A moving liver phantom in an anthropomorphic thorax for SPECT MP imaging

Cranio-caudal respiratory motion and liver activity cause a variety of complex myocardial perfusion (MP) artifacts, especially in the inferior myocardial wall, that may also mask cardiac defects. To assess and characterise such artifacts, an anthropomorphic thorax with moving thoracic phantoms can be utilised in SPECT MP imaging. In this study, a liver phantom was developed and anatomically added into an anthropomorphic phantom that also encloses an ECG beating cardiac phantom and breathing lungs’ phantom. A cranio-caudal respiratory motion was also developed for the liver phantom and it was synchronised with the corresponding ones of the other thoracic phantoms. This continuous motion was further divided into isochronous dynamic respiratory phases, from end-exhalation to end-inspiration, to perform SPECT acquisitions in different respiratory phases. The new motions’ parameters and settings were measured by mechanical means and also validated in a clinical environment by acquiring CT images and by using two imaging software packages. To demonstrate the new imaging capabilities of the phantom assembly, SPECT/CT MP acquisitions were performed and compared to previous phantom and patients studies. All thoracic phantoms can precisely perform physiological motions within the anthropomorphic thorax. The new capabilities of the phantom assembly allow to perform SPECT/CT MP acquisitions for different cardiac-liver activity ratios and cardiac-liver proximities in supine and, for first time, in prone position. Thus, MP artifacts can be characterised and motion correction can be performed due to these new capabilities. The impact of artifacts and motion correction on defect detection can be also investigated.

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.

MOCOnet: Robust Motion Correction of Cardiovascular Magnetic Resonance T1 Mapping Using Convolutional Neural Networks

Background: Quantitative cardiovascular magnetic resonance (CMR) T1 mapping has shown promise for advanced tissue characterisation in routine clinical practise. However, T1 mapping is prone to motion artefacts, which affects its robustness and clinical interpretation. Current methods for motion correction on T1 mapping are model-driven with no guarantee on generalisability, limiting its widespread use. In contrast, emerging data-driven deep learning approaches have shown good performance in general image registration tasks. We propose MOCOnet, a convolutional neural network solution, for generalisable motion artefact correction in T1 maps.Methods: The network architecture employs U-Net for producing distance vector fields and utilises warping layers to apply deformation to the feature maps in a coarse-to-fine manner. Using the UK Biobank imaging dataset scanned at 1.5T, MOCOnet was trained on 1,536 mid-ventricular T1 maps (acquired using the ShMOLLI method) with motion artefacts, generated by a customised deformation procedure, and tested on a different set of 200 samples with a diverse range of motion. MOCOnet was compared to a well-validated baseline multi-modal image registration method. Motion reduction was visually assessed by 3 human experts, with motion scores ranging from 0% (strictly no motion) to 100% (very severe motion).Results: MOCOnet achieved fast image registration (<1 second per T1 map) and successfully suppressed a wide range of motion artefacts. MOCOnet significantly reduced motion scores from 37.1±21.5 to 13.3±10.5 (p < 0.001), whereas the baseline method reduced it to 15.8±15.6 (p < 0.001). MOCOnet was significantly better than the baseline method in suppressing motion artefacts and more consistently (p = 0.007).Conclusion: MOCOnet demonstrated significantly better motion correction performance compared to a traditional image registration approach. Salvaging data affected by motion with robustness and in a time-efficient manner may enable better image quality and reliable images for immediate clinical interpretation.

Low-Dose PET Imaging of Tumors in Lung and Liver Regions Using Internal Motion Estimation

Motion estimation and compensation are necessary for improvement of tumor quantification analysis in positron emission tomography (PET) images. The aim of this study was to propose adaptive PET imaging with internal motion estimation and correction using regional artificial evaluation of tumors injected with low-dose and high-dose radiopharmaceuticals. In order to assess internal motion, molecular sieves imitating tumors were loaded with 18F and inserted into the lung and liver regions in rats. All models were classified into two groups, based on the injected radiopharmaceutical activity, to compare the effect of tumor intensity. The PET study was performed with injection of F-18 fluorodeoxyglucose (18F-FDG). Respiratory gating was carried out by external trigger device. Count, signal to noise ratio (SNR), contrast and full width at half maximum (FWHM) were measured in artificial tumors in gated images. Motion correction was executed by affine transformation with estimated internal motion data. Monitoring data were different from estimated motion. Contrast in the low-activity group was 3.57, 4.08 and 6.19, while in the high-activity group it was 10.01, 8.36 and 6.97 for static, 4 bin and 8 bin images, respectively. The results of the lung target in 4 bin and the liver target in 8 bin showed improvement in FWHM and contrast with sufficient SNR. After motion correction, FWHM was improved in both regions (lung: 24.56%, liver: 10.77%). Moreover, with the low dose of radiopharmaceuticals the PET image visualized specific accumulated radiopharmaceutical areas in the liver. Therefore, low activity in PET images should undergo motion correction before quantification analysis using PET data. We could improve quantitative tumor evaluation by considering organ region and tumor intensity.

PatchWarp: Corrections of non-uniform image distortions in two-photon calcium imaging data by patchwork affine transformations

Two-photon microscopy has been widely used to record the activity of populations of individual neurons at high spatial resolution in behaving animals. The ability to perform imaging for an extended period of time allows the investigation of activity changes associated with behavioral states and learning. However, imaging often accompanies shifts of the imaging field, including rapid (~100ms) translation and slow, spatially non-uniform distortion. To combat this issue and obtain a stable time series of the target structures, motion correction algorithms are commonly applied. However, typical motion correction algorithms are limited to full field translation of images and are unable to correct non-uniform distortions. Here, we developed a novel algorithm, PatchWarp, to robustly correct slow image distortion for calcium imaging data. PatchWarp is a two-step algorithm with rigid and non-rigid image registrations. To correct non-uniform image distortions, it splits the imaging field and estimates the best affine transformation matrix for each of the subfields. The distortion-corrected subfields are stitched together like a patchwork to reconstruct the distortion-corrected imaging field. We show that PatchWarp robustly corrects image distortions of calcium imaging data collected from various cortical areas through glass window or GRIN lens with a higher accuracy than existing non-rigid algorithms. Furthermore, it provides a fully automated method of registering images from different imaging sessions for longitudinal neural activity analyses. PatchWarp improves the quality of neural activity analyses and would be useful as a general approach to correct image distortions in a wide range of disciplines.

Inter- and Intraobserver Repeatability of Myocardial Flow Reserve Values Determined with SPECT Study Using a Discovery NM530c Camera and Corridor 4DM Software

Background: Myocardial blood flow (MBF) and flow reserve (MFR) examination, especially useful in the diagnosis of multivessel coronary artery disease (CAD), can be assessed with a cadmium-zinc-telluride (CZT) SPECT gamma camera, as an alternative to the expensive and less available PET. However, study processing is not free from subjective factors. Therefore, this paper aims to evaluate intra- and interobserver repeatability of MBF and MFR values obtained by the same operator and two independent operators. Methods: This study included 57 adult patients. MBF and MFR were assessed using a Discovery NM530c camera in a two-day, rest/dipyridamople protocol, using 99mTc-MIBI. Data were processed using Corridor4DM software, twice by one operator and once by another operator. Results: The repeatability of the assessed values was quite good in the whole myocardium, LAD and LCX vascular territories, but was poor in the RCA territory. Conclusions: The poor repeatability of MBF and MFR in RCA vascular territory can be explained by poor automatic orientation of the heart axis during post-processing and a so-called “cardiac creep” phenomenon. Better automatic heart orientation and introduction of automatic motion correction is likely to drastically improve this repeatability. In the present state of the software, PET is better for patients requiring assessment of MFR in the RCA territory.