OBJECTIVE: The aim was to present a model of CT-MRI registration used to diagnose liver cancer, specifically for improving the quality of the liver images and provide all the required information for earlier detection of the tumors. This method should concurrently address the issues of imaging procedures for liver cancer to fasten the detection of the tumor from both modalities.
METHODS: In this work, a registration scheme for fusing the CT and MRI liver images is studied. A feature point-based method with normalized cross-correlation has been utilized to aid in the diagnosis of liver cancer and provide multimodal information to physicians. Data on ten patients from an online database were obtained. For each dataset, three planar views from both modalities were interpolated and registered using feature point-based methods. The registration of algorithms was carried out by MATLAB (vR2019b, Mathworks, Natick, USA) on an Intel (R) Core (TM) i5-5200U CPU @ 2.20 GHz computer. The accuracy of the registered image is being validated qualitatively and quantitatively.
RESULTS: The results show that an accurate registration is obtained with minimal distance errors by which CT and MRI were accurately registered based on the validation of the experts. The RMSE ranges from 0.02 to 1.01 for translation, which is equivalent in magnitude to approximately 0 to 5 pixels for CT and registered image resolution.
CONCLUSION: The CT-MRI registration scheme can provide complementary information on liver cancer to physicians, thus improving the diagnosis and treatment planning process.
METHODS: This is a prospective, randomised, crossover, single-blinded study conducted from February 2018 to February 2019 among adult subjects attending respiratory clinic Universiti Kebangsaan Malaysia Medical Centre (UKMMC).
RESULTS: Forty-six subjects were recruited with 27 males (58.7%). The mean age was 54 (+11) year old. The baseline median Body Mass Index (BMI) was 34.2 kg/m2 (Interquartile Range IQR: 30.8 kg/m2 -41.7 kg/m2); baseline median AHI 28.8 /hour (IQR 21.2/hour-54.0/hour); andbaseline median ESS 15 (IQR 13-16). After intervention, the median AHI was 5.0 / hour (IQR 4.2/hour-6.0/hour) at fixed CPAP arm; APAP arm was 5.5/ hour (IQR 4.2/hour-6.3/hour); p<0.01. The median ESS at fixed CPAP arm was 2 (IQR 0-3); APAP arm was 2 (IQR 1-3); p < 0.01. Those who preferred APAP were 22 subjects (47.8%) and had median optimal CPAP pressure 13.0 cmH2O (IQR 12.0 cmH2O -13.5 cmH2O); 24 subjects (52.2%) who preferred Fixed CPAP had median optimal CPAP pressure 8.0 cmH2O (IQR 6.3 cmH2O -8.7 cmH2O); p<0.01. Median baseline BMI was 37.6 kg/m2 (IQR 30.8 kg/m2 -43.0 kg/m2) for those who preferred APAP and 32.3 kg/m2 (IQR 30.8 kg/m2 - 38.4 kg/m2) for subjects preferred Fixed CPAP; p=0.03.
DISCUSSION: Fixed CPAP maybe considered as first line therapy for symptomatic moderate and severe OSA with titrated optimal CPAP pressure less than 8 cmH2O and BMI less than 32.3 kg/m2; based on subjects' preference. Baseline AHI and average daily CPAP usage was not statisticallysignificant in affecting patient preference between fixed and auto adjusting CPAP. This is the first study of its kind conducted in Malaysia.