A learning-based method for online adjustment of C-arm Cone-beam CT source trajectories for artifact avoidance.

PURPOSE: During spinal fusion surgery, screws are placed close to critical nerves suggesting the need for highly accurate screw placement. Verifying screw placement on high-quality tomographic imaging is essential. C-arm cone-beam CT (CBCT) provides intraoperative 3D tomographic imaging which would allow for immediate verification and, if needed, revision. However, the reconstruction quality attainable with commercial CBCT devices is insufficient, predominantly due to severe metal artifacts in the presence of pedicle screws. These artifacts arise from a mismatch between the true physics of image formation and an idealized model thereof assumed during reconstruction. Prospectively acquiring views onto anatomy that are least affected by this mismatch can, therefore, improve reconstruction quality. METHODS: We propose to adjust the C-arm CBCT source trajectory during the scan to optimize reconstruction quality with respect to a certain task, i.e., verification of screw placement. Adjustments are performed on-the-fly using a convolutional neural network that regresses a quality index over all possible next views given the current X-ray image. Adjusting the CBCT trajectory to acquire the recommended views results in non-circular source orbits that avoid poor images, and thus, data inconsistencies. RESULTS: We demonstrate that convolutional neural networks trained on realistically simulated data are capable of predicting quality metrics that enable scene-specific adjustments of the CBCT source trajectory. Using both realistically simulated data as well as real CBCT acquisitions of a semianthropomorphic phantom, we show that tomographic reconstructions of the resulting scene-specific CBCT acquisitions exhibit improved image quality particularly in terms of metal artifacts. CONCLUSION: The proposed method is a step toward online patient-specific C-arm CBCT source trajectories that enable high-quality tomographic imaging in the operating room. Since the optimization objective is implicitly encoded in a neural network trained on large amounts of well-annotated projection images, the proposed approach overcomes the need for 3D information at run-time.

Interferon-α Revisited: Individualized Treatment Management Eased the Selective Pressure of Tyrosine Kinase Inhibitors on BCR-ABL1 Mutations Resulting in a Molecular Response in High-Risk CML Patients.

Bone marrow transplantation or ponatinib treatment are currently recommended strategies for management of patients with chronic myeloid leukemia (CML) harboring the T315I mutation and compound or polyclonal mutations. However, in some individual cases, these treatment scenarios cannot be applied. We used an alternative treatment strategy with interferon-α (IFN-α) given solo, sequentially or together with TKI in a group of 6 cases of high risk CML patients, assuming that the TKI-independent mechanism of action may lead to mutant clone repression. IFN-α based individualized therapy decreases of T315I or compound mutations to undetectable levels as assessed by next-generation deep sequencing, which was associated with a molecular response in 4/6 patients. Based on the observed results from immune profiling, we assumed that the principal mechanism leading to the success of the treatment was the immune activation induced with dasatinib pre-treatment followed by restoration of immunological surveillance after application of IFN-α therapy. Moreover, we showed that sensitive measurement of mutated BCR-ABL1 transcript levels augments the safety of this individualized treatment strategy.