Quantitative error prediction of medical image registration using regression forests

Predicting registration error can be useful for evaluation of registration procedures, which is important for the adoption of registration techniques in the clinic. In addition, quantitative error prediction can be helpful in improving the registration quality. The task of predicting registration error is demanding due to the lack of a ground truth in medical images. This paper proposes a new automatic method to predict the registration error in a quantitative manner, and is applied to chest CT scans. A random regression forest is utilized to predict the registration error locally. The forest is built with features related to the transformation model and features related to the dissimilarity after registration. The forest is trained and tested using manually annotated corresponding points between pairs of chest CT scans in two experiments: SPREAD (trained and tested on SPREAD) and inter-database (including three databases SPREAD, DIR-Lab-4DCT and DIR-Lab-COPDgene). The results show that the mean absolute errors of regression are 1.07  ±  1.86 and 1.76  ±  2.59 mm for the SPREAD and inter-database experiment, respectively. The overall accuracy of classification in three classes (correct, poor and wrong registration) is 90.7% and 75.4%, for SPREAD and inter-database respectively. The good performance of the proposed method enables important applications such as automatic quality control in large-scale image analysis

Avaliação da função cardíaca por ressonância magnética com seqüências em equilíbrio estável: segmentadas × tempo real Assessment of cardiac function by magnetic resonance imaging: segmented x real-time steady-state free precession sequences

OBJETIVO: Comparar os índices de função sistólica ventricular obtidos entre as seqüências de cine-ressonância magnética em equilíbrio estável, em tempo real e acoplada ao eletrocardiograma, em pacientes com ritmo regular ou não. MATERIAIS E MÉTODOS: Foram comparados a fração de ejeção e os volumes diastólico e sistólico finais, em 31 pacientes, 11 com ritmo cardíaco irregular e 20 com ritmo cardíaco sinusal regular, utilizando-se seqüências segmentadas acopladas ao eletrocardiograma e em tempo real. O tratamento estatístico foi feito através da correlação de Pearson e a concordância de Bland-Altman, com p < 0,01. RESULTADOS: As aquisições em tempo real demonstraram borramento dos contornos endocárdicos, mas ambas as seqüências tiveram forte correlação positiva entre os valores obtidos: fração de ejeção, r = 0,94; volume diastólico final, r = 0,93; volume sistólico final, r = 0,98. A análise dos 11 pacientes com ritmo irregular não demonstrou diferença estatisticamente significativa, apesar da menor relação de contraste sangue-miocárdio. CONCLUSÃO: Seqüências em tempo real podem ser utilizadas para a análise da função cardíaca, independente do ritmo cardíaco dos pacientes.<br>OBJECTIVE: To compare ventricular systolic parameters on segmented and real-time steady-state free precession cine-MRI sequences and ECG-gated MRI in patients presenting or not with cardiac arrhythmias. MATERIALS AND METHODS: Ejection fraction and end-diastolic/end-systolic volumes have been compared in 31 patients, 11 presenting with cardiac arrhythmias, and 20 with regular sinus rhythm, using ECG-gated segmented and real-time sequences. The statistical analysis was performed using Pearson's correlation and Bland-Altman agreement plot, with p < 0.01. RESULTS: Real-time acquisitions demonstrated endocardial borders blurring effects, but both sequences presented a clear, positive correlation: ejection fraction r = 0.94; end-diastolic volume r = 0.93 and end-systolic volume r = 0.98. The assessment of 11 patients with arrhythmias has not demonstrated a statistically significant difference, despite the lower blood pool-myocardial contrast ratio. CONCLUSION: Real-time sequences may be utilized for cardiac function assessment, regardless the patient's cardiac rhythm