Search

Objectives: The purpose of this study was to determine the influence of dose reduction on a commercially available lung cancer prediction convolutional neuronal network (LCP-CNN). Methods: CT scans from a cohort provided by the local lung cancer center (n = 218) with confirmed pulmonary malignancies and their corresponding reduced dose simulations (25% and 5% dose) were subjected to the LCP-CNN. The resulting LCP scores (scale 1–10, increasing malignancy risk) and the proportion of correctly classified nodules were compared. The cohort was divided into a low-, medium-, and high-risk group based on the respective LCP scores; shifts between the groups were studied to evaluate the potential impact on nodule management. Two different malignancy risk score thresholds were analyzed: a higher threshold of ≥ 9 ("rule-in" approach) and a lower threshold of > 4 ("rule-out" approach). Results: In total, 169 patients with 196 nodules could be included (mean age ± SD, 64.5 ± 9.2 year; 49% females). Mean LCP scores for original, 25% and 5% dose levels were 8.5 ± 1.7, 8.4 ± 1.7 (p > 0.05 vs. original dose) and 8.2 ± 1.9 (p < 0.05 vs. original dose), respectively. The proportion of correctly classified nodules with the "rule-in" approach decreased with simulated dose reduction from 58.2 to 56.1% (p = 0.34) and to 52.0% for the respective dose levels (p = 0.01). For the "rule-out" approach the respective values were 95.9%, 96.4%, and 94.4% (p = 0.12). When reducing the original dose to 25%/5%, eight/twenty-two nodules shifted to a lower, five/seven nodules to a higher malignancy risk group. Conclusion: CT dose reduction may affect the analyzed LCP-CNN regarding the classification of pulmonary malignancies and potentially alter pulmonary nodule management. Clinical relevance statement: Utilization of a "rule-out" approach with a lower malignancy risk threshold prevents underestimation of the nodule malignancy risk for the analyzed software, especially in high-risk cohorts. Key Points: • LCP-CNN may be affected by CT image parameters such as noise resulting from low-dose CT acquisitions. • CT dose reduction can alter pulmonary nodule management recommendations by affecting the outcome of the LCP-CNN. • Utilization of a lower malignancy risk threshold prevents underestimation of pulmonary malignancies in high-risk cohorts. [ABSTRACT FROM AUTHOR]

Purpose: Phantoms are useful tools in diagnostic CT, but practical limitations reduce phantoms to being only a limited patient surrogate. Furthermore, a phantom with a single cross sectional area cannot be used to evaluate scanner performance in modern CT scanners that use dose reduction techniques such as automated tube current modulation (ATCM) and iterative reconstruction (IR) algorithms to adapt x‐ray flux to patient size, reduce radiation dose, and achieve uniform image noise. A new multisized phantom (Mercury Phantom, MP) has been introduced, representing multiple diameters. This work aimed to ascertain if measurements from MP can predict radiation dose and image noise in clinical CT images to prospectively inform protocol design. Methods: The adult MP design included four different physical diameters (18.5, 23.0, 30.0, and 37.0 cm) representing a range of patient sizes. The study included 1457 examinations performed on two scanner models from two vendors, and two clinical protocols (abdominopelvic with and chest without contrast). Attenuating diameter, radiation dose, and noise magnitude (average pixel standard deviation in uniform image) was automatically estimated in patients and in the MP using a previously validated algorithm. An exponential fit of CTDIvol and noise as a function of size was applied to patients and MP data. Lastly, the fit equations from the phantom data were used to fit the patient data. In each patient distribution fit, the normalized root mean square error (nRMSE) values were calculated in the residuals' plots as a metric to indicate how well the phantom data can predict dose and noise in clinical operations as a function of size. Results: For dose across patient size distributions, the difference between nRMSE from patient fit and MP model data prediction ranged between 0.6% and 2.0% (mean 1.2%). For noise across patient size distributions, the nRMSE difference ranged between 0.1% and 4.7% (mean 1.4%). Conclusions: The Mercury Phantom provided a close prediction of radiation dose and image noise in clinical patient images. By assessing dose and image quality in a phantom with multiple sizes, protocol parameters can be designed and optimized per patient size in a highly constrained setup to predict clinical scanner and ATCM system performance. [ABSTRACT FROM AUTHOR]

Purpose: To quantitatively compare noise texture across computed tomography (CT) scanners from different manufacturers using the noise power spectrum (NPS). Methods: The American College of Radiology CT accreditation phantom (Gammex 464, Gammex, Inc., Middleton, WI) was imaged on two scanners: Discovery CT 750HD (GE Healthcare, Waukesha, WI), and SOMATOM Definition Flash (Siemens Healthcare, Germany), using a consistent acquisition protocol (120 kVp, 0.625/0.6 mm slice thickness, 250 mAs, and 22 cm field of view). Images were reconstructed using filtered backprojection and a wide selection of reconstruction kernels. For each image set, the 2D NPS were estimated from the uniform section of the phantom. The 2D spectra were normalized by their integral value, radially averaged, and filtered by the human visual response function. A systematic kernel-by-kernel comparison across manufacturers was performed by computing the root mean square difference (RMSD) and the peak frequency difference (PFD) between the NPS from different kernels. GE and Siemens kernels were compared and kernel pairs that minimized the RMSD and |PFD| were identified. Results: The RMSD (|PFD|) values between the NPS of GE and Siemens kernels varied from 0.01 mm2 (0.002 mm-1) to 0.29 mm2 (0.74 mm-1). The GE kernels 'Soft,' 'Standard,' 'Chest,' and 'Lung' closely matched the Siemens kernels 'B35f,' 'B43f,' 'B41f,' and 'B80f' (RMSD < 0.05 mm2, |PFD| < 0.02 mm-1, respectively). The GE 'Bone,' 'Bone+,' and 'Edge' kernels all matched most closely with Siemens 'B75f' kernel but with sizeable RMSD and |PFD| values up to 0.18 mm2 and 0.41 mm-1, respectively. These sizeable RMSD and |PFD| values corresponded to visually perceivable differences in the noise texture of the images. Conclusions: It is possible to use the NPS to quantitatively compare noise texture across CT systems. The degree to which similar texture across scanners could be achieved varies and is limited by the kernels available on each scanner. [ABSTRACT FROM AUTHOR]

Purpose: The task‐based assessment of image quality using model observers is increasingly used for the assessment of different imaging modalities. However, the performance computation of model observers needs standardization as well as a well‐established trust in its implementation methodology and uncertainty estimation. The purpose of this work was to determine the degree of equivalence of the channelized Hotelling observer performance and uncertainty estimation using an intercomparison exercise. Materials and Methods: Image samples to estimate model observer performance for detection tasks were generated from two‐dimensional CT image slices of a uniform water phantom. A common set of images was sent to participating laboratories to perform and document the following tasks: (a) estimate the detectability index of a well‐defined CHO and its uncertainty in three conditions involving different sized targets all at the same dose, and (b) apply this CHO to an image set where ground truth was unknown to participants (lower image dose). In addition, and on an optional basis, we asked the participating laboratories to (c) estimate the performance of real human observers from a psychophysical experiment of their choice. Each of the 13 participating laboratories was confidentially assigned a participant number and image sets could be downloaded through a secure server. Results were distributed with each participant recognizable by its number and then each laboratory was able to modify their results with justification as model observer calculation are not yet a routine and potentially error prone. Results: Detectability index increased with signal size for all participants and was very consistent for 6 mm sized target while showing higher variability for 8 and 10 mm sized target. There was one order of magnitude between the lowest and the largest uncertainty estimation. Conclusions: This intercomparison helped define the state of the art of model observer performance computation and with thirteen participants, reflects openness and trust within the medical imaging community. The performance of a CHO with explicitly defined channels and a relatively large number of test images was consistently estimated by all participants. In contrast, the paper demonstrates that there is no agreement on estimating the variance of detectability in the training and testing setting. [ABSTRACT FROM AUTHOR]