  Despite its tremendous value for the diagnosis, treatment monitoring and
surveillance of children with cancer, whole body staging with positron emission
tomography (PET) is time consuming and associated with considerable radiation
exposure. 100x (1% of the standard clinical dosage) ultra-low-dose/ultra-fast
whole-body PET reconstruction has the potential for cancer imaging with
unprecedented speed and improved safety, but it cannot be achieved by the naive
use of machine learning techniques. In this study, we utilize the global
similarity between baseline and follow-up PET and magnetic resonance (MR)
images to develop Masked-LMCTrans, a longitudinal multi-modality co-attentional
CNN-Transformer that provides interaction and joint reasoning between serial
PET/MRs of the same patient. We mask the tumor area in the referenced baseline
PET and reconstruct the follow-up PET scans. In this manner, Masked-LMCTrans
reconstructs 100x almost-zero radio-exposure whole-body PET that was not
possible before. The technique also opens a new pathway for longitudinal
radiology imaging reconstruction, a significantly under-explored area to date.
Our model was trained and tested with Stanford PET/MRI scans of pediatric
lymphoma patients and evaluated externally on PET/MRI images from T\"ubingen
University. The high image quality of the reconstructed 100x whole-body PET
images resulting from the application of Masked-LMCTrans will substantially
advance the development of safer imaging approaches and shorter exam-durations
for pediatric patients, as well as expand the possibilities for frequent
longitudinal monitoring of these patients by PET.
