Dataset Information

Respiratory Motion Compensation for PET/CT with Motion Information Derived from Matched Attenuation-Corrected Gated PET Data.

ABSTRACT: Respiratory motion degrades the detection and quantification capabilities of PET/CT imaging. Moreover, mismatch between a fast helical CT image and a time-averaged PET image due to respiratory motion results in additional attenuation correction artifacts and inaccurate localization. Current motion compensation approaches typically have 3 limitations: the mismatch among respiration-gated PET images and the CT attenuation correction (CTAC) map can introduce artifacts in the gated PET reconstructions that can subsequently affect the accuracy of the motion estimation; sinogram-based correction approaches do not correct for intragate motion due to intracycle and intercycle breathing variations; and the mismatch between the PET motion compensation reference gate and the CT image can cause an additional CT-mismatch artifact. In this study, we established a motion correction framework to address these limitations. Methods: In the proposed framework, the combined emission-transmission reconstruction algorithm was used for phase-matched gated PET reconstructions to facilitate the motion model building. An event-by-event nonrigid respiratory motion compensation method with correlations between internal organ motion and external respiratory signals was used to correct both intracycle and intercycle breathing variations. The PET reference gate was automatically determined by a newly proposed CT-matching algorithm. We applied the new framework to 13 human datasets with 3 different radiotracers and 323 lesions and compared its performance with CTAC and non-attenuation correction (NAC) approaches. Validation using 4-dimensional CT was performed for one lung cancer dataset. Results: For the 10 18F-FDG studies, the proposed method outperformed (P < 0.006) both the CTAC and the NAC methods in terms of region-of-interest-based SUVmean, SUVmax, and SUV ratio improvements over no motion correction (SUVmean: 19.9% vs. 14.0% vs. 13.2%; SUVmax: 15.5% vs. 10.8% vs. 10.6%; SUV ratio: 24.1% vs. 17.6% vs. 16.2%, for the proposed, CTAC, and NAC methods, respectively). The proposed method increased SUV ratios over no motion correction for 94.4% of lesions, compared with 84.8% and 86.4% using the CTAC and NAC methods, respectively. For the 2 18F-fluoropropyl-(+)-dihydrotetrabenazine studies, the proposed method reduced the CT-mismatch artifacts in the lower lung where the CTAC approach failed and maintained the quantification accuracy of bone marrow where the NAC approach failed. For the 18F-FMISO study, the proposed method outperformed both the CTAC and the NAC methods in terms of motion estimation accuracy at 2 lung lesion locations. Conclusion: The proposed PET/CT respiratory event-by-event motion-correction framework with motion information derived from matched attenuation-corrected PET data provides image quality superior to that of the CTAC and NAC methods for multiple tracers.

SUBMITTER: Lu Y

PROVIDER: S-EPMC6126443 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Respiratory Motion Compensation for PET/CT with Motion Information Derived from Matched Attenuation-Corrected Gated PET Data.

Lu Yihuan Y Fontaine Kathryn K Mulnix Tim T Onofrey John A JA Ren Silin S Panin Vladimir V Jones Judson J Casey Michael E ME Barnett Robert R Kench Peter P Fulton Roger R Carson Richard E RE Liu Chi C

Journal of nuclear medicine : official publication, Society of Nuclear Medicine 20180209 9

Respiratory motion degrades the detection and quantification capabilities of PET/CT imaging. Moreover, mismatch between a fast helical CT image and a time-averaged PET image due to respiratory motion results in additional attenuation correction artifacts and inaccurate localization. Current motion compensation approaches typically have 3 limitations: the mismatch among respiration-gated PET images and the CT attenuation correction (CTAC) map can introduce artifacts in the gated PET reconstructio ...[more]

PMID: 29439015

Similar Datasets

Project description:Methods of reducing respiratory motion blurring in cone-beam CT (CBCT) have been limited to lung where soft tissue contrast is large. Respiration-correlated cone-beam CT uses slow continuous gantry rotation but image quality is limited by uneven projection spacing. This study investigates the efficacy of a novel gated CBCT technique.In gated CBCT, the linac is programmed such that gantry rotation and kV image acquisition occur within a gate around end expiration and are triggered by an external respiratory monitor. Standard CBCT and gated CBCT scans are performed in 22 patients (11 thoracic, 11 abdominal) and a respiration-correlated CT (RCCT) scan, acquired on a standard CT scanner, from the same day serves as a criterion standard. Image quality is compared by calculating contrast-to-noise ratios (CNR) for tumors in lung, gastroesophageal junction (GEJ) tissue, and pancreas tissue, relative to surrounding background tissue. Congruence between the object in the CBCT images and that in the RCCT is measured by calculating the optimized normalized cross-correlation (NCC) following CBCT-to-RCCT rigid registrations.Gated CBCT results in reduced motion artifacts relative to standard CBCT, with better visualization of tumors in lung, and of abdominal organs including GEJ, pancreas, and organs at risk. CNR of lung tumors is larger in gated CBCT in 6 of 11 cases relative to standard CBCT. A paired two-tailed t-test of lung patient mean CNR shows no statistical significance (p = 0.133). In 4 of 5 cases where CNR is not increased, lung tumor motion observed in RCCT is small (range 1.3-5.2 mm). CNR is increased and becomes statistically significant for 6 out of 7 lung patients with > 5 mm tumor motion (p = 0.044). CNR is larger in gated CBCT in 5 of 7 GEJ cases and 3 of 4 pancreas cases (p = 0.082 and 0.192). Gated CBCT yields improvement with lower NCC relative to standard CBCT in 10 of 11, 7 of 7, and 3 of 4 patients for lung, GEJ, and pancreas images, respectively (p = 0.0014, 0.0030, 0.165).Gated CBCT reduces image blurring caused by respiratory motion. The gated gantry rotation yields uniformly and closely spaced projections resulting in improved reconstructed image quality. The technique is shown to be applicable to abdominal sites, where image contrast of soft tissues is low.

Project description:The authors evaluate the energy dependent noise and bias properties of monoenergetic images synthesized from dual-energy CT (DECT) acquisitions. These monoenergetic images can be used to estimate attenuation coefficients at energies suitable for positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging. This is becoming more relevant with the increased use of quantitative imaging by PET/CT and SPECT/CT scanners. There are, however, potential variations in the noise and bias of synthesized monoenergetic images as a function of energy.The authors used analytic approximations and simulations to estimate the noise and bias of synthesized monoenergetic images of water-filled cylinders with different shapes and the NURBS-based cardiac-torso (NCAT) phantom from 40 to 520 keV, the range of SPECT and PET energies. The dual-kVp spectra were based on the GE Lightspeed VCT scanner at 80 and 140 kVp with added filtration of 0.5 mm Cu. The authors evaluated strategies of noise suppression with sinogram smoothing and dose minimization with reduction of tube currents at the two kVp settings. The authors compared the impact of DECT-based attenuation correction with single-kVp CT-based attenuation correction on PET quantitation for the NCAT phantom for soft tissue and high-Z materials of bone and iodine contrast enhancement.Both analytic calculations and simulations displayed the expected minimum noise value for a synthesized monoenergetic image at an energy between the mean energies of the two spectra. In addition the authors found that the normalized coefficient of variation in the synthesized attenuation map increased with energy but reached a plateau near 160 keV, and then remained constant with increasing energy up to 511 keV and beyond. The bias was minimal, as the linear attenuation coefficients of the synthesized monoenergetic images were within 2.4% of the known true values across the entire energy range. Compared with no sinogram smoothing, sinogram smoothing can dramatically reduce noise in the DECT-derived attenuation map. Through appropriate selection of tube currents for high and low kVp scans, DECT can deliver roughly the same amount of radiation dose as that of a single kVp CT scan, but could be used for PET attenuation correction with reduced bias in contrast agent regions by a factor of ≈ 2.6 and slightly reduced RMSE for the total image.When DECT is used for attenuation correction at higher energies, there is a noise amplification that is dependent on the energy of the synthesized monoenergetic image of linear attenuation coefficients. Sinogram smoothing reduces the noise amplification in DECT-derived attenuation maps without increasing bias. With an appropriate selection of CT techniques, a DECT scan with the same radiation dose as a single CT scan can result in a PET image with improved quantitative accuracy.

Project description:BackgroundCone beam computed tomography (CBCT) for radiotherapy image guidance suffers from respiratory motion artifacts. This limits soft tissue visualization and localization accuracy, particularly in abdominal sites. We report on a prospective study of respiratory motion-corrected (RMC)-CBCT to evaluate its efficacy in localizing abdominal organs and improving soft tissue visibility at end expiration.Material and methodsIn an IRB approved study, 11 patients with gastroesophageal junction (GEJ) cancer and five with pancreatic cancer underwent a respiration-correlated CT (4DCT), a respiration-gated CBCT (G-CBCT) near end expiration and a one-minute free-breathing CBCT scan on a single treatment day. Respiration was recorded with an external monitor. An RMC-CBCT and an uncorrected CBCT (NC-CBCT) were computed from the free-breathing scan, based on a respiratory model of deformations derived from the 4DCT. Localization discrepancy was computed as the 3D displacement of the GEJ region (GEJ patients), or gross tumor volume (GTV) and kidneys (pancreas patients) in the NC-CBCT and RMC-CBCT relative to their positions in the G-CBCT. Similarity of soft-tissue features was measured using a normalized cross correlation (NCC) function.ResultsLocalization discrepancy from the end-expiration G-CBCT was reduced for RMC-CBCT compared to NC-CBCT in eight of eleven GEJ cases (mean ± standard deviation, respectively, 0.21 ± 0.11 and 0.43 ± 0.28 cm), in all five pancreatic GTVs (0.26 ± 0.21 and 0.42 ± 0.29 cm) and all ten kidneys (0.19 ± 0.13 and 0.51 ± 0.25 cm). Soft-tissue feature similarity around GEJ was higher with RMC-CBCT in nine of eleven cases (NCC =0.48 ± 0.20 and 0.43 ± 0.21), and eight of ten kidneys (0.44 ± 0.16 and 0.40 ± 0.17).ConclusionsIn a prospective study of motion-corrected CBCT in GEJ and pancreas, RMC-CBCT yielded improved organ visibility and localization accuracy for gated treatment at end expiration in the majority of cases.

Project description:Respiratory motion artifacts and partial volume effects (PVEs) are two degrading factors that affect the accuracy of image quantification in PET/CT imaging. In this article, the authors propose a joint motion and PVE correction approach (JMPC) to improve PET quantification by simultaneously correcting for respiratory motion artifacts and PVE in patients with lung/thoracic cancer. The objective of this article is to describe this approach and evaluate its performance using phantom and patient studies.The proposed joint correction approach incorporates a model of motion blurring, PVE, and object size/shape. A motion blurring kernel (MBK) is then estimated from the deconvolution of the joint model, while the activity concentration (AC) of the tumor is estimated from the normalization of the derived MBK. To evaluate the performance of this approach, two phantom studies and eight patient studies were performed. In the phantom studies, two motion waveforms-a linear sinusoidal and a circular motion-were used to control the motion of a sphere, while in the patient studies, all participants were instructed to breathe regularly. For the phantom studies, the resultant MBK was compared to the true MBK by measuring a correlation coefficient between the two kernels. The measured sphere AC derived from the proposed method was compared to the true AC as well as the ACs in images exhibiting PVE only and images exhibiting both PVE and motion blurring. For the patient studies, the resultant MBK was compared to the motion extent derived from a 4D-CT study, while the measured tumor AC was compared to the AC in images exhibiting both PVE and motion blurring.For the phantom studies, the estimated MBK approximated the true MBK with an average correlation coefficient of 0.91. The tumor ACs following the joint correction technique were similar to the true AC with an average difference of 2%. Furthermore, the tumor ACs on the PVE only images and images with both motion blur and PVE effects were, on average, 75% and 47.5% (10%) of the true AC, respectively, for the linear (circular) motion phantom study. For the patient studies, the maximum and mean AC/SUV on the PET images following the joint correction are, on average, increased by 125.9% and 371.6%, respectively, when compared to the PET images with both PVE and motion. The motion extents measured from the derived MBK and 4D-CT exhibited an average difference of 1.9 mm.The proposed joint correction approach can improve the accuracy of PET quantification by simultaneously compensating for the respiratory motion artifacts and PVE in lung/thoracic PET/CT imaging.

Dataset Information

Respiratory Motion Compensation for PET/CT with Motion Information Derived from Matched Attenuation-Corrected Gated PET Data.

Publications

Respiratory Motion Compensation for PET/CT with Motion Information Derived from Matched Attenuation-Corrected Gated PET Data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets