Unsupervised denoising for sparse multi-spectral computed tomography

Multi-energy computed tomography (CT) with photon counting detectors (PCDs) enables spectral imaging as PCDs can assign the incoming photons to specific energy channels. However, PCDs with many spectral channels drastically increase the computational complexity of the CT reconstruction, and bespoke reconstruction algorithms need fine-tuning to varying noise statistics. \rev{Especially if many projections are taken, a large amount of data has to be collected and stored. Sparse view CT is one solution for data reduction. However, these issues are especially exacerbated when sparse imaging scenarios are encountered due to a significant reduction in photon counts.} In this work, we investigate the suitability of learning-based improvements to the challenging task of obtaining high-quality reconstructions from sparse measurements for a 64-channel PCD-CT. In particular, to overcome missing reference data for the training procedure, we propose an unsupervised denoising and artefact removal approach by exploiting different filter functions in the reconstruction and an explicit coupling of spectral channels with the nuclear norm. Performance is assessed on both simulated synthetic data and the openly available experimental Multi-Spectral Imaging via Computed Tomography (MUSIC) dataset. We compared the quality of our unsupervised method to iterative total nuclear variation regularized reconstructions and a supervised denoiser trained with reference data. We show that improved reconstruction quality can be achieved with flexibility on noise statistics and effective suppression of streaking artefacts when using unsupervised denoising with spectral coupling

Local edge computing for radiological image reconstruction and computer-assisted detection: A feasibility study

Computational requirements for data processing at different stages of the radiology value chain are increasing. Cone beam computed tomography (CBCT) is a diagnostic imaging technique used in dental and extremity imaging, involving a highly demanding image reconstruction task. In turn, artificial intelligence (AI) assisted diagnostics are becoming increasingly popular, thus increasing the use of computation resources. Furthermore, the need for fully independent imaging units outside radiology departments and with remotely performed diagnostics emphasize the need for wireless connectivity between the imaging unit and hospital infrastructure. In this feasibility study, we propose an approach based on a distributed edge-cloud computing platform, consisting of small-scale local edge nodes, edge servers with traditional cloud resources to perform data processing tasks in radiology. We are interested in the use of local computing resources with Graphics Processing Units (GPUs), in our case Jetson Xavier NX, for hosting the algorithms for two use-cases, namely image reconstruction in cone beam computed tomography and AI-assisted cancer detection from mammographic images. Particularly, we wanted to determine the technical requirements for local edge computing platform for these two tasks and whether CBCT image reconstruction and breast cancer detection tasks are possible in a diagnostically acceptable time frame. We validated the use-cases and the proposed edge computing platform in two stages. First, the algorithms were validated use-case-wise by comparing the computing performance of the edge nodes against a reference setup (regular workstation). Second, we performed qualitative evaluation on the edge computing platform by running the algorithms as nanoservices. Our results, obtained through real-life prototyping, indicate that it is possible and technically feasible to run both reconstruction and AI-assisted image analysis functions in a diagnostically acceptable computing time. Furthermore, based on the qualitative evaluation, we confirmed that the local edge computing capacity can be scaled up and down during runtime by adding or removing edge devices without the need for manual reconfigurations. We also found all previously implemented software components to be transferable as such. Overall, the results are promising and help in developing future applications, e.g., in mobile imaging scenarios, where such a platform is beneficial

Quantification and visualization of cardiovascular 4D velocity mapping accelerated with parallel imaging or k-t BLAST: head to head comparison and validation at 1.5 T and 3 T

<p>Abstract</p> <p>Background</p> <p>Three-dimensional time-resolved (4D) phase-contrast (PC) CMR can visualize and quantify cardiovascular flow but is hampered by long acquisition times. Acceleration with SENSE or k-t BLAST are two possibilities but results on validation are lacking, especially at 3 T. The aim of this study was therefore to validate quantitative in vivo cardiac 4D-acquisitions accelerated with parallel imaging and k-t BLAST at 1.5 T and 3 T with 2D-flow as the reference and to investigate if field strengths and type of acceleration have major effects on intracardiac flow visualization.</p> <p>Methods</p> <p>The local ethical committee approved the study. 13 healthy volunteers were scanned at both 1.5 T and 3 T in random order with 2D-flow of the aorta and main pulmonary artery and two 4D-flow sequences of the heart accelerated with SENSE and k-t BLAST respectively. 2D-image planes were reconstructed at the aortic and pulmonary outflow. Flow curves were calculated and peak flows and stroke volumes (SV) compared to the results from 2D-flow acquisitions. Intra-cardiac flow was visualized using particle tracing and image quality based on the flow patterns of the particles was graded using a four-point scale.</p> <p>Results</p> <p>Good accuracy of SV quantification was found using 3 T 4D-SENSE (r²= 0.86, -0.7 ± 7.6%) and although a larger bias was found on 1.5 T (r²= 0.71, -3.6 ± 14.8%), the difference was not significant (p = 0.46). Accuracy of 4D k-t BLAST for SV was lower (p < 0.01) on 1.5 T (r²= 0.65, -15.6 ± 13.7%) compared to 3 T (r²= 0.64, -4.6 ± 10.0%). Peak flow was lower with 4D-SENSE at both 3 T and 1.5 T compared to 2D-flow (p < 0.01) and even lower with 4D k-t BLAST at both scanners (p < 0.01). Intracardiac flow visualization did not differ between 1.5 T and 3 T (p = 0.09) or between 4D-SENSE or 4D k-t BLAST (p = 0.85).</p> <p>Conclusions</p> <p>The present study showed that quantitative 4D flow accelerated with SENSE has good accuracy at 3 T and compares favourably to 1.5 T. 4D flow accelerated with k-t BLAST underestimate flow velocities and thereby yield too high bias for intra-cardiac quantitative in vivo use at the present time. For intra-cardiac 4D-flow visualization, however, 1.5 T and 3 T as well as SENSE or k-t BLAST can be used with similar quality.</p

Three alginate lyases provide a new gut <i>Bacteroides ovatus</i> isolate with the ability to grow on alginate

Humans consume alginate in the form of seaweed, food hydrocolloids, and encapsulations, making the digestion of this mannuronic acid (M) and guluronic acid (G) polymer of key interest for human health. To increase knowledge on alginate degradation in the gut, a gene catalog from human feces was mined for potential alginate lyases (ALs). The predicted ALs were present in nine species of the Bacteroidetes phylum, of which two required supplementation of an endo-acting AL, expected to mimic cross-feeding in the gut. However, only a new isolate grew on alginate. Whole-genome sequencing of this alginate-utilizing isolate suggested that it is a new Bacteroides ovatus strain harboring a polysaccharide utilization locus (PUL) containing three ALs of families: PL6, PL17, and PL38. The BoPL6 degraded polyG to oligosaccharides of DP 1-3, and BoPL17 released 4,5-unsaturated monouronate from polyM. BoPL38 degraded both alginates, polyM, polyG, and polyMG, in endo-mode; hence, it was assumed to deliver oligosaccharide substrates for BoPL6 and BoPL17, corresponding well with synergistic action on alginate. BoPL17 and BoPL38 crystal structures, determined at 1.61 and 2.11 Å, respectively, showed (α/α)6-barrel + anti-parallel β-sheet and (α/α)7-barrel folds, distinctive for these PL families. BoPL17 had a more open active site than the two homologous structures. BoPL38 was very similar to the structure of an uncharacterized PL38, albeit with a different triad of residues possibly interacting with substrate in the presumed active site tunnel. Altogether, the study provides unique functional and structural insights into alginate-degrading lyases of a PUL in a human gut bacterium. IMPORTANCE Human ingestion of sustainable biopolymers calls for insight into their utilization in our gut. Seaweed is one such resource with alginate, a major cell wall component, used as a food hydrocolloid and for encapsulation of pharmaceuticals and probiotics. Knowledge is sparse on the molecular basis for alginate utilization in the gut. We identified a new Bacteroides ovatus strain from human feces that grew on alginate and encoded three alginate lyases in a gene cluster. BoPL6 and BoPL17 show complementary specificity toward guluronate (G) and mannuronate (M) residues, releasing unsaturated oligosaccharides and monouronic acids. BoPL38 produces oligosaccharides degraded by BoPL6 and BoPL17 from both alginates, G-, M-, and MG-substrates. Enzymatic and structural characterization discloses the mode of action and synergistic degradation of alginate by these alginate lyases. Other bacteria were cross-feeding on alginate oligosaccharides produced by an endo-acting alginate lyase. Hence, there is an interdependent community in our guts that can utilize alginate