phase with or without correction and on the motion average image. For heterogeneous cases global and boost volumes were delineated. The union of the volumes at each phase with or without motion correction, the average PET volume and the volume with internal margins added to one respiratory phase were compared to the union of the simulated volumes (ground‐truths). For each of these target volumes, IMRT planning was used to compare the different motion management approaches in terms of impact on dosimetry.Results: The smallest and largest volumes were obtained on the average PET and the one with internal margins respectively. The best compromise between sensitivity and positive predictive value were obtained with corrected PET, with similar results for both corrections. The best compromise between ground‐truth volume coverage and organs‐at‐risk sparing was achieved by the volumes with most accurate delineation. The volume with margins always covered the ground‐truth Conclusions: 4D‐PET with motion correction leads to more accurate delineation of lungtumours and boost volumes in the presence of respiratory motion. This also leads to improved margins for treatment planning.