Prediction of the as Low as Diagnostically Acceptable CT Dose for Identification of the Inferior Alveolar Canal Using 3D Convolutional Neural Networks with Multi-Balancing Strategies

Ionizing radiation is necessary for diagnostic imaging and deciding the right radiation dose is extremely critical to obtain a decent quality image. However, increasing the dosage to improve the image quality has risks due to the potential harm from ionizing radiation. Thus, finding the optimal as low as diagnostically acceptable (ALADA) dosage is an open research problem that has yet to be tackled using artificial intelligence (AI) methods. This paper proposes a new multi-balancing 3D convolutional neural network methodology to build 3D multidetector computed tomography (MDCT) datasets and develop a 3D classifier model that can work properly with 3D CT scan images and balance itself over the heavy unbalanced multi-classes. The proposed models were exhaustively investigated through eighteen empirical experiments and three re-runs for clinical expert examination. As a result, it was possible to confirm that the proposed models improved the performance by an accuracy of 5% to 10% when compared to the baseline method. Furthermore, the resulting models were found to be consistent, and thus possibly applicable to different MDCT examinations and reconstruction techniques. The outcome of this paper can help radiologists to predict the suitability of CT dosages across different CT hardware devices and reconstruction algorithms. Moreover, the developed model is suitable for clinical application where the right dose needs to be predicted from numerous MDCT examinations using a certain MDCT device and reconstruction technique

ALADA Dose Optimization in the Computed Tomography of the Temporal Bone: The Diagnostic Potential of Different Low-Dose CT Protocols

Objective: Repeated computed tomography (CT) is essential for diagnosis, surgical planning and follow-up in patients with middle and inner ear pathology. Dose reduction to “as low as diagnostically acceptable” (ALADA) is preferable but challenging. We aimed to compare the diagnostic quality of images of subtle temporal bone structures produced with low doses (LD) and reference protocols (RP). Methods: Two formalin-fixed human cadaver heads were scanned using a 64-slice CT scanner and cone-beam CT (CBCT). The protocols were: RP (120 kV, 250 mA, CTDIvol 83.72 mGy), LD1 (100 kV, 80 mA, CTDIvol 26.79 mGy), LD2 (100 kV, 35 mA, CTDIvol 7.66 mGy), LD3 (80 kV, 40 mA, CTDIvol 4.82 mGy), and CBCT standard protocol. Temporal bone structures were assessed using a 5-point scale. Results: A median score of ≥2 was achieved with protocols such as the tendons of m. tensor tympani (RP/LD1/LD2/CBCT) and m. stapedius (CBCT), the incudostapedial joint (RP/LD1/CBCT), the incudomalleolar joint (RP/LD1/LD2/CBCT), the stapes feet (RP/LD1/CBCT), the stapes head (RP/LD1/LD2/CBCT), the tympanic membrane (RP/LD1/LD2/CBCT), the lamina spiralis ossea (none), the chorda tympani (RP/LD1/CBCT), and the modiolus (RP/LD1/LD2/CBCT). Adaptive statistical iterative reconstructions did not show advantages over the filtered back projection. Conclusions: LD protocols using a CTDIvol of 7.66 mGy may be sufficient for the identification of temporal bone structures