4D-CT imaging of a volume influenced by respiratory motion on multi-slice CT

We have previously developed a retrospective 4D‐MRI technique using body area as the respiratory surrogate, but generally, the reconstructed 4D MR images suffer from severe or mild artifacts mainly caused by irregular motion during image acquisition. Those image artifacts may potentially affect the accuracy of tumor target delineation or the shape representation of surrounding nontarget tissues and organs. So the purpose of this study is to propose an approach employing principal component analysis (PCA), combined with a linear polynomial fitting model, to remodel the displacement vector fields (DVFs) obtained from deformable image registration (DIR), with the main goal of reducing the motion artifacts in 4D MR images. Seven patients with hepatocellular carcinoma (2/7) or liver metastases (5/7) in the liver, as well as a patient with non‐small cell lung cancer (NSCLC), were enrolled in an IRB‐approved prospective study. Both CT and MR simulations were performed for each patient for treatment planning. Multiple‐slice, multiple‐phase, cine‐MRI images were acquired in the axial plane for 4D‐MRI reconstruction. Single‐slice 2D cine‐MR images were acquired across the center of the tumor in axial, coronal, and sagittal planes. For a 4D MR image dataset, the DVFs in three orthogonal direction (inferior–superior (SI), anterior–posterior (AP), and medial–lateral (ML)) relative to a specific reference phase were calculated using an in‐house DIR algorithm. The DVFs were preprocessed in three temporal and spatial dimensions using a polynomial fitting model, with the goal of correcting the potential registration errors introduced by three‐dimensional DIR. Then PCA was used to decompose each fitted DVF into a linear combination of three principal motion bases whose spanned subspaces combined with their projections had been validated to be sufficient to represent the regular respiratory motion. By wrapping the reference MR image using the remodeled DVFs, ‘synthetic’ MR images with reduced motion artifacts were generated at selected phase. Tumor motion trajectories derived from cine‐MRI, 4D CT, original 4D MRI, and ‘synthetic’ 4D MRI were analyzed in the SI, AP, and ML directions, respectively. Their correlation coefficient (CC) and difference (D) in motion amplitude were calculated for comparison. Of all the patients, the means and standard deviations (SDs) of CC comparing ‘synthetic’ 4D MRI and cine‐MRI were 0.98±0.01,0.98±0,01, and 0.99±0.01 in SI, AP, and ML directions, respectively. The mean±SD Ds were 0.59±0.09 mm,0.29±0.10 mm, and 0.15±0.05 mm in SI, AP and ML directions, respectively. The means and SDs of CC comparing ‘synthetic’ 4D MRI and 4D CT were 0.96±0.01,0.95±0.01, and 0.95±0.01 in SI, AP, and ML directions, respectively. The mean±SD Ds were 0.76±0.20 mm,0.33±0.14 mm, and 0.19±0.07 mm in SI, AP, and ML directions, respectively. The means and SDs of CC comparing ‘synthetic’ 4D MRI and original 4D MRI were 0.98±0.01,0.98±0.01, and 0.97±0.01 in SI, AP, and ML directions, respectively. The mean±SD Ds were 0.58±0.10 mm...

Section: Introductionmentioning

confidence: 99%

Reducing motion artifacts in 4D MR images using principal component analysis (PCA) combined with linear polynomial fitting model

Yang

Wang

Yin

et al. 2015

“…( 21 ) Each phase of CT scan was named after percentage in respiration phases: the 0% phase CT scan corresponding to end‐of‐inspiration was named CT0% and the 50% phase CT scan corresponding to end‐of‐expiration was named CT50%. After acquisition of 4D CT scan set, two CT scans were reconstructed, an average (AVG) and a maximum‐intensity‐projection (MIP) scan, by taking the average and maximum CT numbers, respectively, in the 4D CT dataset at each pixel point.…”

Section: Methodsmentioning

confidence: 99%

Compensation method for respiratory motion in proton treatment planning for mobile liver cancer

Jeong

Lee

Yoo

et al. 2013

We evaluated the dosimetric effect of a respiration motion, and sought an effective planning strategy to compensate the motion using four‐dimensional computed tomography (4D CT) dataset of seven selected liver patients. For each patient, we constructed four different proton plans based on: (1) average (AVG) CT, (2) maximum‐intensity projection (MIP) CT, (3) AVG CT with density override of tumor volume (OVR), and (4) AVG CT with field‐specific proton margin which was determined by the range difference between AVG and MIP plans (mAVG). The overall effectiveness of each planning strategy was evaluated by calculating the cumulative dose distribution over an entire breathing cycle. We observed clear differences between AV G and MIP CT‐based plans, with significant underdosages at expiratory and inspiratory phases, respectively. Only the mAVG planning strategy was fully successful as the field‐specific proton margin applied in the planning strategy complemented both the limitations of AVG and MIP CT‐based strategies. These results demonstrated that respiration motion induced significant changes in dose distribution of 3D proton plans for mobile liver cancer and the changes can be effectively compensated by applying field‐specific proton margin to each proton field.PACS numbers: 87.55.D; 87.53.Bn; 87.53.Jw; 87.55.dk

“…4D CT scans for these patients were acquired on a GE Discovery PET/CT Scanner (General Electric Medical Systems, Waukesha, WI) equipped with the real‐time position management (RPM) system (Varian Medical Systems, Palo Alto, CA) for monitoring the patients' breathing, using previously described acquisition techniques (19) . Ten reconstructed phase bins were used, yielding 10 full‐field volumetric image datasets per breathing cycle for each patient.…”

Section: Methodsmentioning

confidence: 99%

An automated method for comparing motion artifacts in cine four‐dimensional computed tomography images

Cui

Jew

Hong

et al. 2012

The aim of this study is to develop an automated method to objectively compare motion artifacts in two four‐dimensional computed tomography (4D CT) image sets, and identify the one that would appear to human observers with fewer or smaller artifacts. Our proposed method is based on the difference of the normalized correlation coefficients between edge slices at couch transitions, which we hypothesize may be a suitable metric to identify motion artifacts. We evaluated our method using ten pairs of 4D CT image sets that showed subtle differences in artifacts between images in a pair, which were identifiable by human observers. One set of 4D CT images was sorted using breathing traces in which our clinically implemented 4D CT sorting software miscalculated the respiratory phase, which expectedly led to artifacts in the images. The other set of images consisted of the same images; however, these were sorted using the same breathing traces but with corrected phases. Next we calculated the normalized correlation coefficients between edge slices at all couch transitions for all respiratory phases in both image sets to evaluate for motion artifacts. For nine image set pairs, our method identified the 4D CT sets sorted using the breathing traces with the corrected respiratory phase to result in images with fewer or smaller artifacts, whereas for one image pair, no difference was noted. Two observers independently assessed the accuracy of our method. Both observers identified 9 image sets that were sorted using the breathing traces with corrected respiratory phase as having fewer or smaller artifacts. In summary, using the 4D CT data of ten pairs of 4D CT image sets, we have demonstrated proof of principle that our method is able to replicate the results of two human observers in identifying the image set with fewer or smaller artifacts.PACS number: 87.57.cp; 87.57.N‐