METHODS: A 3-step framework was proposed, consisting of: (1) 3D LV model reconstruction from motion-corrected 4D cine-MRI; (2) Registration of 2D LGE-MRI with 4D cine-MRI; (3) LV contour extraction from the intersection of LGE slices with the LV model. The framework was evaluated against cardiac MRI data from 27 patients scanned within 6 months after acute myocardial infarction. We compared the use of local Pearson's correlation (LPC) and normalized mutual information (NMI) as similarity measures for the registration. The use of 2 and 6 long-axis (LA) cine-MRI scans was also compared. The accuracy of the framework was evaluated using manual segmentation, and the interobserver variability of the scar volume derived from the segmented LV was determined using Bland-Altman analysis.
RESULTS: LPC outperformed NMI as a similarity measure for the proposed framework using 6 LA scans, with Hausdorrf distance (HD) of 1.19 ± 0.53 mm versus 1.51 ± 2.01 mm (endocardial) and 1.21 ± 0.48 mm versus 1.46 ± 1.78 mm (epicardial), respectively. Segmentation using 2 LA scans was comparable to 6 LA scans with a HD of 1.23 ± 0.70 mm (endocardial) and 1.25 ± 0.74 mm (epicardial). The framework yielded a lower interobserver variability in scar volumes compared with manual segmentation.
CONCLUSION: The framework showed high accuracy and robustness in delineating LV in LGE-MRI and allowed for bidirectional mapping of information between LGE- and cine-MRI scans, crucial in personalized model studies for treatment planning.
Material and Methods: This is a prospective case series conducted on patients who were diagnosed with type V osteochondral lesions of talus. All the cases were treated with arthroscopic debridement, microfracture, and PRGF injections. The patients were evaluated for the healing of subchondral cysts and progression of osteoarthritis with radiography (plain radiographs and computerised tomography Scan). Also, the patients' outcome was evaluated with Quadruple Visual Analogue Scale, Ankle Range of Motion, Foot and Ankle Disability Index, Foot and Ankle Outcome Instrument and a Satisfaction Questionnaire.
Results: Five male patients underwent arthroscopic debridement, microfracture and PRGF injection for type V osteochondral lesion of talus. The mean age of patients was 27.4 years (19-47 years). All the patients gave history of minor twisting injury. Subchondral cyst healing was achieved in all patients by six months post-surgery. However, four out of five patients had developed early osteoarthritic changes of the ankle by their last follow-up [mean follow-up 29 months (ranged 15-36 months)]. Despite arthritic changes, all the patients reported 'Good' to 'Excellent' results on satisfaction questionnaire and Foot and Ankle Disability Index and could perform their day to day activities including sports.
Conclusion: Arthroscopic debridement, microfracture, and PRGF causes healing of the subchondral cyst but does not cause cessation of progression to osteoarthritis of ankle in type V osteochondral defects of talus. However, despite progress to osteoarthritis, patient satisfaction post-procedure is good to excellent at short-term follow-up.
METHODS: Continuous raw PPG waveforms were blindly allocated into segments with an equal length (5s) without leveraging any pulse location information and were normalized with Z-score normalization methods. A 1-D-CNN was designed to automatically learn the intrinsic features of the PPG waveform, and perform the required classification. Several training hyperparameters (initial learning rate and gradient threshold) were varied to investigate the effect of these parameters on the performance of the network. Subsequently, this proposed network was trained and validated with 30 subjects, and then tested with eight subjects, with our local dataset. Moreover, two independent datasets downloaded from the PhysioNet MIMIC II database were used to evaluate the robustness of the proposed network.
RESULTS: A 13 layer 1-D-CNN model was designed. Within our local study dataset evaluation, the proposed network achieved a testing accuracy of 94.9%. The classification accuracy of two independent datasets also achieved satisfactory accuracy of 93.8% and 86.7% respectively. Our model achieved a comparable performance with most reported works, with the potential to show good generalization as the proposed network was evaluated with multiple cohorts (overall accuracy of 94.5%).
CONCLUSION: This paper demonstrated the feasibility and effectiveness of applying blind signal processing and deep learning techniques to PPG motion artifact detection, whereby manual feature thresholding was avoided and yet a high generalization ability was achieved.