Evaluation of a Motion Correction Algorithm for C-Arm Computed Tomography Acquired During Transarterial Chemoembolization

Abstract

Purpose The aim of this retrospective study was to evaluate the feasibility of a motion correction 3D reconstruction prototype technique for C-arm computed tomography (CACT). Material and Methods We included 65 consecutive CACTs acquired during transarterial chemoembolization of 54 patients (47 m,7f; 67 ± 11.3 years). All original raw datasets (CACTOrg) underwent reconstruction with and without volume punching of high-contrast objects using a 3D image reconstruction software to compensate for motion (CACTMC_bone;CACTMC_no bone). Subsequently, the effect on image quality (IQ) was evaluated using objective (image sharpness metric) and subjective criteria. Subjective criteria were defined by vessel geometry, overall IQ, delineation of tumor feeders, the presence of foreign material-induced artifacts and need for additional imaging, assessed by two independent readers on a 3-(vessel geometry and overall IQ) or 2-point scale, respectively. Friedman rank-sum test and post hoc analysis in form of pairwise Wilcoxon signed-rank test were computed and inter-observer agreement analyzed using kappa test. Results Objective IQ as defined by an image sharpness metric, increased from 273.5 ± 28 (CACTOrg) to 328.5 ± 55.1 (CACTMC_bone) and 331 ± 57.8 (CACTMC_no bone; all p < 0.0001). These results could largely be confirmed by the subjective analysis, which demonstrated predominantly good and moderate inter-observer agreement, with best agreement for CACTMC_no bone in all categories (e.g., vessel geometry: CACTOrg: κ = 0.51, CACTMC_bone: κ = 0.42, CACTMC_no bone: κ = 0.69). Conclusion The application of a motion correction algorithm was feasible for all data sets and led to an increase in both objective and subjective IQ parameters. Level of Evidence 3

Effectuality study of a 3D motion correction algorithm in C-arm CTs of severely impaired image quality during transarterial chemoembolization

“…The benefit of adjunct CACT versus stand-alone DSA is well documented for a variety of procedures, but especially for TACE [1][2][3][4][5][6][7][8][9][10][11], where improved soft-tissue resolution and elimination of vessel superposition helps to identify all tumor feeders thus supporting a more selective TACE. As the value of CACT largely depends on good image quality [6,7,25], the 3D-motion compensating image algorithm has shown to improve both, objective and subjective image quality criteria, in liver and lung, respectively [22,23]. Having proven beneficial in optimizing sparse objects such as vessels, this algorithm includes manual volume punching for bone removal [19][20][21][22][23].…”

“…Based on previously published work determining influencing factors on image quality in CACTs as well as the IQ-improving effect of the 3D motion compensating algorithm, we purposefully selected datasets with poor image quality as main inclusion requirement (see Fig. 1 ), represented by substantially limited visualization of both central as well as peripheral hepatic arteries, including distinct blurriness of vessel margins and/ or severe cardiorespiratory motion artifacts [ 22 , 23 ]. The study population thus comprised of 27 CACTs in 26 patients (18 m, 8 f; mean age: 69.7 years ± 10.7 SD)– one patient having received a CACT of both the left and the right liver artery due to variant hepatic anatomy—with patient characteristics shown in Table 1 .…”

“…The 3D reconstruction prototype software, developed and modified by the manufacturer (Siemens Healthcare, Forchheim, Germany), was installed on a dedicated workstation (syngo X Workplace ® , Software Version VD20C, Siemens Healthcare, Forchheim, Germany) and applied to the original raw datasets. These were retrospectively modified by the algorithm's utilization of iterative motion estimation and compensation of a 4D deformable motion, as previously published elsewhere [19][20][21][22][23]. For motion correction of selective CACT images of the hepatic arteries, we used 500 iterations in total and an iteration update/display every 100 iterations.…”

“…Motion artifacts due to patient movement on the angiography table, breathing or cardiac pulsation may cause loss of critical periprocedural information as well as severely compromise the interventional outcome [1][2][3][4][5]. Based on a vascular reconstruction algorithm (CAVAREC, Siemens Healthcare, Forchheim, Germany), initially developed for motion compensation in 3D cardiac imaging studies [19][20][21], its beneficial effect on diagnostic CACT datasets by evaluating both objective and subjective IQ criteria, has been previously described [22,23]. While the mean dataset IQ in the database was serviceable, the current study focused on severely impaired CACT images to diminish any influence of a CACT's good baseline image quality on the algorithm's effectiveness and to emphasize the potential value of CACT postprocessing methods in decreasing motion artefacts.…”

Flat-panel conebeam CT in the clinic: history and current state

“…; (3) calculation, using a robust cost function, of a nonperiodic, smooth elastic 3D motion vector field describing the nonrigid deformation map between the projection images and the motion-blurred volume; and (4) re-reconstruction of the volume using the motion-corrected projection images. Iterate over steps 2 to 4 using an appropriate stopping criterion [117][118][119][120] (see Fig. 21 for an example of achievable image quality improvement).…”