Deconvolution-based analysis of CT and MR brain perfusion data is widely used in clinical practice and it is still a topic of ongoing research activities. In this paper, we present a comprehensive derivation and explanation of the underlying physiological model for intravascular tracer systems. We also discuss practical details that are needed to properly implement algorithms for perfusion analysis. Our description of the practical computer implementation is focused on the most frequently employed algebraic deconvolution methods based on the singular value decomposition. In particular, we further discuss the need for regularization in order to obtain physiologically reasonable results. We include an overview of relevant preprocessing steps and provide numerous references to the literature. We cover both CT and MR brain perfusion imaging in this paper because they share many common aspects. The combination of both the theoretical as well as the practical aspects of perfusion analysis explicitly emphasizes the simplifications to the underlying physiological model that are necessary in order to apply it to measured data acquired with current CT and MR scanners.

BACKGROUND AND PURPOSE CTP imaging in the interventional suite could reduce delays to the start of image-guided interventions and help determine the treatment progress and end point. However, C-arms rotate slower than clinical CT scanners, making CTP challenging. We developed a cerebral CTP protocol for C-arm CBCT and evaluated it in an animal study. MATERIALS AND METHODS Five anesthetized swine were imaged by using C-arm CBCT and conventional CT. The C-arm rotates in 4.3 seconds plus a 1.25-second turnaround, compared with 0.5 seconds for clinical CT. Each C-arm scan had 6 continuous bidirectional sweeps. Multiple scans each with a different delay to the start of an aortic arch iodinated contrast injection and a novel image reconstruction algorithm were used to increase temporal resolution. Three different scan sets (consisting of 6, 3, or 2 scans) and 3 injection protocols (3-mL/s 100%, 3-mL/s 67%, and 6-mL/s 50% contrast concentration) were studied. CBF maps for each scan set and injection were generated. The concordance and Pearson correlation coefficients (ρ and r) were calculated to determine the injection providing the best match between the following: the left and right hemispheres, and CT and C-arm CBCT. RESULTS The highest ρ and r values (both 0.92) for the left and right hemispheres were obtained by using the 6-mL 50% iodinated contrast concentration injection. The same injection gave the best match for CT and C-arm CBCT for the 6-scan set (ρ = 0.77, r = 0.89). Some of the 3-scan and 2-scan protocols provided matches similar to those in CT. CONCLUSIONS This study demonstrated that C-arm CBCT can produce CBF maps that correlate well with those from CTP.

IMPORTANCEMost early lung cancers present as pulmonary nodules on imaging, but these can be easily missed on chest radiographs. OBJECTIVE To assess if a novel artificial intelligence (AI) algorithm can help detect pulmonary nodules on radiographs at different levels of detection difficulty. DESIGN, SETTING, AND PARTICIPANTSThis diagnostic study included 100 posteroanterior chest radiograph images taken between 2000 and 2010 of adult patients from an ambulatory health care center in Germany and a lung image database in the US. Included images were selected to represent nodules with different levels of detection difficulties (from easy to difficult), and comprised both normal and nonnormal control.EXPOSURES All images were processed with a novel AI algorithm, the AI Rad Companion Chest X-ray. Two thoracic radiologists established the ground truth and 9 test radiologists from Germany and the US independently reviewed all images in 2 sessions (unaided and AI-aided mode) with at least a 1-month washout period. MAIN OUTCOMES AND MEASURESEach test radiologist recorded the presence of 5 findings (pulmonary nodules, atelectasis, consolidation, pneumothorax, and pleural effusion) and their level of confidence for detecting the individual finding on a scale of 1 to 10 (1 representing lowest confidence; 10, highest confidence). The analyzed metrics for nodules included sensitivity, specificity, accuracy, and receiver operating characteristics curve area under the curve (AUC). RESULTSImages from 100 patients were included, with a mean (SD) age of 55 (20) years and including 64 men and 36 women. Mean detection accuracy across the 9 radiologists improved by 6.4% (95% CI, 2.3% to 10.6%) with AI-aided interpretation compared with unaided interpretation. Partial AUCs within the effective interval range of 0 to 0.2 false positive rate improved by 5.6% (95% CI, −1.4% to 12.0%) with AI-aided interpretation. Junior radiologists saw greater improvement in sensitivity for nodule detection with AI-aided interpretation as compared with their senior counterparts (12%; 95% CI, 4% to 19% vs 9%; 95% CI, 1% to 17%) while senior radiologists experienced similar improvement in specificity (4%; 95% CI, −2% to 9%) as compared with junior radiologists (4%; 95% CI, −3% to 5%). CONCLUSIONS AND RELEVANCEIn this diagnostic study, an AI algorithm was associated with improved detection of pulmonary nodules on chest radiographs compared with unaided interpretation for different levels of detection difficulty and for readers with different experience.

BACKGROUND AND PURPOSE: Assessment of perfusion parameters is important in the selection of patients who are most likely to benefit from revascularization after an acute ischemic stroke. The aim of this study was to evaluate the feasibility of measuring cerebral perfusion parameters with the use of a novel high-speed C-arm CT acquisition in conjunction with a single intravenous injection of contrast.