List-mode-based reconstruction for respiratory motion correction in PET using non-rigid body transformations

Multiparametric Oncologic Hybrid Imaging: Machine Learning Challenges and Opportunities

BACKGROUND

Machine learning (ML) is considered an important technology for future data analysis in health care.

METHODS

The inherently technology-driven fields of diagnostic radiology and nuclear medicine will both benefit from ML in terms of image acquisition and reconstruction. Within the next few years, this will lead to accelerated image acquisition, improved image quality, a reduction of motion artifacts and - for PET imaging - reduced radiation exposure and new approaches for attenuation correction. Furthermore, ML has the potential to support decision making by a combined analysis of data derived from different modalities, especially in oncology. In this context, we see great potential for ML in multiparametric hybrid imaging and the development of imaging biomarkers.

RESULTS AND CONCLUSION

In this review, we will describe the basics of ML, present approaches in hybrid imaging of MRI, CT, and PET, and discuss the specific challenges associated with it and the steps ahead to make ML a diagnostic and clinical tool in the future.

KEY POINTS

· ML provides a viable clinical solution for the reconstruction, processing, and analysis of hybrid imaging obtained from MRI, CT, and PET..

On the correction of respiratory motion-induced image reconstruction errors in positron-emission tomography-guided radiation therapy

Background and purpose

Free breathing (FB) positron emission tomography (PET) images are routinely used in radiotherapy for lung cancer patients. Respiration-induced artifacts in these images compromise treatment response assessment and obstruct clinical implementation of dose painting and PET-guided radiotherapy. The purpose of this study is to develop a blurry image decomposition (BID) method to correct motion-induced image-reconstruction errors in FB-PETs.

Materials and methods

Assuming a blurry PET is represented as an average of multi-phase PETs. A four-dimensional computed-tomography image is deformably registered from the end-inhalation (EI) phase to other phases. With the registration-derived deformation maps, PETs at other phases can be deformed from a PET at the EI phase. To reconstruct the EI-PET, the difference between the blurry PET and the average of the deformed EI-PETs is minimized using a maximum-likelihood expectation-maximization algorithm. The developed method was evaluated with computational and physical phantoms as well as PET/CT images acquired from three patients.

Results

The BID method increased the signal-to-noise ratio from 1.88 ± 1.05 to 10.5 ± 3.3 and universal-quality index from 0.72 ± 0.11 to 1.0 for the computational phantoms, and reduced the motion-induced error from 69.9% to 10.9% in the maximum of activity concentration and from 317.5% to 8.7% in the full width at half maximum of the physical PET-phantom. The BID-based corrections increased the maximum standardized-uptake values by 17.7 ± 15.4% and reduced tumor volumes by 12.5 ± 10.4% on average for the three patients.

Conclusions

The proposed image-decomposition method reduces respiration-induced errors in PET images and holds potential to improve the quality of radiotherapy for thoracic and abdominal cancer patients.

Multiparametric Oncologic Hybrid Imaging: Machine Learning Challenges and Opportunities

BACKGROUND

 Machine learning (ML) is considered an important technology for future data analysis in health care.

METHODS

 The inherently technology-driven fields of diagnostic radiology and nuclear medicine will both benefit from ML in terms of image acquisition and reconstruction. Within the next few years, this will lead to accelerated image acquisition, improved image quality, a reduction of motion artifacts and - for PET imaging - reduced radiation exposure and new approaches for attenuation correction. Furthermore, ML has the potential to support decision making by a combined analysis of data derived from different modalities, especially in oncology. In this context, we see great potential for ML in multiparametric hybrid imaging and the development of imaging biomarkers.

RESULTS AND CONCLUSION

 In this review, we will describe the basics of ML, present approaches in hybrid imaging of MRI, CT, and PET, and discuss the specific challenges associated with it and the steps ahead to make ML a diagnostic and clinical tool in the future.

KEY POINTS

  · ML provides a viable clinical solution for the reconstruction, processing, and analysis of hybrid imaging obtained from MRI, CT, and PET..

CITATION FORMAT

· Küstner T, Hepp T, Seith F. Multiparametric Oncologic Hybrid Imaging: Machine Learning Challenges and Opportunities. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1718-4128.

PET respiratory motion correction: quo vadis?

Positron emission tomography (PET) respiratory motion correction has been a subject of great interest for the last twenty years, prompted mainly by the development of multimodality imaging devices such as PET/computed tomography (CT) and PET/magnetic resonance imaging (MRI). PET respiratory motion correction involves a number of steps including acquisition synchronization, motion estimation and finally motion correction. The synchronization steps include the use of different external device systems or data driven approaches which have been gaining ground over the last few years. Patient specific or generic motion models using the respiratory synchronized datasets can be subsequently derived and used for correction either in the image space or within the image reconstruction process. Similar overall approaches can be considered and have been proposed for both PET/CT and PET/MRI devices. Certain variations in the case of PET/MRI include the use of MRI specific sequences for the registration of respiratory motion information. The proposed review includes a comprehensive coverage of all these areas of development in field of PET respiratory motion for different multimodality imaging devices and approaches in terms of synchronization, estimation and subsequent motion correction. Finally, a section on perspectives including the potential clinical usage of these approaches is included.

Motion in nuclear cardiology imaging: types, artifacts, detection and correction techniques

In this paper, the authors review the field of motion detection and correction in nuclear cardiology with single photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging systems. We start with a brief overview of nuclear cardiology applications and description of SPECT and PET imaging systems, then explaining the different types of motion and their related artefacts. Moreover, we classify and describe various techniques for motion detection and correction, discussing their potential advantages including reference to metrics and tasks, particularly towards improvements in image quality and diagnostic performance. In addition, we emphasize limitations encountered in different motion detection and correction methods that may challenge routine clinical applications and diagnostic performance.

General simultaneous motion estimation and image reconstruction (G-SMEIR)

To achieve better performance for 4D multi-frame reconstruction with the parametric motion model (MF-PMM), a general simultaneous motion estimation and image reconstruction (G-SMEIR) method is proposed. In G-SMEIR, projection domain motion estimation and image domain motion estimation are performed alternatively to achieve better 4D reconstruction. This method can mitigate the local optimum trapping problem in either domain. To improve computational efficiency, the image domain motion estimation is accelerated by adapting fast convergent algorithms and graphics processing unit (GPU) computing. The proposed G-SMEIR method is tested using a cone-beam computed tomography (CBCT) simulation study of 4D XCAT phantom at different dose levels and compared with 3D total variation-based reconstruction (3D TV), 4D reconstruction with image domain motion estimation (IM4D), and SMEIR. G-SMEIR shows strong denoising capability and achieves similar performance at regular dose and half dose. The root mean squared error (RMSE) of G-SMEIR is the best among the four methods and improved about 12% over SMEIR for all respiratory phase images at full dose. G-SMEIR also achieved the best structural similarity index (SSIM) values among all methods. More importantly, G-SMEIR leads to more than 40% improvement of the mean deviation from the phantom tumor motion over SMEIR. A preliminary patient CBCT image reconstruction also shows better image quality of G-SMEIR than that of the frame-by-frame reconstruction (3D TV) and MF-PMM either using image domain motion estimation (IM4D) or using projection domain motion estimation (SMEIR) alone. G-SMEIR with a flexible combination of image domain and projection domain motion estimation provides an effective tool for 4D tomographic reconstruction.

Simulation Study of a Frame-Based Motion Correction Algorithm for Positron Emission Imaging

Positron emission tomography (PET) is a functional non-invasive imaging modality that uses radioactive substances (radiotracers) to measure changes in metabolic processes. Advances in scanner technology and data acquisition in the last decade have led to the development of more sophisticated PET devices with good spatial resolution (1–3 mm of full width at half maximum (FWHM)). However, there are involuntary motions produced by the patient inside the scanner that lead to image degradation and potentially to a misdiagnosis. The adverse effect of the motion in the reconstructed image increases as the spatial resolution of the current scanners continues improving. In order to correct this effect, motion correction techniques are becoming increasingly popular and further studied. This work presents a simulation study of an image motion correction using a frame-based algorithm. The method is able to cut the acquired data from the scanner in frames, taking into account the size of the object of study. This approach allows working with low statistical information without losing image quality. The frames are later registered using spatio-temporal registration developed in a multi-level way. To validate these results, several performance tests are applied to a set of simulated moving phantoms. The results obtained show that the method minimizes the intra-frame motion, improves the signal intensity over the background in comparison with other literature methods, produces excellent values of similarity with the ground-truth (static) image and is able to find a limit in the patient-injected dose when some prior knowledge of the lesion is present.

PET/MRI of atherosclerosis

Myocardial infarction and stroke are the most prevalent global causes of death. Each year 15 million people worldwide die due to myocardial infarction or stroke. Rupture of a vulnerable atherosclerotic plaque is the main underlying cause of stroke and myocardial infarction. Key features of a vulnerable plaque are inflammation, a large lipid-rich necrotic core (LRNC) with a thin or ruptured overlying fibrous cap, and intraplaque hemorrhage (IPH). Noninvasive imaging of these features could have a role in risk stratification of myocardial infarction and stroke and can potentially be utilized for treatment guidance and monitoring. The recent development of hybrid PET/MRI combining the superior soft tissue contrast of MRI with the opportunity to visualize specific plaque features using various radioactive tracers, paves the way for comprehensive plaque imaging. In this review, the use of hybrid PET/MRI for atherosclerotic plaque imaging in carotid and coronary arteries is discussed. The pros and cons of different hybrid PET/MRI systems are reviewed. The challenges in the development of PET/MRI and potential solutions are described. An overview of PET and MRI acquisition techniques for imaging of atherosclerosis including motion correction is provided, followed by a summary of vessel wall imaging PET/MRI studies in patients with carotid and coronary artery disease. Finally, the future of imaging of atherosclerosis with PET/MRI is discussed.

Motion correction of respiratory-gated PET images using deep learning based image registration framework

Artifacts caused by patient breathing and movement during PET data acquisition affect image quality. Respiratory gating is commonly used to gate the list-mode PET data into multiple bins over a respiratory cycle. Non-rigid registration of respiratory-gated PET images can reduce motion artifacts and preserve count statistics, but it is time consuming. In this work, we propose an unsupervised non-rigid image registration framework using deep learning for motion correction. Our network uses a differentiable spatial transformer layer to warp the moving image to the fixed image and uses a stacked structure for deformation field refinement. Estimated deformation fields were incorporated into an iterative image reconstruction algorithm to perform motion compensated PET image reconstruction. We validated the proposed method using simulation and clinical data and implemented an iterative image registration approach for comparison. Motion compensated reconstructions were compared with ungated images. Our simulation study showed that the motion compensated methods can generate images with sharp boundaries and reveal more details in the heart region compared with the ungated image. The resulting normalized root mean square error (NRMS) was 24.3 ± 1.7% for the deep learning based motion correction, 31.1 ± 1.4% for the iterative registration based motion correction, and 41.9 ± 2.0% for ungated reconstruction. The proposed deep learning based motion correction reduced the bias compared with the ungated image without increasing the noise level and outperformed the iterative registration based method. In the real data study, both motion compensated images provided higher lesion contrast and sharper liver boundaries than the ungated image and had lower noise than the reference gate image. The contrast of the proposed method based on the deep neural network was higher than the ungated image and iterative registration method at any matched noise level.

Motion tracking of low-activity fiducial markers using adaptive region of interest with list-mode positron emission tomography

PURPOSE

Motion compensated positron emission tomography (PET) imaging requires detecting and monitoring of patient body motion. We developed a semiautomatic list-mode method to track the three-dimensional (3D) motion of fiducial positron-emitting markers during PET imaging.

METHODS

A previously developed motion tracking method using positron-emitting markers (PeTrack) was enhanced to work with PET imaging. A novel combination of filtering methods was developed to reject physiological tracer background, which would drown out the events from the marker if unfiltered. The most critical filter rejects events whose line-of-response (LOR) is outside an adaptive region of interest (ADROI). The size of ROI was optimized by exploiting the distinct differences between the distributions of events from background and marker. The ADROI PeTrack method was evaluated with Monte Carlo and phantom studies. A 92.5-kBq 22 Na marker moving sinusoidally in 3D was simulated with Monte Carlo methods. The simulated events were combined with list-mode data from cardiac PET imaging patients to evaluate the performance of the tracking. In phantom studies, three 22 Na markers were placed on a dynamic torso phantom with an initial activity of 680 MBq of 82 Rb in its cardiac insert. The motion of the markers was tracked while the phantom simulated various types of patient motion. Motion correction on an event-by-event basis of the list-mode data was then applied and images were reconstructed.

RESULTS

Simulation results show that the background rejection methods can significantly suppress the tracer background and increase the fraction of marker events by a factor of up to 2500. A 92.5-kBq marker can be tracked in 3D at a frequency of 2.0 Hz with an accuracy of 0.8 mm and a precision of 0.3 mm. The phantom study experimentally confirms that the algorithm can track various types of motion. The relative accuracy of the experimental tracking is 0.26 ± 0.14 mm. Motion-corrected images from the phantom study show reduced blurring.

CONCLUSIONS

An algorithm and background rejection methods were developed that can track the 3D motion of low-activity positron-emitting markers during PET imaging. The motion information may be used for motion-compensated PET imaging.

Comparison of two elastic motion correction approaches for whole-body PET/CT: motion deblurring vs gate-to-gate motion correction

Background

Respiratory motion in PET/CT leads to well-known image degrading effects commonly compensated using elastic motion correction approaches. Gate-to-gate motion correction techniques are promising tools for improving clinical PET data but suffer from relatively long reconstruction times. In this study, the performance of a fast elastic motion compensation approach based on motion deblurring (DEB-MC) was evaluated on patient and phantom data and compared to an EM-based fully 3D gate-to-gate motion correction method (G2G-MC) which was considered the gold standard.

Methods

Twenty-eight patients were included in this study with suspected or confirmed malignancies in the thorax or abdomen. All patients underwent whole-body [¹⁸F]FDG PET/CT examinations applying hardware-based respiratory gating. In addition, a dynamic anthropomorphic thorax phantom was studied with PET/CT simulating tumour motion under controlled but realistic conditions. PET signal recovery values were calculated from phantom scans by comparing lesion activities after motion correction to static ground truth data. Differences in standardized uptake values (SUV) and metabolic volume (MV) between both reconstruction methods as well as between motion-corrected (MC) and non motion-corrected (NOMC) results were statistically analyzed using a Wilcoxon signed-rank test.

Results

Phantom data analysis showed high lesion recovery values of 91% (2 cm motion) and 98% (1 cm) for G2G-MC and 83% (2 cm) and 90% (1 cm) for DEB-MC. The statistical analysis of patient data found significant differences between NOMC and MC reconstructions for SUV _max, SUV _mean, MV, and contrast-to-noise ratio (CNR) for both reconstruction algorithms. Furthermore, both methods showed similar increases of 11–12% in SUV _max and SUV _mean after MC. The statistical analysis of the MC/NOMC ratio found no significant differences between the methods.

Conclusion

Both motion correction techniques deliver comparable improvements of SUV _max, SUV _mean, and CNR after MC on clinical and phantom data. The fast elastic motion compensation technique DEB-MC may thereby be a valuable alternative to state-of-the art motion correction techniques.

Higher-order singular value decomposition-based lung parcellation for breathing motion management

Positron emission tomography (PET) imaging of the lungs is confounded by respiratory motion-induced blurring artifacts that degrade quantitative accuracy. Gating and motion-compensated image reconstruction are frequently used to correct these motion artifacts in PET. In the absence of voxel-by-voxel deformation measures, surrogate signals from external markers are used to track internal motion and generate gated PET images. The objective of our work is to develop a group-level parcellation framework for the lungs to guide the placement of markers depending on the location of the internal target region. We present a data-driven framework based on higher-order singular value decomposition (HOSVD) of deformation tensors that enables identification of synchronous areas inside the torso and on the skin surface. Four-dimensional (4-D) magnetic resonance (MR) imaging based on a specialized radial pulse sequence with a one-dimensional slice-projection navigator was used for motion capture under free-breathing conditions. The deformation tensors were computed by nonrigidly registering the gated MR images. Group-level motion signatures obtained via HOSVD were used to cluster the voxels both inside the volume and on the surface. To characterize the parcellation result, we computed correlation measures across the different regions of interest (ROIs). To assess the robustness of the parcellation technique, leave-one-out cross-validation was performed over the subject cohort, and the dependence of the result on varying numbers of gates and singular value thresholds was examined. Overall, the parcellation results were largely consistent across these test cases with Jaccard indices reflecting high degrees of overlap. Finally, a PET simulation study was performed which showed that, depending on the location of the lesion, the selection of a synchronous ROI may lead to noticeable gains in the recovery coefficient. Accurate quantitative interpretation of PET images is important for lung cancer management. Therefore, a guided motion monitoring approach is of utmost importance in the context of pulmonary PET imaging.

Correction of respiratory and cardiac motion in cardiac PET/MR using MR-based motion modeling

Cardiac positron emission tomography (PET) imaging suffers from image blurring due to the constant motion of the heart that can impact interpretation. Hybrid PET/magnetic resonance (MR) has the potential to use radiation-free MR imaging to correct for the effects of cardio-respiratory motion in the PET data, improving qualitative and quantitative PET imaging in the heart. The purpose of this study was (i) to implement a MR image-based motion-corrected PET/MR method and (ii) to perform a proof-of-concept study of quantitative myocardial PET data in patients. The proposed method takes reconstructions of respiratory and cardiac gated PET data and applies spatial transformations to a single reference frame before averaging to form a single motion-corrected PET (MC-PET) image. Motion vector fields (MVFs) describing the transformations were derived from affine or non-rigid registration of respiratory and cardiac gated MR data. Eight patients with suspected cardiac sarcoidosis underwent cardiac PET/MR imaging after injection of 5 MBq kg^-1 of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG). Myocardial regions affected by motion were identified by expert readers within which target-to-background ratios (TBR) and contrast-to-noise ratios (CNR) were measured on non-MC-non-gated, MC-PET, and double respiratory and cardiac gated PET images. Paired t-tests were used to determine statistical differences in quantitative uptake-measures between the different types of PET images. MC-PET images showed less blurring compared to non-MC-non-gated PET and tracer activity qualitatively aligned better with the underlying myocardial anatomy when fused with MR. TBR and CNR were significantly greater for MC-PET (2.8  ±  0.9; 21  ±  22) compared to non-MC-non-gated PET (2.4  ±  0.9, p  =  0.0001; 15  ±  13, p  =  0.02), while TBR was lower and CNR greater compared to double-gated PET (3.2  ±  0.9, p  =  0.04; 6  ±  3, p  =  0.004). This study demonstrated in a patient cohort that motion-corrected (MC) cardiac PET/MR is feasible using a retrospective MR image-based method and that improvement in TBR and CNR are achievable. MC PET/MR holds promise for improving interpretation and quantification in cardiac PET imaging.

Improving the Spatial Alignment in PET/CT Using Amplitude-Based Respiration-Gated PET and Patient-Specific Breathing-Instructed CT

Appropriate attenuation correction is important for accurate quantification of SUVs in PET. Patient respiratory motion can introduce a spatial mismatch between respiration-gated PET and CT, reducing quantitative accuracy. In this study, the effect of a patient-specific breathing-instructed CT protocol on the spatial alignment between CT and amplitude-based optimal respiration-gated PET images was investigated. Methods: ¹⁸F-FDG PET/CT imaging was performed on 20 patients. In addition to the standard low-dose free-breathing CT, breath-hold CT was performed. The amplitude limits of the respiration-gated PET were used to instruct patients to hold their breath during CT acquisition at a similar amplitude level. Spatial mismatch was quantified using the position differences between the lung-liver transition in PET and CT images, the distance between PET and CT lesions' centroids, and the amount of overlap as indicated by the Jaccard similarity coefficient. Furthermore, the effect on attenuation correction was quantified by measuring SUVs, metabolic tumor volume, and total lesion glycolysis (TLG) of lung lesions. Results: All patients found the breathing instructions feasible; however, 4 patients had trouble complying with the instructions. In total, 18 patients were included. The average distance between the lung-liver transition between PET and CT was significantly reduced for breath-hold CT (1.7 ± 2.1 mm), compared with standard CT (5.6 ± 7.3 mm) (P = 0.049). Furthermore, the mean distance between the lesions' centroids on PET and CT was significantly smaller for breath-hold CT (3.6 ± 2.0 mm) than for standard CT (5.5 ± 6.5 mm) (P = 0.040). Quantification of lung lesion SUV was significantly affected, with a higher SUV_mean when breath-hold CT (6.3 ± 3.9 g/cm³) was used for image reconstruction than for standard CT (6.1 ± 3.8 g/cm³) (P = 0.044). Though metabolic tumor volume was not significantly different, TLG reached statistical significance. Conclusion: Optimal respiration-gated PET in combination with patient-specific breathing-instructed CT results in an improved alignment between PET and CT images and shows an increased SUV_mean and TLG. Even though the effects are small, a more accurate SUV and TLG determination is of importance for a more stable PET quantification, which is relevant for radiotherapy planning and therapy response monitoring.

Improved Detection of Small Pulmonary Nodules Through Simultaneous MR/PET Imaging

Magnetic resonance (MR)/PET scanners provide an imaging platform that enables simultaneous acquisition of MR and PET data in perfect spatial and temporal registration. This feature allows improving image quality for the MR and PET images obtained during the course of an examination. In this work the authors demonstrate the use of prospective MR-based motion tracking information for removing motion blur in MR/PET images of small pulmonary nodules. The theoretical basis for the algorithms is presented alongside clinical examples of its use.

Magnetic Resonance-based Motion Correction for Quantitative PET in Simultaneous PET-MR Imaging

Motion degrades image quality and quantitation of PET images, and is an obstacle to quantitative PET imaging. Simultaneous PET-MR offers a tool that can be used for correcting the motion in PET images by using anatomic information from MR imaging acquired concurrently. Motion correction can be performed by transforming a set of reconstructed PET images into the same frame or by incorporating the transformation into the system model and reconstructing the motion-corrected image. Several phantom and patient studies have validated that MR-based motion correction strategies have great promise for quantitative PET imaging in simultaneous PET-MR.

Usefulness of abdominal belt for restricting respiratory cardiac motion and improving image quality in myocardial perfusion PET

BACKGROUND

The current study evaluated the usefulness of a belt technique for restricting respiratory motion of the heart and for improving image quality of 13N-ammonia myocardial PET/CT, and it assessed the tolerability of the belt technique in the clinical setting.

METHODS

Myocardial 13N-ammonia PET/CT scanning was performed in 8 volunteers on Discovery PET/CT 690 with an optical respiratory motion tracking system. Emission scans were performed with and without an abdominal belt. The amplitude of left ventricular (LV) respiratory motion was measured on respiratory-gated PET images. The degree of erroneous decreases in regional myocardial uptake was visually assessed on ungated PET images using a 5-point scale (0 = normal, 1/2/3 = mild/moderate/severe decrease, 4 = defect). The tolerability of the belt technique was evaluated in 53 patients.

RESULTS

All subjects tolerated the belt procedure. The amplitude of the LV respiratory motion decreased significantly with the belt (8.1 ± 7.1 vs 12.1 ± 6.1 mm, P = .0078). The belt significantly improved the image quality scores in the anterior (0.29 ± 0.81 vs 0.71 ± 1.04, P = .015) and inferior (0.33 ± 0.92 vs 1.04 ± 1.04, P < .0001) wall. No adverse events related to the belt technique were observed.

CONCLUSIONS

The belt technique restricts LV respiratory motion and improves the image quality of myocardial PET/CT, and it is well tolerated by patients.

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

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 SUV_mean, SUV_max, and SUV ratio improvements over no motion correction (SUV_mean: 19.9% vs. 14.0% vs. 13.2%; SUV_max: 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.

Non-Rigid Event-by-Event Continuous Respiratory Motion Compensated List-Mode Reconstruction for PET

Respiratory motion during positron emission tomography (PET)/computed tomography (CT) imaging can cause significant image blurring and underestimation of tracer concentration for both static and dynamic studies. In this paper, with the aim to eliminate both intra-cycle and inter-cycle motions, and apply to dynamic imaging, we developed a non-rigid event-by-event (NR-EBE) respiratory motion-compensated list-mode reconstruction algorithm. The proposed method consists of two components: the first component estimates a continuous non-rigid motion field of the internal organs using the internal-external motion correlation. This continuous motion field is then incorporated into the second component, non-rigid MOLAR (NR-MOLAR) reconstruction algorithm to deform the system matrix to the reference location where the attenuation CT is acquired. The point spread function (PSF) and time-of-flight (TOF) kernels in NR-MOLAR are incorporated in the system matrix calculation, and therefore are also deformed according to motion. We first validated NR-MOLAR using a XCAT phantom with a simulated respiratory motion. NR-EBE motion-compensated image reconstruction using both the components was then validated on three human studies injected with ¹⁸F-FPDTBZ and one with ¹⁸F-fluorodeoxyglucose (FDG) tracers. The human results were compared with conventional non-rigid motion correction using discrete motion field (NR-discrete, one motion field per gate) and a previously proposed rigid EBE motion-compensated image reconstruction (R-EBE) that was designed to correct for rigid motion on a target lesion/organ. The XCAT results demonstrated that NR-MOLAR incorporating both PSF and TOF kernels effectively corrected for non-rigid motion. The ¹⁸F-FPDTBZ studies showed that NR-EBE out-performed NR-Discrete, and yielded comparable results with R-EBE on target organs while yielding superior image quality in other regions. The FDG study showed that NR-EBE clearly improved the visibility of multiple moving lesions in the liver where some of them could not be discerned in other reconstructions, in addition to improving quantification. These results show that NR-EBE motion-compensated image reconstruction appears to be a promising tool for lesion detection and quantification when imaging thoracic and abdominal regions using PET.

Concurrent Respiratory Motion Correction of Abdominal PET and Dynamic Contrast-Enhanced-MRI Using a Compressed Sensing Approach

We present an approach for concurrent reconstruction of respiratory motion-compensated abdominal dynamic contrast-enhanced (DCE)-MRI and PET data in an integrated PET/MR scanner. The MR and PET reconstructions share the same motion vector fields derived from radial MR data; the approach is robust to changes in respiratory pattern and does not increase the total acquisition time. Methods: PET and DCE-MRI data of 12 oncologic patients were simultaneously acquired for 6 min on an integrated PET/MR system after administration of ¹⁸F-FDG and gadoterate meglumine. Golden-angle radial MR data were continuously acquired simultaneously with PET data and sorted into multiple motion phases on the basis of a respiratory signal derived directly from the radial MR data. The resulting multidimensional dataset was reconstructed using a compressed sensing approach that exploits sparsity among respiratory phases. Motion vector fields obtained using the full 6-min (MC_6-min) and only the last 1 min (MC_1-min) of data were incorporated into the PET reconstruction to obtain motion-corrected PET images and in an MR iterative reconstruction algorithm to produce a series of motion-corrected DCE-MR images (moco_GRASP). The motion-correction methods (MC_6-min and MC_1-min) were evaluated by qualitative analysis of the MR images and quantitative analysis of SUV_max and SUV_mean, contrast, signal-to-noise ratio (SNR), and lesion volume in the PET images. Results: Motion-corrected MC_6-min PET images demonstrated 30%, 23%, 34%, and 18% increases in average SUV_max, SUV_mean, contrast, and SNR and an average 40% reduction in lesion volume with respect to the non-motion-corrected PET images. The changes in these figures of merit were smaller but still substantial for the MC_1-min protocol: 19%, 10%, 15%, and 9% increases in average SUV_max, SUV_mean, contrast, and SNR; and a 28% reduction in lesion volume. Moco_GRASP images were deemed of acceptable or better diagnostic image quality with respect to conventional breath-hold Cartesian volumetric interpolated breath-hold examination acquisitions. Conclusion: We presented a method that allows the simultaneous acquisition of respiratory motion-corrected diagnostic quality DCE-MRI and quantitatively accurate PET data in an integrated PET/MR scanner with negligible prolongation in acquisition time compared with routine PET/DCE-MRI protocols.

Principles of Simultaneous PET/MR Imaging

Combined PET/MR imaging scanners capable of acquiring simultaneously the complementary information provided by the 2 imaging modalities are now available for human use. After addressing the hardware challenges for integrating the 2 imaging modalities, most of the efforts in the field have focused on developing MR-based attenuation correction methods for neurologic and whole-body applications, implementing approaches for improving one modality by using the data provided by the other and exploring research and clinical applications that could benefit from the synergistic use of the multimodal data.

Enhancing Cardiac PET by Motion Correction Techniques

PURPOSE OF REVIEW

Cardiac positron emission tomography (PET) images often contain errors due to cardiac, respiratory, and patient motion during relatively long image acquisition. Advanced motion compensation techniques may improve PET spatial resolution, eliminate potential artifacts, and ultimately improve the research and clinical capabilities of PET.

RECENT FINDINGS

Combined cardiac and respiratory gating has only recently been implemented in clinical PET systems. Considering that the gated image bins contain much lower counts than the original PET data, they need to be summed after correcting for motion, forming motion-corrected, high-count image volume. Furthermore, automated image registration techniques can be used to correct for motion between CT attenuation scan and PET acquisition. While motion correction methods are not yet widely used in clinical practice, approaches including dual-gated non-rigid motion correction and the incorporation of motion correction information into the reconstruction process have the potential to markedly improve cardiac PET imaging.

A framework based on hidden Markov trees for multimodal PET/CT image co-segmentation

PURPOSE

The purpose of this study was to investigate the use of a probabilistic quad-tree graph (hidden Markov tree, HMT) to provide fast computation, robustness and an interpretational framework for multimodality image processing and to evaluate this framework for single gross tumor target (GTV) delineation from both positron emission tomography (PET) and computed tomography (CT) images.

METHODS

We exploited joint statistical dependencies between hidden states to handle the data stack using multi-observation, multi-resolution of HMT and Bayesian inference. This framework was applied to segmentation of lung tumors in PET/CT datasets taking into consideration simultaneously the CT and the PET image information. PET and CT images were considered using either the original voxels intensities, or after wavelet/contourlet enhancement. The Dice similarity coefficient (DSC), sensitivity (SE), positive predictive value (PPV) were used to assess the performance of the proposed approach on one simulated and 15 clinical PET/CT datasets of non-small cell lung cancer (NSCLC) cases. The surrogate of truth was a statistical consensus (obtained with the Simultaneous Truth and Performance Level Estimation algorithm) of three manual delineations performed by experts on fused PET/CT images. The proposed framework was applied to PET-only, CT-only and PET/CT datasets, and were compared to standard and improved fuzzy c-means (FCM) multimodal implementations.

RESULTS

A high agreement with the consensus of manual delineations was observed when using both PET and CT images. Contourlet-based HMT led to the best results with a DSC of 0.92 ± 0.11 compared to 0.89 ± 0.13 and 0.90 ± 0.12 for Intensity-based HMT and Wavelet-based HMT, respectively. Considering PET or CT only in the HMT led to much lower accuracy. Standard and improved FCM led to comparatively lower accuracy than HMT, even when considering multimodal implementations.

CONCLUSIONS

We evaluated the accuracy of the proposed HMT-based framework for PET/CT image segmentation. The proposed method reached good accuracy, especially with pre-processing in the contourlet domain.

MR-based respiratory and cardiac motion correction for PET imaging

PURPOSE

To develop a motion correction for Positron-Emission-Tomography (PET) using simultaneously acquired magnetic-resonance (MR) images within 90 s.

METHODS

A 90 s MR acquisition allows the generation of a cardiac and respiratory motion model of the body trunk. Thereafter, further diagnostic MR sequences can be recorded during the PET examination without any limitation. To provide full PET scan time coverage, a sensor fusion approach maps external motion signals (respiratory belt, ECG-derived respiration signal) to a complete surrogate signal on which the retrospective data binning is performed. A joint Compressed Sensing reconstruction and motion estimation of the subsampled data provides motion-resolved MR images (respiratory + cardiac). A 1-POINT DIXON method is applied to these MR images to derive a motion-resolved attenuation map. The motion model and the attenuation map are fed to the Customizable and Advanced Software for Tomographic Reconstruction (CASToR) PET reconstruction system in which the motion correction is incorporated. All reconstruction steps are performed online on the scanner via Gadgetron to provide a clinically feasible setup for improved general applicability. The method was evaluated on 36 patients with suspected liver or lung metastasis in terms of lesion quantification (SUVmax, SNR, contrast), delineation (FWHM, slope steepness) and diagnostic confidence level (3-point Likert-scale).

RESULTS

A motion correction could be conducted for all patients, however, only in 30 patients moving lesions could be observed. For the examined 134 malignant lesions, an average improvement in lesion quantification of 22%, delineation of 64% and diagnostic confidence level of 23% was achieved.

CONCLUSION

The proposed method provides a clinically feasible setup for respiratory and cardiac motion correction of PET data by simultaneous short-term MRI. The acquisition sequence and all reconstruction steps are publicly available to foster multi-center studies and various motion correction scenarios.

Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132

Image registration and fusion algorithms exist in almost every software system that creates or uses images in radiotherapy. Most treatment planning systems support some form of image registration and fusion to allow the use of multimodality and time-series image data and even anatomical atlases to assist in target volume and normal tissue delineation. Treatment delivery systems perform registration and fusion between the planning images and the in-room images acquired during the treatment to assist patient positioning. Advanced applications are beginning to support daily dose assessment and enable adaptive radiotherapy using image registration and fusion to propagate contours and accumulate dose between image data taken over the course of therapy to provide up-to-date estimates of anatomical changes and delivered dose. This information aids in the detection of anatomical and functional changes that might elicit changes in the treatment plan or prescription. As the output of the image registration process is always used as the input of another process for planning or delivery, it is important to understand and communicate the uncertainty associated with the software in general and the result of a specific registration. Unfortunately, there is no standard mathematical formalism to perform this for real-world situations where noise, distortion, and complex anatomical variations can occur. Validation of the software systems performance is also complicated by the lack of documentation available from commercial systems leading to use of these systems in undesirable 'black-box' fashion. In view of this situation and the central role that image registration and fusion play in treatment planning and delivery, the Therapy Physics Committee of the American Association of Physicists in Medicine commissioned Task Group 132 to review current approaches and solutions for image registration (both rigid and deformable) in radiotherapy and to provide recommendations for quality assurance and quality control of these clinical processes.

Rigid motion correction of dual opposed planar projections in single photon imaging

Awake and/or freely moving small animal single photon emission imaging allows the continuous study of molecules exhibiting slow kinetics without the need to restrain or anaesthetise the animals. Estimating motion free projections in freely moving small animal planar imaging can be considered as a limited angle tomography problem, except that we wish to estimate the 2D planar projections rather than the 3D volume, where the angular sampling in all three axes depends on the rotational motion of the animal. In this study, we hypothesise that the motion corrected planar projections estimated by reconstructing an estimate of the 3D volume using an iterative motion compensating reconstruction algorithm and integrating it along the projection path, will closely match the true, motion-less, planar distribution regardless of the object motion. We tested this hypothesis for the case of rigid motion using Monte-Carlo simulations and experimental phantom data based on a dual opposed detector system, where object motion was modelled with 6 degrees of freedom. In addition, we investigated the quantitative accuracy of the regional activity extracted from the geometric mean of opposing motion corrected planar projections. Results showed that it is feasible to estimate qualitatively accurate motion-corrected projections for a wide range of motions around all 3 axes. Errors in the geometric mean estimates of regional activity were relatively small and within 10% of expected true values. In addition, quantitative regional errors were dependent on the observed motion, as well as on the surrounding activity of overlapping organs. We conclude that both qualitatively and quantitatively accurate motion-free projections of the tracer distribution in a rigidly moving object can be estimated from dual opposed detectors using a correction approach within an iterative reconstruction framework and we expect this approach can be extended to the case of non-rigid motion.

Technical and instrumentational foundations of PET/MRI

This paper highlights the origins of combined positron emission tomography (PET) and magnetic resonance imaging (MRI) whole-body systems that were first introduced for applications in humans in 2010. This text first covers basic aspects of each imaging modality before describing the technical and methodological challenges of combining PET and MRI within a single system. After several years of development, combined and even fully-integrated PET/MRI systems have become available and made their way into the clinic. This multi-modality imaging system lends itself to the advanced exploration of diseases to support personalized medicine in a long run. To that extent, this paper provides an introduction to PET/MRI methodology and important technical solutions.

Enhancing ejection fraction measurement through 4D respiratory motion compensation in cardiac PET imaging

ECG gated cardiac PET imaging measures functional parameters such as left ventricle (LV) ejection fraction (EF), providing diagnostic and prognostic information for management of patients with coronary artery disease (CAD). Respiratory motion degrades spatial resolution and affects the accuracy in measuring the LV volumes for EF calculation. The goal of this study is to systematically investigate the effect of respiratory motion correction on the estimation of end-diastolic volume (EDV), end-systolic volume (ESV), and EF, especially on the separation of normal and abnormal EFs. We developed a respiratory motion incorporated 4D PET image reconstruction technique which uses all gated-frame data to acquire a motion-suppressed image. Using the standard XCAT phantom and two individual-specific volunteer XCAT phantoms, we simulated dual-gated myocardial perfusion imaging data for normally and abnormally beating hearts. With and without respiratory motion correction, we measured the EDV, ESV, and EF from the cardiac-gated reconstructed images. For all the phantoms, the estimated volumes increased and the biases significantly reduced with motion correction compared with those without. Furthermore, the improvement of ESV measurement in the abnormally beating heart led to better separation of normal and abnormal EFs. The simulation study demonstrated the significant effect of respiratory motion correction on cardiac imaging data with motion amplitude as small as 0.7 cm. The larger the motion amplitude the more improvement respiratory motion correction brought about on the EF measurement. Using data-driven respiratory gating, we also demonstrated the effect of respiratory motion correction on estimating the above functional parameters from list mode patient data. Respiratory motion correction has been shown to improve the accuracy of EF measurement in clinical cardiac PET imaging.

Improved Detection of Small Pulmonary Nodules Through Simultaneous MR/PET Imaging

Magnetic resonance (MR)/PET scanners provide an imaging platform that enables simultaneous acquisition of MR and PET data in perfect spatial and temporal registration. This feature allows improving image quality for the MR and PET images obtained during the course of an examination. In this work the authors demonstrate the use of prospective MR-based motion tracking information for removing motion blur in MR/PET images of small pulmonary nodules. The theoretical basis for the algorithms is presented alongside clinical examples of its use.

Cardiac and Respiratory Motion Correction for Simultaneous Cardiac PET/MR

Cardiac PET is a versatile imaging technique providing important diagnostic information about ischemic heart diseases. Respiratory and cardiac motion of the heart can strongly impair image quality and therefore diagnostic accuracy of cardiac PET scans. The aim of this study was to investigate a new cardiac PET/MR approach providing respiratory and cardiac motion-compensated MR and PET images in less than 5 min. Methods: Free-breathing 3-dimensional MR data were acquired and retrospectively binned into multiple respiratory and cardiac motion states. Three-dimensional cardiac and respiratory motion fields were obtained with a nonrigid registration algorithm and used in motion-compensated MR and PET reconstructions to improve image quality. The improvement in image quality and diagnostic accuracy of the technique was assessed in simultaneous ¹⁸F-FDG PET/MR scans of a canine model of myocardial infarct and was demonstrated in a human subject. Results: MR motion fields were successfully used to compensate for in vivo cardiac motion, leading to improvements in full width at half maximum of the canine myocardium of 13% ± 5%, similar to cardiac gating but with a 90% ± 57% higher contrast-to-noise ratio between myocardium and blood. Motion correction led to an improvement in MR image quality in all subjects, with an increase in sharpness of the canine coronary arteries of 85% ± 72%. A functional assessment showed good agreement with standard MR cine scans with a difference in ejection fraction of -2% ± 3%. MR-based respiratory and cardiac motion information was used to improve the PET image quality of a human in vivo scan. Conclusion: The MR technique presented here provides both diagnostic and motion information that can be used to improve MR and PET image quality. Reliable respiratory and cardiac motion correction could make cardiac PET results more reproducible.

Attenuation correction in 4D-PET using a single-phase attenuation map and rigidity-adaptive deformable registration

PURPOSE

Four-dimensional positron emission tomography (4D-PET) imaging is a potential solution to the respiratory motion effect in the thoracic region. Computed tomography (CT)-based attenuation correction (AC) is an essential step toward quantitative imaging for PET. However, due to the temporal difference between 4D-PET and a single attenuation map from CT, typically available in routine clinical scanning, motion artifacts are observed in the attenuation-corrected PET images, leading to errors in tumor shape and uptake. We introduced a practical method to align single-phase CT with all other 4D-PET phases for AC.

METHODS

A penalized non-rigid Demons registration between individual 4D-PET frames without AC provides the motion vectors to be used for warping single-phase attenuation map. The non-rigid Demons registration was used to derive deformation vector fields (DVFs) between PET matched with the CT phase and other 4D-PET images. While attenuated PET images provide useful data for organ borders such as those of the lung and the liver, tumors cannot be distinguished from the background due to loss of contrast. To preserve the tumor shape in different phases, an ROI-covering tumor was excluded from nonrigid transformation. Instead the mean DVF of the central region of the tumor was assigned to all voxels in the ROI. This process mimics a rigid transformation of the tumor along with a nonrigid transformation of other organs. A 4D-XCAT phantom with spherical lung tumors, with diameters ranging from 10 to 40 mm, was used to evaluate the algorithm. The performance of the proposed hybrid method for attenuation map estimation was compared to (a) the Demons nonrigid registration only and (b) a single attenuation map based on quantitative parameters in individual PET frames.

RESULTS

Motion-related artifacts were significantly reduced in the attenuation-corrected 4D-PET images. When a single attenuation map was used for all individual PET frames, the normalized root-mean-square error (NRMSE) values in tumor region were 49.3% (STD: 8.3%), 50.5% (STD: 9.3%), 51.8% (STD: 10.8%) and 51.5% (STD: 12.1%) for 10-mm, 20-mm, 30-mm, and 40-mm tumors, respectively. These errors were reduced to 11.9% (STD: 2.9%), 13.6% (STD: 3.9%), 13.8% (STD: 4.8%), and 16.7% (STD: 9.3%) by our proposed method for deforming the attenuation map. The relative errors in total lesion glycolysis (TLG) values were -0.25% (STD: 2.87%) and 3.19% (STD: 2.35%) for 30-mm and 40-mm tumors, respectively, in proposed method. The corresponding values for Demons method were 25.22% (STD: 14.79%) and 18.42% (STD: 7.06%). Our proposed hybrid method outperforms the Demons method especially for larger tumors. For tumors smaller than 20 mm, nonrigid transformation could also provide quantitative results.

CONCLUSION

Although non-AC 4D-PET frames include insignificant anatomical information, they are still useful to estimate the DVFs to align the attenuation map for accurate AC. The proposed hybrid method can recover the AC-related artifacts and provide quantitative AC-PET images.

Motion compensation using origin ensembles in awake small animal positron emission tomography

In emission tomographic imaging, the stochastic origin ensembles algorithm provides unique information regarding the detected counts given the measured data. Precision in both voxel and region-wise parameters may be determined for a single data set based on the posterior distribution of the count density allowing uncertainty estimates to be allocated to quantitative measures. Uncertainty estimates are of particular importance in awake animal neurological and behavioral studies for which head motion, unique for each acquired data set, perturbs the measured data. Motion compensation can be conducted when rigid head pose is measured during the scan. However, errors in pose measurements used for compensation can degrade the data and hence quantitative outcomes. In this investigation motion compensation and detector resolution models were incorporated into the basic origin ensembles algorithm and an efficient approach to computation was developed. The approach was validated against maximum liklihood-expectation maximisation and tested using simulated data. The resultant algorithm was then used to analyse quantitative uncertainty in regional activity estimates arising from changes in pose measurement precision. Finally, the posterior covariance acquired from a single data set was used to describe correlations between regions of interest providing information about pose measurement precision that may be useful in system analysis and design. The investigation demonstrates the use of origin ensembles as a powerful framework for evaluating statistical uncertainty of voxel and regional estimates. While in this investigation rigid motion was considered in the context of awake animal PET, the extension to arbitrary motion may provide clinical utility where respiratory or cardiac motion perturb the measured data.

4-Dimensional MRI and Attenuation Map Generation in PET/MRI with 4-Dimensional PET-Derived Deformation Matrices: Study of Feasibility for Lung Cancer Applications

Respiratory motion may reduce accuracy in the fusion of functional and anatomic images from combined PET/MRI systems. Methodologies for the correction of respiratory motion in PET acquisitions with such systems are mostly based on the use of respiration-synchronized MRI acquisitions to derive motion fields. Existing approaches based on tagging acquisitions may introduce artifacts in MR images, whereas motion model approaches require the acquisition of training datasets. The objective of this work was to investigate the possibility of generating 4-dimensional (4D) MR images and associated attenuation maps (AMs) from the combination of a single static MR image and motion fields obtained from simultaneously acquired 4D non-attenuation-corrected (NAC) PET images. Methods: Four-dimensional PET/MRI datasets were acquired for 11 patients on a simultaneous PET/MRI system. The 4D PET datasets were retrospectively binned into 4 motion amplitude frames corresponding to the simultaneously acquired T1-weighted 4D MR images. A T1-weighted 3-dimensional MRI sequence with Dixon-based fat and water separation was also acquired at the end of expiration for PET attenuation correction purposes. All reconstructed 4D NAC PET images were then elastically registered to the single end-of-expiration NAC PET image. The derived motion fields were subsequently applied to the end-of-expiration frame of the acquired 4D MRI volume and the AM derived from the Dixon MR image to generate respiration-synchronized MR images and corresponding AMs. Results: The accuracy of the proposed method was assessed by comparing the generated and acquired images according to metrics such as overall correlation coefficients and differences in distances of anatomic landmarks on the generated and acquired MRI datasets. High correlation coefficients (mean ± SD: 0.93 ± 0.03) and small differences (2.69 ± 0.5 mm) were obtained. Moreover, small tissue classification differences (2.23% ± 0.68%) between generated and 4D MRI-extracted AMs were observed. Conclusion: Our results confirm the feasibility of using 4D NAC PET images for accurate PET attenuation correction and respiratory motion correction in PET/MRI, without the need for patient-specific 4D MRI acquisitions.

List-mode image reconstruction for positron emission tomography using tetrahedral voxels

Image space decomposition based on tetrahedral voxels are interesting candidates for use in emission tomography. Tetrahedral voxels provide many of the advantages of point clouds with irregular spacing, such as being intrinsically multi-resolution, yet they also serve as a volumetric partition of the image space and so are comparable to more standard cubic voxels. Additionally, non-rigid displacement fields can be applied to the tetrahedral mesh in a straight-forward manner. So far studies incorporating tetrahedral decomposition of the image space have concentrated on pre-calculated, node-based, system matrix elements which reduces the flexibility of the tetrahedral approach and the capacity to accurately define regions of interest. Here, a list-mode on-the-fly calculation of the system matrix elements is described using a tetrahedral decomposition of the image space and volumetric elements-voxels. The algorithm is demonstrated in the context of awake animal PET which may require both rigid and non-rigid motion compensation, as well as quantification within small regions of the brain. This approach allows accurate, event based, motion compensation including non-rigid deformations.

Technical Pitfalls and Limitations of SPECT/CT

The synergy of functional and anatomic information in hybrid systems has undoubtedly enhanced the diagnostic potential of radionuclide imaging in recent years, contributing to the advancement of SPECT/CT in clinical practice. Since the introduction of commercial SPECT/CT in the late 1990 s, the field has seen rapid expansion and development toward multidetector CT subsystems, establishing the role of SPECT/CT as a routine imaging tool. It is, however, important to discuss possible challenges and technical limitations of such systems and how these influence imaging outcomes. In particular, the issues of patient motion and spatial misalignment of the SPECT and CT modalities, data corrections such as those for photon attenuation, and the choice of CT acquisition protocols in relation to radiation exposure are discussed in the article.

Local respiratory motion correction for PET/CT imaging: Application to lung cancer

PURPOSE

Despite multiple methodologies already proposed to correct respiratory motion in the whole PET imaging field of view (FOV), such approaches have not found wide acceptance in clinical routine. An alternative can be the local respiratory motion correction (LRMC) of data corresponding to a given volume of interest (VOI: organ or tumor). Advantages of LRMC include the use of a simple motion model, faster execution times, and organ specific motion correction. The purpose of this study was to evaluate the performance of LMRC using various motion models for oncology (lung lesion) applications.

METHODS

Both simulated (NURBS based 4D cardiac-torso phantom) and clinical studies (six patients) were used in the evaluation of the proposed LRMC approach. PET data were acquired in list-mode and synchronized with respiration. The implemented approach consists first in defining a VOI on the reconstructed motion average image. Gated PET images of the VOI are subsequently reconstructed using only lines of response passing through the selected VOI and are used in combination with a center of gravity or an affine/elastic registration algorithm to derive the transformation maps corresponding to the respiration effects. Those are finally integrated in the reconstruction process to produce a motion free image over the lesion regions.

RESULTS

Although the center of gravity or affine algorithm achieved similar performance for individual lesion motion correction, the elastic model, applied either locally or to the whole FOV, led to an overall superior performance. The spatial tumor location was altered by 89% and 81% for the elastic model applied locally or to the whole FOV, respectively (compared to 44% and 39% for the center of gravity and affine models, respectively). This resulted in similar associated overall tumor volume changes of 84% and 80%, respectively (compared to 75% and 71% for the center of gravity and affine models, respectively). The application of the nonrigid deformation model in LRMC led to over an order of magnitude gain in computational efficiency of the correction relative to the application of the deformable model to the whole FOV.

CONCLUSIONS

The results of this study support the use of LMRC as a flexible and efficient correction approach for respiratory motion effects for single lesions in the thoracic area.

Respiratory motion correction in 4D-PET by simultaneous motion estimation and image reconstruction (SMEIR)

In conventional 4D positron emission tomography (4D-PET), images from different frames are reconstructed individually and aligned by registration methods. Two issues that arise with this approach are as follows: (1) the reconstruction algorithms do not make full use of projection statistics; and (2) the registration between noisy images can result in poor alignment. In this study, we investigated the use of simultaneous motion estimation and image reconstruction (SMEIR) methods for motion estimation/correction in 4D-PET. A modified ordered-subset expectation maximization algorithm coupled with total variation minimization (OSEM-TV) was used to obtain a primary motion-compensated PET (pmc-PET) from all projection data, using Demons derived deformation vector fields (DVFs) as initial motion vectors. A motion model update was performed to obtain an optimal set of DVFs in the pmc-PET and other phases, by matching the forward projection of the deformed pmc-PET with measured projections from other phases. The OSEM-TV image reconstruction was repeated using updated DVFs, and new DVFs were estimated based on updated images. A 4D-XCAT phantom with typical FDG biodistribution was generated to evaluate the performance of the SMEIR algorithm in lung and liver tumors with different contrasts and different diameters (10-40 mm). The image quality of the 4D-PET was greatly improved by the SMEIR algorithm. When all projections were used to reconstruct 3D-PET without motion compensation, motion blurring artifacts were present, leading up to 150% tumor size overestimation and significant quantitative errors, including 50% underestimation of tumor contrast and 59% underestimation of tumor uptake. Errors were reduced to less than 10% in most images by using the SMEIR algorithm, showing its potential in motion estimation/correction in 4D-PET.

Evaluation of motion-correction methods for dual-gated cardiac positron emission tomography/computed tomography imaging

BACKGROUND

Dual gating is a method of dividing the data of a cardiac PET scan into smaller bins according to the respiratory motion and the ECG of the patient. It reduces the undesirable motion artefacts in images, but produces several images for interpretation and decreases the quality of single images. By using motion-correction techniques, the motion artefacts in the dual-gated images can be corrected and the images can be combined into a single motion-free image with good statistics.

AIM

The aim of the present study is to develop and evaluate motion-correction methods for cardiac PET studies. We have developed and compared two different methods: computed tomography (CT)/PET-based and CT-only methods.

METHODS

The methods were implemented and tested with a cardiac phantom and three patient datasets. In both methods, anatomical information of CT images is used to create models for the cardiac motion.

RESULTS

In the patient study, the CT-only method reduced motion (measured as the centre of mass of the myocardium) on average 43%, increased the contrast-to-noise ratio on average 6.0% and reduced the target size on average 10%. Slightly better figures (51, 6.9 and 28%) were obtained with the CT/PET-based method. Even better results were obtained in the phantom study for both the CT-only method (57, 68 and 43%) and the CT/PET-based method (61, 74 and 52%).

CONCLUSION

We conclude that using anatomical information of CT for motion correction of cardiac PET images, both respiratory and pulsatile motions can be corrected with good accuracy.

Whole-body FDG PET-MR oncologic imaging: pitfalls in clinical interpretation related to inaccurate MR-based attenuation correction

Simultaneous data collection for positron emission tomography and magnetic resonance imaging (PET/MR) is now a reality. While the full benefits of concurrently acquiring PET and MR data and the potential added clinical value are still being evaluated, initial studies have identified several important potential pitfalls in the interpretation of fluorodeoxyglucose (FDG) PET/MRI in oncologic whole-body imaging, the majority of which being related to the errors in the attenuation maps created from the MR data. The purpose of this article was to present such pitfalls and artifacts using case examples, describe their etiology, and discuss strategies to overcome them. Using a case-based approach, we will illustrate artifacts related to (1) Inaccurate bone tissue segmentation; (2) Inaccurate air cavities segmentation; (3) Motion-induced misregistration; (4) RF coils in the PET field of view; (5) B0 field inhomogeneity; (6) B1 field inhomogeneity; (7) Metallic implants; (8) MR contrast agents.

Pulmonary imaging using respiratory motion compensated simultaneous PET/MR

PURPOSE

Pulmonary positron emission tomography (PET) imaging is confounded by blurring artifacts caused by respiratory motion. These artifacts degrade both image quality and quantitative accuracy. In this paper, the authors present a complete data acquisition and processing framework for respiratory motion compensated image reconstruction (MCIR) using simultaneous whole body PET/magnetic resonance (MR) and validate it through simulation and clinical patient studies.

METHODS

The authors have developed an MCIR framework based on maximum a posteriori or MAP estimation. For fast acquisition of high quality 4D MR images, the authors developed a novel Golden-angle RAdial Navigated Gradient Echo (GRANGE) pulse sequence and used it in conjunction with sparsity-enforcing k-t FOCUSS reconstruction. The authors use a 1D slice-projection navigator signal encapsulated within this pulse sequence along with a histogram-based gate assignment technique to retrospectively sort the MR and PET data into individual gates. The authors compute deformation fields for each gate via nonrigid registration. The deformation fields are incorporated into the PET data model as well as utilized for generating dynamic attenuation maps. The framework was validated using simulation studies on the 4D XCAT phantom and three clinical patient studies that were performed on the Biograph mMR, a simultaneous whole body PET/MR scanner.

RESULTS

The authors compared MCIR (MC) results with ungated (UG) and one-gate (OG) reconstruction results. The XCAT study revealed contrast-to-noise ratio (CNR) improvements for MC relative to UG in the range of 21%-107% for 14 mm diameter lung lesions and 39%-120% for 10 mm diameter lung lesions. A strategy for regularization parameter selection was proposed, validated using XCAT simulations, and applied to the clinical studies. The authors' results show that the MC image yields 19%-190% increase in the CNR of high-intensity features of interest affected by respiratory motion relative to UG and a 6%-51% increase relative to OG.

CONCLUSIONS

Standalone MR is not the traditional choice for lung scans due to the low proton density, high magnetic susceptibility, and low T2 (∗) relaxation time in the lungs. By developing and validating this PET/MR pulmonary imaging framework, the authors show that simultaneous PET/MR, unique in its capability of combining structural information from MR with functional information from PET, shows promise in pulmonary imaging.