Artificial intelligence solution to accelerate the acquisition of MRI images: Impact on the therapeutic care in oncology in radiology and radiotherapy departments.

PURPOSE: MRI is essential in the management of brain tumours. However, long waiting times reduce patient accessibility. Reducing acquisition time could improve access but at the cost of spatial resolution and diagnostic quality. A commercially available artificial intelligence (AI) solution, SubtleMR™, can increase the resolution of acquired images. The objective of this prospective study was to evaluate the impact of this algorithm that halves the acquisition time on the detectability of brain lesions in radiology and radiotherapy. MATERIAL AND METHODS: The T1/T2 MRI of 33 patients with brain metastases or meningiomas were analysed. Images acquired quickly have a matrix divided by two which halves the acquisition time. The visual quality and lesion detectability of the AI images were evaluated by radiologists and radiation oncologist as well as pixel intensity and lesions size. RESULTS: The subjective quality of the image is lower for the AI images compared to the reference images. However, the analysis of lesion detectability shows a specificity of 1 and a sensitivity of 0.92 and 0.77 for radiology and radiotherapy respectively. Undetected lesions on the IA image are lesions with a diameter less than 4mm and statistically low average gadolinium-enhancement contrast. CONCLUSION: It is possible to reduce MRI acquisition times by half using the commercial algorithm to restore the characteristics of the image and obtain good specificity and sensitivity for lesions with a diameter greater than 4mm.

The retrospective binning method improves the consistency of phase binning in respiratory-gated PET/CT.

This study assesses the accuracy of prospective phase-gated PET/CT data binning and presents a retrospective data binning method that improves image quality and consistency. Respiratory signals from 17 patients who underwent 4D PET/CT were analysed to evaluate the reproducibility of temporal triggers used for the standard phase-based gating method. Breathing signals were reprocessed to implement retrospective PET data binning. The mean and standard deviation of time lags between automatic triggers provided by the Real-time Position Management (RPM, Varian) gating device and inhalation peaks derived from respiratory curves were computed for each patient. The total number of respiratory cycles available for 4D PET/CT according to the binning mode (prospective versus retrospective) was compared. The maximum standardized uptake value (SUV(max)), biological tumour volume (BTV) and tumour trajectory measures were determined from the PET/CT images of five patients. Compared to retrospective binning (RB), prospective gating approach led to (i) a significant loss in breathing cycles (15%) and (ii) the inconsistency of data binning due to temporal dispersion of triggers (average 396 ms). Consequently, tumour characterization could be impacted. In retrospective mode, SUV(max) was up to 27% higher, where no significant difference appeared in BTV. In addition, prospective mode gave an inconsistent spatial location of the tumour throughout the bins. Improved consistency with breathing patterns and greater motion amplitude of the tumour centroid were observed with retrospective mode. The detection of the tumour motion and trajectory was improved also for small temporal dispersion of triggers. This study shows that the binning mode could have a significant impact on 4D PET images. The consistency of triggers with breathing signals should be checked before clinical use of gated PET/CT images, and our RB method improves 4D PET/CT image quantification.