Stationary chest tomosynthesis using a carbon nanotube x-ray source array: a feasibility study.

Chest tomosynthesis is a low-dose, quasi-3D imaging modality that has been demonstrated to improve the detection sensitivity for small lung nodules, compared to 2D chest radiography. The purpose of this study is to investigate the feasibility and system requirements of stationary chest tomosynthesis (s-DCT) using a spatially distributed carbon nanotube (CNT) x-ray source array, where the projection images are collected by electronically activating individual x-ray focal spots in the source array without mechanical motion of the x-ray source, detector, or the patient. A bench-top system was constructed using an existing CNT field emission source array and a flat panel detector. The tube output, beam quality, focal spot size, system in-plane and in-depth resolution were characterized. Tomosynthesis slices of an anthropomorphic chest phantom were reconstructed for image quality assessment. All 75 CNT sources in the source array were shown to operate reliably at 80 kVp and 5 mA tube current. Source-to-source consistency in the tube current and focal spot size was observed. The incident air kerma reading per mAs was measured as 74.47 uGy mAs(-1) at 100 cm. The first half value layer of the beam was 3 mm aluminum. An average focal spot size of 2.5  ×  0.5 mm was measured. The system MTF was measured to be 1.7 cycles mm(-1) along the scanning direction, and 3.4 cycles mm(-1) perpendicular to the scanning direction. As the angular coverage of 11.6°-34°, the full width at half maximum of the artifact spread function improved greatly from 9.5 to 5.2 mm. The reconstructed tomosynthesis slices clearly show airways and pulmonary vascular structures in the anthropomorphic lung phantom. The results show the CNT source array is capable of generating sufficient dose for chest tomosynthesis imaging. The results obtained so far suggest an s-DCT using a distributed CNT x-ray source array is feasible.

Intraoperative stereoscopic 3D video imaging: pushing the boundaries of surgical visualisation and applications for neurosurgical education.

OBJECTIVE: In the past decades, we have witnessed waves of interest in three-dimensional (3D) stereoscopic imaging. Previously, the complexity associated with 3D technology led to its absence in the operating room. But recently, the public's resurrection of interest in this imaging modality has revived its exploration in surgery. Technological advances have also paved the way for incorporation of 3D stereoscopic imaging in neurosurgical education. METHODS: Herein, the authors discuss the advantages of intraoperative 3D recording and display for neurosurgical learning and contemplate its future directions based on their experience with 3D technology and a review of the literature. RESULTS: Potential benefits of stereoscopic displays include an enhancement of subjective image quality, proper identification of the structure of interest from surrounding tissues and improved surface detection and depth judgment. Such benefits are critical during the intraoperative decision-making process and proper handling of the lesion (specifically, for surgery on aneurysms and tumours), and should therefore be available to the observers in the operating room and residents in training. Our trainees can relive the intraoperative experience of the primary surgeon by reviewing the recorded stereoscopic 3D videos. CONCLUSION: Proper 3D knowledge of surgical anatomy is important for operative success. 3D stereoscopic viewing of this anatomy may accelerate the learning curve of trainees and improve the standards of surgical teaching. More objective studies are relevant in further establishing the value of 3D technology in neurosurgical education.

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.

PURPOSE: The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. METHODS: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ("nodule > or =3 mm," "nodule <3 mm," and "non-nodule > or =3 mm"). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus. RESULTS: The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked "nodule > or =3 mm" by at least one radiologist, of which 928 (34.7%) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings. CONCLUSIONS: The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.