Dual-domain accelerated MRI reconstruction using transformers with learning-based undersampling.

Acceleration in MRI has garnered much attention from the deep-learning community in recent years, particularly for imaging large anatomical volumes such as the abdomen or moving targets such as the heart. A variety of deep learning approaches have been investigated, with most existing works using convolutional neural network (CNN)-based architectures as the reconstruction backbone, paired with fixed, rather than learned, k-space undersampling patterns. In both image domain and k-space, CNN-based architectures may not be optimal for reconstruction due to its limited ability to capture long-range dependencies. Furthermore, fixed undersampling patterns, despite ease of implementation, may not lead to optimal reconstruction. Lastly, few deep learning models to date have leveraged temporal correlation across dynamic MRI data to improve reconstruction. To address these gaps, we present a dual-domain (image and k-space), transformer-based reconstruction network, paired with learning-based undersampling that accepts temporally correlated sequences of MRI images for dynamic reconstruction. We call our model DuDReTLU-net. We train the network end-to-end against fully sampled ground truth dataset. Human cardiac CINE images undersampled at different factors (5-100) were tested. Reconstructed images were assessed both visually and quantitatively via the structural similarity index, mean squared error, and peak signal-to-noise. Experimental results show superior performance of DuDReTLU-net over state-of-the-art methods (LOUPE, k-t SLR, BM3D-MRI) in accelerated MRI reconstruction; ablation studies show that transformer-based reconstruction outperformed CNN-based reconstruction in both image domain and k-space; dual-domain reconstruction architectures outperformed single-domain reconstruction architectures regardless of reconstruction backbone (CNN or transformer); and dynamic sequence input leads to more accurate reconstructions than single frame input. We expect our results to encourage further research in the use of dual-domain architectures, transformer-based architectures, and learning-based undersampling, in the setting of accelerated MRI reconstruction. The code for this project is made freely available at https://github.com/william2343/dual-domain-mri-recon-nets (Hong et al., 2022).

Wnt-associated adult stem cell marker Lgr6 is required for osteogenesis and fracture healing.

Despite the remarkable regenerative capacity of skeletal tissues, nonunion of bone and failure of fractures to heal properly presents a significant clinical concern. Stem and progenitor cells are present in bone and become activated following injury; thus, elucidating mechanisms that promote adult stem cell-mediated healing is important. Wnt-associated adult stem marker Lgr6 is implicated in the regeneration of tissues with well-defined stem cell niches in stem cell-reliant organs. Here, we demonstrate that Lgr6 is dynamically expressed in osteoprogenitors in response to fracture injury. We used an Lgr6-null mouse model and found that Lgr6 expression is necessary for maintaining bone volume and efficient postnatal bone regeneration in adult mice. Skeletal progenitors isolated from Lgr6-null mice have reduced colony-forming potential and reduced osteogenic differentiation capacity due to attenuated cWnt signaling. Lgr6-null mice consist of a lower proportion of self-renewing stem cells. In response to fracture injury, Lgr6-null mice have a deficiency in the proliferation of periosteal progenitors and reduced ALP activity. Further, analysis of the bone regeneration phase and remodeling phase of fracture healing in Lgr6-null mice showed impaired endochondral ossification and decreased mineralization. We propose that in contrast to not being required for successful skeletal development, Lgr6-positive cells have a direct role in endochondral bone repair.

Diabetes impairs periosteal progenitor regenerative potential.

Diabetics are at increased risk for fracture, and experience severely impaired skeletal healing characterized by delayed union or nonunion of the bone. The periosteum harbors osteochondral progenitors that can differentiate into chondrocytes and osteoblasts, and this connective tissue layer is required for efficient fracture healing. While bone marrow-derived stromal cells have been studied extensively in the context of diabetic skeletal repair and osteogenesis, the effect of diabetes on the periosteum and its ability to contribute to bone regeneration has not yet been explicitly evaluated. Within this study, we utilized an established murine model of type I diabetes to evaluate periosteal cell differentiation capacity, proliferation, and availability under the effect of a diabetic environment. Periosteal cells from diabetic mice were deficient in osteogenic differentiation ability in vitro, and diabetic mice had reduced periosteal populations of mesenchymal progenitors with a corresponding reduction in proliferation capacity following injury. Additionally, fracture callus mineralization and mature osteoblast activity during periosteum-mediated healing was impaired in diabetic mice compared to controls. We propose that the effect of diabetes on periosteal progenitors and their ability to aid in skeletal repair directly impairs fracture healing.

A cost-minimisation analysis comparing different antibiotic regimens used in treating all-cause bacterial pneumonia in Hong Kong.

BACKGROUND: To find out the antibiotic treatment regimens with the lowest cost for all-cause bacterial pneumonia, a study to compare the costs of different antibiotic regimens in the treatment of patients diagnosed with all-cause bacterial pneumonia who required hospitalisation was carried out. METHODOLOGY: This was a multicentre, retrospective study of patient medical records. The primary aim was to examine whether the initial choice of antibiotic had affected the total cost of treatment, while the secondary aim was to find out whether the initial choice of antibiotic had affected the initial treatment failure rates and death rates. A cost-minimisation analysis (CMA) from a public hospital perspective was employed. RESULTS: A total of 333 patient medical case notes were reviewed. The most commonly prescribed antibiotic regimen was amoxycillin-clavulanate (AC) followed by amoxycillin-clavulanate plus macrolide (ACM) and quinolone (Q). In the study population, no statistical significance could be detected between the mean cost of the three regimens. In the subgroup analysis of patients with a history of chronic obstructive pulmonary disease (COPD) and patients with a history of smoking, the Q regimen appeared to be the least expensive. CONCLUSION: In the study population, no significant difference could be identified between the mean cost of the three antibiotic regimens. In a special populations such as patients with a history of COPD and patients with a history of smoking, the Q regimen appeared to be superior. Further studies in these areas are needed.