Using ITK to obtain motion transform in anatomically guided PET motion correction for simultaneous PET/MR

Nonspecific aortoarteritis (NSAA) is a chronic inflammatory vasculitis involving aorta and its branches. We conducted a study prospectively to compare time resolved magnetic resonance angiography (MRA) with diagnostic digital subtraction angiography (DSA) for the vascular assessment in the patients of NSAA. Seventeen patients of NSAA were recruited in the study over the period of 3 years. Contrast enhanced MRA using Time-resolved angiography With Interleaved Stochastic Trajectories (TWIST) sequence and diagnostic DSA were performed in these 17 patients. The majority of the patients were young (median age was 25 years, range 8 to 46 years) and 11 patients were females. Erythrocyte sedimentation rate (ESR) was elevated in 9 patients and C-reactive protein (CRP) was elevated in 6 patients. Most commonly involved vessels in our patients were right renal artery (14 patients), abdominal aorta (12 patients) and left renal artery (11 patients). Left and right subclavian arteres were involved in 10 and 6 patients respectively. The sensitivity and specificity of time-resolved MRA using TWIST sequence is 100% as compared to DSA in the assessment of major vessels such as aorta, arch vessels, celiac artery and superior mesenteric artery. However, the sensitivity and specificity of time resolved MRA in the evaluation of renal arteries and vertebral arteries were 100%, 71.4% and 85.7%, 33.3% respectively. No significant association of MRI contrast enhancement with erythrocyte sedimentation rate (p = 1.00) and C-reactive protein (p = 0.600). Time resolved MRA images obtained using TWIST sequence were as qualitative as DSA images and can noninvasively evaluate the vascular involvement in NSAA patients.

Degradation of image quality caused by cardiac and respiratory motions hampers the diagnostic quality of cardiac PET. It has been shown that improved diagnostic accuracy of myocardial defect can be achieved by tagged MR (tMR) based PET motion correction using simultaneous PET-MR. However, one major hurdle for the adoption of tMR-based PET motion correction in the PET-MR routine is the long acquisition time needed for the collection of fully sampled tMR data. In this work, the authors propose an accelerated tMR acquisition strategy using parallel imaging and/or compressed sensing and assess the impact on the tMR-based motion corrected PET using phantom and patient data. Fully sampled tMR data were acquired simultaneously with PET list-mode data on two simultaneous PET-MR scanners for a cardiac phantom and a patient. Parallel imaging and compressed sensing were retrospectively performed by GRAPPA and kt-FOCUSS algorithms with various acceleration factors. Motion fields were estimated using nonrigid B-spline image registration from both the accelerated and fully sampled tMR images. The motion fields were incorporated into a motion corrected ordered subset expectation maximization reconstruction algorithm with motion-dependent attenuation correction. Although tMR acceleration introduced image artifacts into the tMR images for both phantom and patient data, motion corrected PET images yielded similar image quality as those obtained using the fully sampled tMR images for low to moderate acceleration factors (<4). Quantitative analysis of myocardial defect contrast over ten independent noise realizations showed similar results. It was further observed that although the image quality of the motion corrected PET images deteriorates for high acceleration factors, the images were still superior to the images reconstructed without motion correction. Accelerated tMR images obtained with more than 4 times acceleration can still provide relatively accurate motion fields and yield tMR-based motion corrected PET images with similar image quality as those reconstructed using fully sampled tMR data. The reduction of tMR acquisition time makes it more compatible with routine clinical cardiac PET-MR studies.

PET image of tumor located in thoracic region was affected by various organ motions such as respiration and heartbeat. Thoracic motion is difficult to estimate and correct accurately using external measurement or anatomical image solely. The aim of this study was to compare the accuracy of motion correction using PET fiducial mark and 3D MRI image. The radioactive bead for PET fiducial mark was realized from molecular sieve contained 0.37 MBq F-18 and placed in thoracic region. PET study was performed using a small animal PET scanner after IV injection of FDG. MRI study was performed using 3-T clinical MRI system with 3D T1-VIBE (TR/TE=5.67/1.42 ms) sequence. Motion corrected PET image was created by mutual information registration with B-Spline interpolation to the mean image after first realignment. FWHM of lung and liver region in static PET image was 4.77±0.87 and 4.81±0.45, respectively. Measured FWHM of lung region in motion corrected PET image using PET fiducial mark and 3D VIBE MRI was measured 4.22±0.09 and 4.59±0.06, respectively. In case of liver region, FWHM was measured 4.47±0.16 and 4.65±0.25 respectively. The improvement of resolution was observed by proper correction method. In this study PET correction was implemented by motion information extracted from various images. These results suggest motion correction would be possible without external device or fiducial mark using MRI motion data. Motion correction using MRI should be considered acquisition method and organ region in accordance with motion characteristics.

This study introduces a new technique in the assessment of nasolacrimal drainage with magnetic resonance imaging. To describe a new dynamic magnetic resonance dacryocystography (MRDCG) protocol and report normative findings on asymptomatic lacrimal drainage systems. A prospective observational study at the Royal Adelaide Hospital. Seventeen patients with unilateral epiphora were prospectively recruited. Patients underwent dynamic MRDCG. The time-resolved angiography with interleaved stochastic trajectories (TWIST) sequence was used to visualise contrast flow through the lacrimal drainage system. Tear transit times and anatomical dimensions were measured. Dynamic MRDCG with TWIST sequence reliably demonstrated contrast flow. Tear transit times and anatomical dimensions were comparable to previously reported values in other imaging modalities. Dynamic MRDCG provides good structural and temporal resolution to the assessment of lacrimal outflow obstructions. The physiological nature of this modality allows potential assessment for functional obstruction and future research on tear flow assessment.

PurposeTo develop a framework for efficient and simultaneous acquisition of motion‐compensated whole‐heart coronary MR angiography (CMRA) and left ventricular function by MR and myocardial integrity by PET on a 3T PET‐MR system.MethodsAn acquisition scheme based on a dual‐phase CMRA sequence acquired simultaneously with cardiac PET data has been developed. The framework is integrated with a motion‐corrected image reconstruction approach, so that non‐rigid respiratory and cardiac deformation fields estimated from MR images are used to correct both the CMRA (respiratory motion correction for each cardiac phase) and the PET data (respiratory and cardiac motion correction). The proposed approach was tested in a cohort of 8 healthy subjects and 6 patients with coronary artery disease. Left ventricular (LV) function estimated from motion‐corrected dual‐phase CMRA was compared to the gold standard estimated from a stack of 2D CINE images for the healthy subjects. Relative increase of signal in motion‐corrected PET images compared to uncorrected images was computed for standard 17‐segment polar maps for each patient.ResultsMotion‐corrected dual‐phase CMRA images allow for visualization of the coronary arteries in both systole and diastole for all healthy subjects and cardiac patients. LV functional indices from healthy subjects result in good agreement with the reference method, underestimating stroke volume by 3.07 ± 3.26 mL and ejection fraction by 0.30 ± 1.01%. Motion correction improved delineation of the myocardium in PET images, resulting in an increased 18F‐FDG signal of up to 28% in basal segments of the myocardial wall compared to uncorrected images.ConclusionThe proposed motion‐corrected dual‐phase CMRA and cardiac PET produces co‐registered good quality images in both modalities in a single efficient examination of ~13 min.

Purpose: Cone-beam CT is a common approach for 3D guidance in interventional radiology (IR), but its long image acquisition time poses a limit to image quality due to complex deformable motion of soft-tissue structures. Multi-region autofocus optimization showed successful compensation of deformable motion with gains in image quality. However, the use of sub-optimal conventional autofocus metrics, accompanied by the high-dimensionality and non-convexity of the optimization problem result in challenged convergence. This work presents a learning-based approach to the design of autofocus metrics tailored to the specific problem of motion compensation in soft-tissue CBCT. Methods: A deep convolutional neural network (CNN) was used to automatically extract image features quantifying the local motion amplitude and principal direction in volumetric regions of interest (128 x 128x 128 voxels) of a motion contaminated volume. The estimated motion amplitude is then used as the core component of the cost function of the deformable autofocus method, complemented by a regularization term favoring similar motion for regions close in space. The network consists of a siamese arrangement of three branches acting on the three central orthogonal planes of the volumetric ROI. The network was trained with simulated data generated from publicly available CT datasets, including deformable motion fields from which volumetric ROIs with locally rigid motion were extracted. The performance of motion amplitude estimation and of the final CNN-based deformable autofocus were assessed on synthetic CBCT data generated similarly to the training dataset and featuring deformable motion fields with 1 to 5 components with random direction and random amplitude ranging from 0 mm to 50 mm. Results: Predicted local motion amplitude showed good agreement with the true values, showing a liner relationship (R2 = 0.96, slope 0.95), and slight underestimation of the motion amplitude. Absolute errors in total motion amplitude and individual components remained < 2 mm throughout the explored range. Relative errors were higher for low amplitude motion, pointing to need of a larger training cohort, more focused on low motion amplitude scenarios. Motion compensation with the learning-based metric showed successful compensation of motion artifacts in single ROI environments, with 40% reduction in median RMSE using the motion static image as reference. Deformable motion compensation resulted in better visualization of soft-tissue structures, and overall sharper image details, with slight residual motion artifacts for regions combining moderate motion amplitude with complex anatomical structures. Conclusion: Deformable motion compensation with automatically learned autofocus metrics was proved feasible, opening the way to the design of metrics with potential for more reliable performance and easier optimization than conventional autofocus metrics.

Methods like PET imaging which use radiotracers that bind to amyloid-β (Aβ) - plaques that accumulate and lead to Alzheimer's disease (AD) - are crucial to detect AD early. However, current semi-quantitative methods that quantify Aβ using the Standardized Uptake Values Ratio (SUVr) cannot distinguish between the specific binding of radiotracers to Aβ and non-specific binding to other targets. In this paper, we propose a novel method to predict non-specific binding from MR images using deep learning by developing a novel conditional Generative Adversarial Network (cGAN) to improve Aβ quantification. PET images with low amyloid load were selected to represent non-specific uptake of the Aβ [11C]PiB radiotracer. Paired MR and PET images with low amyloid load were used for training, with the MRI image as the network input and the PET image as the training label. The cGAN was trained to learn the mapping between the MR image and the non-specific PET image. The generated non-specific PET images were compared to the real PET scans, achieving a mean SUVr difference of 1.90% in the gray matter. The small SUVr difference indicated that we have successfully developed cGAN for predicting the non-specific uptake of PET and that deep learning can be used to estimate the non-specific binding of Aβ PET radiotracer from MR images.

Simultaneous PET and MR imaging is a promising new technique allowing the fusion of functional (PET) and anatomic/functional (MR) information. In the thoracic-abdominal regions, respiratory motion is a major challenge leading to reduced quantitative and qualitative image accuracy. Correction methodologies include the use of gated frames that lead to low signal-to-noise ratio considering the associated low statistics. More advanced correction approaches, previously developed for PET/CT imaging, consist of either registering all the reconstructed gated frames to the reference frame or incorporating motion parameters into the iterative reconstruction process to produce a single motion-compensated PET image. The goal of this work was to compare these two—previously implemented in PET/CT—correction approaches within the context of PET/MR motion correction for oncology applications using clinical 4-dimensional PET/MR acquisitions. Two different correction approaches were evaluated comparing the incorporation of elastic transformations extracted from 4-dimensional MR imaging datasets during PET list-mode image reconstruction to a postreconstruction image-based approach. Eleven patient datasets acquired on a PET/MR system were used. T1-weighted 4D MR images were registered to the end-expiration image using a nonrigid B-spline registration algorithm to derive deformation matrices accounting for respiratory motion. The derived matrices were subsequently incorporated within a PET image reconstruction of the original emission list-mode data (reconstruction space [RS] method). The corrected images were compared with those produced by applying the deformation matrices in the image space (IS method) followed by summing the realigned gated frames, as well as with uncorrected motion-averaged images. Both correction techniques led to significant improvement in accounting for respiratory motion artifacts when compared with uncorrected motion-averaged images. These improvements included signal-to-noise ratio (mean increase of 28.0% and 24.2% for the RS and IS methods, respectively), lesion size (reduction of 60.4% and 47.9%, respectively), lesion contrast (increase of 70.1% and 57.2%, respectively), and lesion position (changes of 60.9% and 46.7%, respectively). Our results demonstrate significant respiratory motion compensation using both methods, with superior results from a 4D PET RS approach.

Accurate identification of eloquent cortex is important to ensure that resective surgery in the region surrounding the central sulcus is performed with minimum risk of permanent neurological deficit. Functional localization has traditionally been accomplished using intraoperative cortical stimulation (ICS). However, this technique suffers from several disadvantages that make the development and validation of noninvasive methods desirable. Functional localization accomplished by activation studies in which positron emission tomography (PET) scanning and the tracer [15O]H2O have been used has been shown to correlate well with the results of ICS. Another noninvasive method for functional localization is functional magnetic resonance (fMR) imaging. We compared the locations of activation peaks obtained in individual patients using fMR and [15O]H2O PET imaging. Twenty-six combined PET activation-fMR imaging studies were performed in 11 patients who were admitted for evaluation before undergoing surgery in the region surrounding the central sulcus. The PET scans were obtained using bolus injections of the cerebral blood flow tracer [15O]H2O (10 mCi). Multislice T2*-weighted gradient-echo echoplanar images were acquired using a 1.5-tesla MR imaging system. Activation maps were aligned with anatomical MR images and transformed into stereotactic space, after which the locations of activation peaks obtained using both modalities were compared. The average distance between activation peaks obtained using fMR imaging and those obtained using PET imaging was 7.9+/-4.8 mm (p>0.05), with 96% of the peaks being located on either the same or adjacent sulci and gyri. Overlapping of voxels activated by each modality occurred in 92% of the studies. Functional MR imaging failed to activate the primary sensorimotor cortex in one study and produced results that were ambiguous in the clinical setting in three cases. Overall, fMR imaging produced activation that correlated well with that obtained using PET scanning. Discrepancies between the sites of activation identified using these two techniques may reflect differences in their physiological bases.

To compare (11)C-choline positron emission tomography (C-PET), (18)F-fluorodeoxyglucose PET (FDG-PET), and MR imaging in the preoperative detection of prostate cancer. C-PET, FDG-PET, and MR images were obtained in 43 consecutive patients with suspected prostate cancer, and prostate cancers were histopathologically confirmed in 26 patients. Unenhanced T1-weighted, T2-weighted, and gadolinium-enhanced MR images were obtained. C-PET and FDG-PET were conducted 1.5 and 60 minutes after injection of 5.5 and 5.0 MBq/kg tracers, respectively. A nuclear and a genitourinary radiologist retrospectively reviewed PET and MR images at random, respectively, and assigned a confidence level for the presence of prostate cancer using a four-point scale. Diagnostic performance was tested using the McNemar test and receiver operating characteristic curve analysis. The sensitivity was greater (P < 0.05) with MR (88%) and C-PET (73%) images than with FDG-PET images (31%). The accuracy was greater (P < 0.05) with MR images (88%) than with C-PET (67%) and FDG-PET (53%) images. The area-under-curve value with MR (0.90) was greater than those with C-PET (0.53) and FDG-PET (0.54) images (P < 0.01). MR imaging should be primarily performed in the preoperative detection of prostate cancer. C-PET and FDG-PET did not improve the detection.

Respiratory motion blurs positron emission tomography (PET) images in thorax and abdominal regions, and can cause attenuation-related errors in quantitative parameters. In addition, this motion can cause localization error and disappearance of tumor(s) near the diaphragm. Respiratory gated PET imaging, or 4D PET imaging, is an attractive solution. For 4D PET, a surrogate measurement of the patient's breathing is made during the scan, and based on this measurement the list-mode PET data are sorted and reconstructed into multiple images. In several studies, it has been reported that 4D PET imaging recovers tumor volume more accurately than 3D PET imaging, and yields more accurate standard uptake values. In spite of its promise, 4D PET images suffer from statistical noise because only a fraction of the acquired data can be used in each image. Moreover, they also suffer from inconsistent attenuation correction due to mismatches between PET and CT images. In this paper, we propose a novel framework for phased attenuation correction and respiratory motion compensation for a 3D PET image by using a 3D CT image and multiple respiratory-phase 3D MR images. For phased attenuation correction, we generate and utilize 4D CT image obtained via non-rigid registrations among a 3D CT image and multiple respiratory-phase 3D MR images. A 3D PET image at a selected respiratory phase is then obtained by adding attenuation corrected multiple 3D PET images through non-rigid transformation. Experimental result for a clinical dataset shows that the proposed algorithm can provide much clearer organ boundary with improved quality than the conventional method in a 3D PET image.

Accurate regional brain quantitative PET measurements, particularly when using partial volume correction, rely on robust image registration between PET and MR images. We argue here that the precision, and hence the uncertainty, of MR-PET image registration is mainly driven by the registration implementation and the quality of PET images due to their lower resolution and higher noise compared to the structural MR images. We propose a dedicated uncertainty analysis for quantifying the precision of MR-PET registration, centred around the bootstrap resampling of PET list-mode events to generate multiple PET image realisations with different noise (count) levels. The effects of PET image reconstruction parameters, such as the use of attenuation and scatter corrections and different number of iterations, on the precision and accuracy of MR-PET registration were investigated. In addition, the performance of four software packages with their default settings for rigid inter-modality image registration were considered: NiftyReg, Vinci, FSL and SPM. Four distinct PET image distributions made of two early time frames (similar to cortical FDG) and two late frames using two amyloid PET dynamic acquisitions of one amyloid positive and one amyloid negative participants were investigated.For the investigated four PET frames, the biggest impact on the uncertainty was observed between registration software packages (up to 10-fold difference in precision) followed by the reconstruction parameters. On average, the lowest uncertainty for different PET frames and brain regions was observed with SPM and two iterations of fully quantitative image reconstruction. The observed uncertainty for the varying PET count-level (from 5% to 60%) was slightly lower than for the reconstruction parameters. We also observed that the registration uncertainty in quantitative PET analysis depends on amyloid status of the considered PET frames, with increased uncertainty (up to three times) when using post-reconstruction partial volume correction. This analysis is applicable for PET data obtained from either PET/MR or PET/CT scanners.

Respiratory motion can potentially reduce accuracy in functional and anatomical images fusion from combined PET/MR systems. Methodologies for the correction of respiratory motion in PET acquisitions in combined PET/MR systems are almost exclusively based on the use of respiratory synchronized MRI acquisitions to derive motion fields. The acquisition of 4D MR datasets is however associated with a compromise between image quality and acquisition duration limiting its use in clinical practice. The objective of this work was therefore to generate 4D MR images and associated attenuation maps from a single static MR image and motion fields obtained from simultaneously acquired 4D non-attenuation corrected (NAC) PET images. 4D PET/MRI datasets were acquired for five patients on the SIEMENS mMR PET/MR system. The 4D PET acquired datasets were retrospectively binned into 4 motion amplitude frames corresponding to the simultaneously acquired 4D gradient echo MR images. A single MR Dixon scan was also acquired at end of expiration for PET AC purposes. All the reconstructed 4D NAC PET images were then registered using an elastic registration algorithm to the single `end of expiration' NAC PET image. The derived motion fields were subsequently applied to the `end expiration' 3D gradient echo MR volume and its corresponding attenuation map to generate respiratory synchronized MR images and corresponding attenuation maps. The accuracy of the proposed method was assessed by comparing the generated to the original images. Good diaphragm profile matching, high correlation coefficients (0.91± 0.03) and small errors (<;3mm) were noticed between the generated and acquired 4D MRI series. Moreover, small classification errors between the generated and 4D MR extracted attenuation maps. As a conclusion, these results may allow for accurate PET AC and respiratory motion correction in PET/MR, without the need for patient specific 4D MR acquisitions.

Association of Internal and External Motion Based on Cine MR Images Acquired During Real-Time Treatment on MRI–Guided Linear Accelerator for Patients With Lung Cancer

Nasopharynx: Clinical, pathologic, and radiologic assessment