Patricia Johnson – författare

I went to a Salvation Army dinner with some of the staff of our church and came out knowing something had happened to me spiritually! The next morning, in my devotions, singing In My Life Lord Be Glorified, I heard, I am glorified. It was the beginning of my poetry. In the first poem He gave me, I realized He had birthed in my heart a ministry of missions. My mission field is wherever the Lord places me. I love sharing Jesus with everyone, but my special love is young people.

Ive been blessed to spend much time with the Lord, sitting at His feet, in worship and praise, which what much of this poetry is about. Many are about sharing Jesus with the lost and the importance of salvation. They show His love and compassion for each and every one of us in different ways. One shows the Lords sense of humor, in that its about how the Lord defeated the Devil through the Cross at Calvary. Most are prophetic and about winning the lost to Jesus. Remember, as you are reading and being blessed, Jesus loves each and every one of you so much, He shed His blood and died on the cross at Calvary for all of our sins. Hes resurrected, hallelujah, and seated at the right hand of God the Father, interceding for all who have been saved and are His! Praise God!

This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshop was held virtually.

The 15 papers presented were carefully reviewed and selected from 18 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.

The 13 papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.

The 15 papers presented were carefully reviewed and selected from 19 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.