Evaluating different methods of MR-based motion correction in simultaneous PET/MR using a head phantom moved by a robotic system

BACKGROUND: Due to comparatively long measurement times in simultaneous positron emission tomography and magnetic resonance (PET/MR) imaging, patient movement during the measurement can be challenging. This leads to artifacts which have a negative impact on the visual assessment and quantitative validity of the image data and, in the worst case, can lead to misinterpretations. Simultaneous PET/MR systems allow the MR-based registration of movements and enable correction of the PET data. To assess the effectiveness of motion correction methods, it is necessary to carry out measurements on phantoms that are moved in a reproducible way. This study explores the possibility of using such a phantom-based setup to evaluate motion correction strategies in PET/MR of the human head. METHOD: An MR-compatible robotic system was used to generate rigid movements of a head-like phantom. Different tools, either from the manufacturer or open-source software, were used to estimate and correct for motion based on the PET data itself (SIRF with SPM and NiftyReg) and MR data acquired simultaneously (e.g. MCLFIRT, BrainCompass). Different motion estimates were compared using data acquired during robot-induced motion. The effectiveness of motion correction of PET data was evaluated by determining the segmented volume of an activity-filled flask inside the phantom. In addition, the segmented volume was used to determine the centre-of-mass and the change in maximum activity concentration. RESULTS: The results showed a volume increase between 2.7 and 36.3% could be induced by the experimental setup depending on the motion pattern. Both, BrainCompass and MCFLIRT, produced corrected PET images, by reducing the volume increase to 0.7–4.7% (BrainCompass) and to -2.8–0.4% (MCFLIRT). The same was observed for example for the centre-of-mass, where the results show that MCFLIRT (0.2–0.6 mm after motion correction) had a smaller deviation from the reference position than BrainCompass (0.5–1.8 mm) for all displacements. CONCLUSIONS: The experimental setup is suitable for the reproducible generation of movement patterns. Using open-source software for motion correction is a viable alternative to the vendor-provided motion-correction software. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40658-022-00442-6

Super-resolution T2-weighted 4D MRI for image guided radiotherapy.

BACKGROUND AND PURPOSE:The superior soft-tissue contrast of 4D-T2w MRI motivates its use for delineation in radiotherapy treatment planning. We address current limitations of slice-selective implementations, including thick slices and artefacts originating from data incompleteness and variable breathing. MATERIALS AND METHODS:A method was developed to calculate midposition and 4D-T2w images of the whole thorax from continuously acquired axial and sagittal 2D-T2w MRI (1.5 × 1.5 × 5.0 mm3). The method employed image-derived respiratory surrogates, deformable image registration and super-resolution reconstruction. Volunteer imaging and a respiratory motion phantom were used for validation. The minimum number of dynamic acquisitions needed to calculate a representative midposition image was investigated by retrospectively subsampling the data (10-30 dynamic acquisitions). RESULTS:Super-resolution 4D-T2w MRI (1.0 × 1.0 × 1.0 mm3, 8 respiratory phases) did not suffer from data incompleteness and exhibited reduced stitching artefacts compared to sorted multi-slice MRI. Experiments using a respiratory motion phantom and colour-intensity projection images demonstrated a minor underestimation of the motion range. Midposition diaphragm differences in retrospectively subsampled acquisitions were <1.1 mm compared to the full dataset. 10 dynamic acquisitions were found sufficient to generate midposition MRI. CONCLUSIONS:A motion-modelling and super-resolution method was developed to calculate high quality 4D/midposition T2w MRI from orthogonal 2D-T2w MRI

Impact of motion compensation and partial volume correction for ¹⁸F-NaF PET/CT imaging of coronary plaque

Recent studies have suggested that ¹⁸F-NaF-PET enables visualization and quantification of plaque micro-calcification in the coronary tree. However, PET imaging of plaque calcification in the coronary arteries is challenging because of the respiratory and cardiac motion as well as partial volume effects. The objective of this work is to implement an image reconstruction framework, which incorporates compensation for respiratory as well as cardiac motion (MoCo) and partial volume correction (PVC), for cardiac ¹⁸F-NaF PET imaging in PET/CT. We evaluated the effect of MoCo and PVC on the quantification of vulnerable plaques in the coronary arteries. Realistic simulations (Biograph TPTV, Biograph mCT) and phantom acquisitions (Biograph mCT) were used for these evaluations. Different uptake values in the calcified plaques were evaluated in the simulations, while three "plaque-type" lesions of 36, 31 and 18 mm³ were included in the phantom experiments. After validation, the MoCo and PVC methods were applied in four pilot NaF-PET patient studies. In all cases, the MoCo-based image reconstruction was performed using the STIR software. The PVC was obtained from a local projection (LP) method, previously evaluated in preclinical and clinical PET. The results obtained show a significant increase of the measured lesion-to-background ratios (LBR) in the MoCo+PVC images. These ratios were further enhanced when using directly the tissue-activities from the LP method, making this approach more suitable for the quantitative evaluation of coronary plaques. When using the LP method on the MoCo images, LBR increased between 200% and 1119% in the simulated data, between 212% and 614% in the phantom experiments and between 46% and 373% in the plaques with positive uptake observed in the pilot patients. In conclusion, we have built and validated a STIR framework incorporating MoCo and PVC for ¹⁸NaF PET imaging of coronary plaques. First results indicate an improved quantification of plaque-type lesions

Fast and radiation-free high-resolution MR cranial bone imaging for pediatric patients

AbstractEach year, 2.2 million pediatric head computed tomography (CT) scans are performed in the United States. Head trauma and craniosynostosis are two of the most common pediatric conditions requiring head CT scans. Head trauma is common in children and one-third of the patients that present to the emergency room undergoes head CT imaging. Craniosynostosis is a congenital disability defined by a prematurely fused cranial suture. Standard clinical care for pediatric patients with head trauma or craniosynostosis uses high-resolution head CT to identify cranial fractures or cranial sutures. Unfortunately, the ionizing radiation of CT imaging imposes a risk to patients, particularly pediatric patients who are vulnerable to radiation. Moreover, multiple CT scans are often performed during follow-up, exacerbating their cumulative risk. The National Cancer Institute reported that radiation exposure from multiple head CT scans will triple the risk of leukemia and brain cancer. Many medical centers have recently removed CT from the postoperative care of craniosynostosis, limiting postoperative evaluation and highlighting the urgent need for radiation-free imaging. Several “Black bone” magnetic resonance imaging (MRI) methods have been introduced as radiation-free alternatives. Despite the initially encouraging results, these methods have not translated into clinical practice due to several challenges, including 1) subjective manual image processing; 2) long acquisition time. Due to poor signal contrast between bone and its surrounding tissues in MR images, existing post-processing methods rely on extensive manual MR segmentation which is subjective, prone to noise and artifacts, hard to reproduce, and time-consuming. As a result, they do not meet the need for clinical diagnosis and have not been employed clinically. A CT scan takes tens of seconds; however, a high-resolution MR scan takes minutes, which may be challenging for pediatric subject compliance and limit clinical adoption. The overall objective of this study is to develop rapid and radiation-free 3D high-resolution MRI methods to provide CT-equivalent information in diagnosing cranial fractures and cranial suture patency for pediatric patients. Two specific aims are proposed to achieve the overall objective. Aim 1: Develop a fully automated deep learning method to synthesize high-resolution pseudo-CT (pCT) of pediatric cranial bone from MR images. Aim 2: Develop a deep learning image reconstruction method to reduce MR acquisition time. Aim 1 is to address the issues of subjective manual image processing. In this aim, we developed a robust and fully automated deep learning method to create pCT images from MRI, which facilitates translating MR cranial bone imaging into clinical practice for pediatric patients. Two 3D patch-based ResUNets were trained using paired MR and CT patches randomly selected from the whole head (NetWH) or in the vicinity of bone, fractures/sutures, or air (NetBA) to synthesize pCT. A third ResUNet was trained to generate a binary brain mask using only MRI. The pCT images from NetWH (pCTNetWH) in the brain area and NetBA (pCTNetBA) in the non-brain area were combined to generate pCTCom. A manual processing method using inverted MR images (iMR) was also employed for comparison. pCTCom had significantly smaller mean absolute errors (MAE) than pCTNetWH and pCTNetBA in the whole head. Dice Similarity Coefficient (DSC) of the segmented bone was significantly higher in pCTCom than in pCTNetWH, pCTNetBA, and iMR. DSC from pCTCom demonstrated significantly reduced age dependence than iMR. Furthermore, pCTCom provided excellent suture and fracture visibility comparable to CT. A fast MR acquisition is highly desirable to translate novel MR cranial to clinical practice in place of CT. However, fast MR acquisition usually results in under-sampled data below the Nyquist rate, leading to artifacts and high noise. Recently, numerous deep learning MR reconstruction methods have been employed to mitigate artifacts and minimize noise. Despite many successes, existing deep learning methods have not accounted for MR k-space sampling density variations. In aim 2, we developed a self-supervised and physics-guided deep learning method by weighting k-space sampling Density in network training Loss (wkDeLo). The proposed method uses an unrolled network with a data consistency (DC) and a regularization (R). A forward Fourier model was used to transform the reconstructed image into k-space. The data consistency between the transformed k-space and the acquired k-space data is enforced in the DC layer. This unrolled network is regularized by k-space deep-learning prior using a convolution neural network. In total, 400 radial spokes were acquired with an acquisition time of 5 minutes. Two disjoint k-space data sets, including the first 1 minute (80 radial spokes) and the remaining 4 minutes (320 radial spokes), were used as the network training input and target. A unique feature of our proposed method is to use a L1 loss weighted by k-space sampling density in an end-to-end training of the unrolled network. Moreover, we also reconstructed images using the same unrolled network structure but without accounting for the k-space sampling density variations in the loss for comparison. In other words, a uniform weighted k-space is used in the training loss (un-wkDeLo). Furthermore, we implemented a well-accepted deep learning reconstruction method, Self-Supervision via Data Undersampling (SSDU) as a baseline method reference. Using the images reconstructed from a 5-min scan as the gold standard, we computed the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) for reconstructed images from 1-min k-space data using SSDU, un-wkDeLo, and wkDeLo. The SSIM and PSNR of the wkDeLo images are significantly higher than both SSDU and un-wkDeLo. Moreover, the wkDeLo reconstructed images have the highest sharpness and the least artifacts and noise. In aim 2, we have demonstrated that high quality MR images at a spatial resolution of 0.6x0.6x0.8 mm3 could be achieved using only 1 min acquisition time. Finally, we evaluated the clinical utility of the proposed MR cranial bone imaging in identifying cranial fractures and cranial suture patency. Clinicians by consensus evaluated the MR-derived pCT images. Acceptable image quality was achieved in greater than 90% of all MR scans; diagnoses were 100% accurate in the subset of patients with acceptable image quality. We have demonstrated that the proposed 3D high-resolution MR cranial bone method provided CT-equivalent images for pediatric patients with head trauma or craniosynostosis. This work will have a profound impact on pediatric health by providing clinicians with a rapid diagnostic tool without radiation safety concerns

Developments in PET-MRI for Radiotherapy Planning Applications

The hybridization of magnetic resonance imaging (MRI) and positron emission tomography (PET) provides the benefit of soft-tissue contrast and specific molecular information in a simultaneous acquisition. The applications of PET-MRI in radiotherapy are only starting to be realised. However, quantitative accuracy of PET relies on accurate attenuation correction (AC) of, not only the patient anatomy but also MRI hardware and current methods, which are prone to artefacts caused by dense materials. Quantitative accuracy of PET also relies on full characterization of patient motion during the scan. The simultaneity of PET-MRI makes it especially suited for motion correction. However, quality assurance (QA) procedures for such corrections are lacking. Therefore, a dynamic phantom that is PET and MR compatible is required. Additionally, respiratory motion characterization is needed for conformal radiotherapy of lung. 4D-CT can provide 3D motion characterization but suffers from poor soft-tissue contrast. In this thesis, I examine these problems, and present solutions in the form of improved MR-hardware AC techniques, a PET/MRI/CT-compatible tumour respiratory motion phantom for QA measurements, and a retrospective 4D-PET-MRI technique to characterise respiratory motion. Chapter 2 presents two techniques to improve upon current AC methods that use a standard helical CT scan for MRI hardware in PET-MRI. One technique uses a dual-energy computed tomography (DECT) scan to construct virtual monoenergetic image volumes and the other uses a tomotherapy linear accelerator to create CT images at megavoltage energies (1.0 MV) of the RF coil. The DECT-based technique reduced artefacts in the images translating to improved μ-maps. The MVCT-based technique provided further improvements in artefact reduction, resulting in artefact free μ-maps. This led to more AC of the breast coil. In chapter 3, I present a PET-MR-CT motion phantom for QA of motion-correction protocols. This phantom is used to evaluate a clinically available real-time dynamic MR images and a respiratory-triggered PET-MRI protocol. The results show the protocol to perform well under motion conditions. Additionally, the phantom provided a good model for performing QA of respiratory-triggered PET-MRI. Chapter 4 presents a 4D-PET/MRI technique, using MR sequences and PET acquisition methods currently available on hybrid PET/MRI systems. This technique is validated using the motion phantom presented in chapter 3 with three motion profiles. I conclude that our 4D-PET-MRI technique provides information to characterise tumour respiratory motion while using a clinically available pulse sequence and PET acquisition method

Multimodality and multi-parametric imaging in abdominal oncology:current and future strategies to harnessing the complementary value of PET/CT and MRI

Medical imaging is essential for the diagnosis, treatment and follow-up of patients with cancer. Combinations of different, complementary imaging modalities are increasingly being used: multimodal imaging. This thesis describes recent developments and expected innovations in research (and application of) combined PET/CT and MRI in patients with abdominal cancer. To this end, the effect of integrated assessment of PET/CT and MRI scans was investigated. This resulted in a different result in 1 in 9 patients, as well as a positive effect on the confidence in the results. As a next step, the value of quantitative parameters from PET/CT and MRI was assessed, to predict the treatment outcome of patients with cancer of the rectum, uterine cervix or anus. This value appears to be limited, but the findings from conventional, visual image assessment, does contribute to the prediction