  In recent years, deep-learning-based image processing has emerged as a
valuable tool for medical imaging owing to its high performance. However, the
quality of deep-learning-based methods heavily relies on the amount of training
data; the high cost of acquiring a large dataset is a limitation to their
utilization in medical fields. Herein, based on deep learning, we developed a
computed tomography (CT) modality conversion method requiring only a few
unsupervised images. The proposed method is based on CycleGAN with several
extensions tailored for CT images, which aims at preserving the structure in
the processed images and reducing the amount of training data. This method was
applied to realize the conversion of megavoltage computed tomography (MVCT) to
kilovoltage computed tomography (kVCT) images. Training was conducted using
several datasets acquired from patients with head and neck cancer. The size of
the datasets ranged from 16 slices (two patients) to 2745 slices (137 patients)
for MVCT and 2824 slices (98 patients) for kVCT. The required size of the
training data was found to be as small as a few hundred slices. By statistical
and visual evaluations, the quality improvement and structure preservation of
the MVCT images converted by the proposed model were investigated. As a
clinical benefit, it was observed by medical doctors that the converted images
enhanced the precision of contouring. We developed an MVCT to kVCT conversion
model based on deep learning, which can be trained using only a few hundred
unpaired images. The stability of the model against changes in data size was
demonstrated. This study promotes the reliable use of deep learning in clinical
medicine by partially answering commonly asked questions, such as "Is our data
sufficient?" and "How much data should we acquire?"
