Diagnostic and Interventional Imaging最新文献

Purpose: The purpose of this study was to develop and validate a computer-aided detection (CAD) tool for the detection of pancreatic cancer (PC) on diagnostic and prediagnostic computed tomography (CT) examinations.

Materials and methods: A CAD tool was developed using 2496 contrast-enhanced CT images (596 PCs, 1335 normal pancreas, 565 other pancreatic diseases) from a referral center (October 2004-December 2019) and underwent external validation at two independent institutions (January 2018-December 2020) in a retrospective case-control design. Prediagnostic CT images obtained one to 12 months before the clinical diagnosis of PC, representing clinically challenging or missed images, were collected (November 2004-August 2022) from three referral centers to further evaluate the performance of the CAD tool. Classification performance of the CAD tool was assessed using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), RESULTS: From internal and external datasets, the diagnostic test sets included 200 PCs and 4998 controls of 4744 patients with normal pancreas and 254 patients with other pancreatic diseases (2448 women and 2750 men; median age, 63 years; age range: 18-101). The CAD tool achievedan AUC of 0.950 (95 % confidence interval [CI]: 0.932-0.968), 90.0 % sensitivity (180 out of 200; 95 % CI: 85.0-93.8), and 87.8 % specificity (4389 out of 4998; 95 % CI: 86.9-88.7) in the diagnosis of PC. For prediagnostic test sets, which included 54 PCs and 118 controls of 89 patients with normal pancreas and 19 patients with other pancreatic diseases (63 women and 99 men; median age, 61 years; age range: 18-99), the sensitivity was 66.7 % (36 out of 54; 95 % CI: 52.5-78.9). Sensitivities for PCs ≤ 2 cm were 77.1 % (27 out of 35; 95 % CI: 59.9-89.6) and 66.7 % (14 out of 21; 95 % CI: 43.0-85.4) in diagnostic and prediagnostic test sets, respectively.

Conclusion: This CAD tool demonstrates high diagnostic performance for the detection of PC, including for small PC or clinically unrecognized patients.

Photon-counting computed tomography (PCCT) is a significant technological advancement in musculoskeletal imaging. Unlike traditional CT detectors, which are energy-integrating detectors, PCCT uses direct-conversion technology, or photon-counting detectors. This enables ultra-high spatial resolution, systematic spectral imaging, and effective electronic noise reduction without increasing radiation exposure. This review article illustrates the potential benefit of PCCT in clinical practice across a broad spectrum of musculoskeletal disorders. PCCT is expected to improve the detection and characterization of fractures, infections, inflammatory and degenerative arthropathies, bone marrow disorders, tumors, congenital bone diseases, and postoperative complications. It will also assist with interventional procedures. PCCT holds great promise for opportunistic imaging and artificial intelligence-driven analytics in musculoskeletal radiology.

Purpose: The purpose of this study was to determine the capabilities of dynamic susceptibility contrast (DSC)‑derived microvascular and oxygen metabolism metrics to distinguish radiation necrosis (RN) from tumor progression (TP) in irradiated brain metastases.

Materials and methods: Fifty‑eight patients who completed cranial irradiation and underwent DSC perfusion MRI between August 2014 and August 2024 were retrospectively included. There were 31 men and 27 women, with a median age of 60.5 years (first quartile [Q1], 52.3; third quartile [Q3], 68.8). Perfusion, microvascular, and metabolic maps were generated with commercially available software. Lesion‑to‑white‑matter ratios were computed for all DSC-derived microvascular and oxygenation metrics including relative cerebral blood volume (rCBV) and oxygen extraction fraction (rOEF). Reference diagnoses were histopathology (n = 11) or multidisciplinary follow‑up (n = 47). Logistic regression analysis was used to identify metrics associated with RN versus TP, and receiver operating characteristic curve analysis was used to estimate diagnostic performance. For prognosis, overall survival was analyzed using Cox proportional hazards models.

Results: A total of 58 brain lesions were studied, including 34 TPs and 24 RNs. Patients with RN had longer overall survival than those with TP (median not reached vs. 22 months; P = 0.01). Among all metrics, only rCBV and rOEF differed significantly. TP showed higher median rCBV (1.8; Q1, 1.2; Q3, 2.8) than RN (1.1; Q1, 0.6; Q3, 1.9) (P = 0.02). RN exhibited greater median rOEF (1.9; Q1, 1.4; Q3, 2.1) than TP (1.5; Q1, 1.3; Q3, 1.8, P = 0.03). rCBV achieved an area under the receiver operating characteristic curve (AUC) of 0.69 (95 % confidence interval [CI]: 0.54-0.83), rOEF an AUC of 0.66 (95 % CI: 0.52-0.81), and their combination and AUC of 0.74 (95 % CI: 0.60-0.87) without significant differences (P ≥ 0.19). After adjusting for rCBV in multivariable analysis, rOEF remained significantly associated with RN (odds ratio, 0.23; 95 % CI: 0.06-0.72; P = 0.02). A greater rOEF was also associated with a longer overall survival in Cox analysis (adjusted hazard ratio, 0.72; 95 % CI: 0.55-0.95. P = 0.02).

Conclusion: Elevated rCBV is in favor of the diagnosis of TP whereas increased rOEF is in favor of the diagnosis of RN in patients with irradiated brain metastases. Although combining metrics did not confer significant diagnostic advantages, rOEF shows an independent association with longer overall survival.

Purpose: The purpose of this study was to assess the performance of iterative reconstruction (IR) and deep-learning image reconstruction (DLR) algorithms developed by four CT vendors in terms of image quality.

Materials and methods: Acquisitions were performed on an image quality phantom at three dose levels (1.8, 6 and 11 mGy) using four CT systems (further referred to as G-CT, P-CT, U-CT, and C-CT). For each CT, raw data were reconstructed using the commonly used soft tissue kernel and level for IR and DLR algorithms. Noise power spectrum and task-based transfer function were computed to assess noise magnitude, noise texture (f_av) and spatial resolution, respectively. Detectability indexes (d') were computed to model the detection of two abdominal lesions.

Results: Compared to IR, noise magnitude reduction with DLR was similar for all dose levels for G-CT (-21.1 ± 1.5 [standard deviation (SD)] %) and P-CT (-48.4 ± 0.1 [SD] %) but more pronounced at 1.8 mGy and decreased as the dose level increased for U-CT and C-CT. Noise texture was greater with DLR than IR at all dose levels for all CT systems, except for U-CT, which gave similar f_av values. For both inserts, spatial resolution was better with DLR than with IR for all CT systems, except for the low-contrast insert with C-CT at 1.8 and 6 mGy and P-CT at 1.8 mGy. For both simulated lesions and all dose levels, d' values were greater with DLR than with IR by 77.5 ± 8.7 (SD) % for C-CT, 33.7 ± 5.6 (SD) % for G-CT, 112.7 ± 4.7 (SD) % for P-CT and from 158.3 % to 546.6 % on average for U-CT.

Conclusion: Compared to IR, DLR algorithms reduce the image noise and improve detectability whilst providing similar or better noise texture and spatial resolution.