Impact of deep learning image reconstruction on volumetric accuracy and image quality of pulmonary nodules with different morphologies in low-dose CT.

BACKGROUND: This study systematically compares the impact of innovative deep learning image reconstruction (DLIR, TrueFidelity) to conventionally used iterative reconstruction (IR) on nodule volumetry and subjective image quality (IQ) at highly reduced radiation doses. This is essential in the context of low-dose CT lung cancer screening where accurate volumetry and characterization of pulmonary nodules in repeated CT scanning are indispensable. MATERIALS AND METHODS: A standardized CT dataset was established using an anthropomorphic chest phantom (Lungman, Kyoto Kaguku Inc., Kyoto, Japan) containing a set of 3D-printed lung nodules including six diameters (4 to 9 mm) and three morphology classes (lobular, spiculated, smooth), with an established ground truth. Images were acquired at varying radiation doses (6.04, 3.03, 1.54, 0.77, 0.41 and 0.20 mGy) and reconstructed with combinations of reconstruction kernels (soft and hard kernel) and reconstruction algorithms (ASIR-V and DLIR at low, medium and high strength). Semi-automatic volumetry measurements and subjective image quality scores recorded by five radiologists were analyzed with multiple linear regression and mixed-effect ordinal logistic regression models. RESULTS: Volumetric errors of nodules imaged with DLIR are up to 50% lower compared to ASIR-V, especially at radiation doses below 1 mGy and when reconstructed with a hard kernel. Also, across all nodule diameters and morphologies, volumetric errors are commonly lower with DLIR. Furthermore, DLIR renders higher subjective IQ, especially at the sub-mGy doses. Radiologists were up to nine times more likely to score the highest IQ-score to these images compared to those reconstructed with ASIR-V. Lung nodules with irregular margins and small diameters also had an increased likelihood (up to five times more likely) to be ascribed the best IQ scores when reconstructed with DLIR. CONCLUSION: We observed that DLIR performs as good as or even outperforms conventionally used reconstruction algorithms in terms of volumetric accuracy and subjective IQ of nodules in an anthropomorphic chest phantom. As such, DLIR potentially allows to lower the radiation dose to participants of lung cancer screening without compromising accurate measurement and characterization of lung nodules.

Pneumosinus dilatans in anterior skull base meningiomas.

INTRODUCTION: Skull base meningiomas are often missed on non-contrast CT or MR examinations due to their close proximity to bone and low lesion to brain contrast. The purpose of this study is to illustrate that pneumosinus dilatans can be an indicator of anterior skull base meningiomas. METHODS: A retrospective search of the radiology information system and picture archiving and computing system database was performed. Search terms were "meningioma" in association with "pneumosinus dilatans." Medical records and imaging studies were reviewed independently by two experienced neuroradiologists and were read in consensus. We recorded the patient age at the time of discovery of the meningioma, main presenting symptom(s), location of the tumor, and imaging characteristics. We also performed a comparative literature search for pneumosinus dilatans and its association with meningiomas. RESULTS: Ten patients (six women; four men) were identified in whom a meningioma of the anterior skull base was associated with a pneumosinus dilatans. Three patients had multiple meningiomas, so a total of 14 intracranial tumors were identified. Mean age at discovery was 59 years with an age range of ± 20years. All meningiomas were diagnosed by MRI and/or CT. CONCLUSION: Pneumosinus dilatans can be a helpful sign to indicate the presence of a meningioma of the anterior skull base.

Lesion detection on a combined "All-in-One" window compared to conventional window settings in thoracic oncology chest CT examinations.

PURPOSE: The purpose of this study was to investigate if lesion detection using a single "All-in-One" (AIO) window was non-inferior to lesion detection on conventional window settings in thoracic oncology chest computed tomography (CT) examinations. MATERIALS AND METHODS: In a retrospective study, 50 consecutive chest CT examinations of 50 patients (31 men, 19 women; mean age 64±10 [SD] years, range: 35-82 years) containing 417 lesions, were reviewed by 6 radiologists, subdivided into 2 groups of 3 radiologists each, with similar levels of expertise in each group (senior staff member, junior staff member and radiology resident). All examinations were reviewed in conventional or AIO window settings by one of the groups. A 'lesion' was defined as any abnormality seen on the chest CT examination, including both benign and malignant lesions, findings in chest and upper abdomen, and measurable and non-measurable disease. Lesions were listed as 'missed' when they were not seen by at least two out of three observers. F-tests were used to evaluate the significance of the variables of interest within a mixed model framework and kappa statistics to report interobserver agreement. RESULTS: On a reader level, 54/417 lesions (12.9%) were not detected by the senior staff member reading the studies in conventional window settings and 45/417 (10.8%) by the senior staff member reading the AIO images. For the junior staff member and radiology resident this was respectively 55/417 (13.2%) and 67/417 (16.1%) for the conventional window settings and 43/417 (10.3%) and 61/417 (14.6%) for the AIO window. On a lesion level, 68/417 (16.3%) were defined as 'missed' lesions (lesions not detected by at least 2 readers): 21/68 (30.9%) on the AIO-window, 30/68 (44.1%) on conventional views and 17/68 (25.0%) on both views. The use of the AIO window did not result in an increase of missed lesions (P>0.99). Interobserver agreement in both groups was similar (P=0.46). Regarding lesions that were categorized as 'missed' on the AIO window or on conventional window settings, there was no effect of location (chest or upper abdomen) (P=0.35), window (P=0.97) and organ (P=0.98). CONCLUSIONS: A single AIO-window is non-inferior to multiple conventional window settings for lesion detection on chest CT examinations in thoracic oncology patients.

Porcine liver (2-->3)-alpha-sialyltransferase: substrate specificity studies and application of the immobilized enzyme to the synthesis of various sialylated oligosaccharide sequences.

In search of substrate analogues for the porcine liver beta-D-Gal p-(1-->3)-D-Gal p-NAc: CMP-Neu5Ac-(2-->3')-alpha-sialyltransferase, three disaccharides beta-D-Gal p-(1-->3)-beta-D-Gal p-O-CH3 (5), beta-D-Gal p-(1-->3)-beta-D-(2-OAc)-Gal p-O-CH3 (7) and beta-D-Gal p-(1-->3)-beta-D-(2-OAc)-Gal p-O-Bn (11) were synthesized and tested with the enzyme. Disaccharide 7 turned out to be a very good substrate allowing a rapid access to the trisaccharide alpha-Neu5Ac-(2-->3)-beta-D-Gal p-(1-->3)-beta-D-(2-OAc)-Gal p-O-CH3 (13) on a preparative scale using the crude enzyme immobilized on cationic exchanger. Trisaccharide 13 was further exploited, first as a sialyl donor in Trypanosoma cruzi trans-sialidase catalyzed reaction and second through acetolysis reaction as a source for the synthon alpha-Neu5Ac-(2-->3)-D-Gal (16).

Comparative localized linear accuracy of small-field cone-beam CT and multislice CT for alveolar bone measurements.

OBJECTIVES: To compare the accuracy of cone-beam computerized tomography (CBCT) and multislice CT (MSCT) for linear jaw bone measurements. STUDY DESIGN: An ex vivo formalin-fixed human maxilla was imaged with both CBCT (Accuitomo 3D; Morita, Kyoto, Japan) and MSCT (4-slice Somatom VolumeZoom and 16-slice Somatom Sensation 16; Siemens, Erlangen, Germany). The MSCT images were reconstructed using different reconstruction filters to optimize bone visualization (U70u and U90u for VolumeZoom, H30s and H60s for Sensation 16). Before scanning, triplets of small gutta-percha markers were glued onto the soft tissues overlying the maxillary bone on the top and on both sides of the alveolar ridge to define a set of reproducible linear measurements in 11 planes. Image measurements were performed by 2 observers. The gold standard was determined by means of physical measurements with a caliper by 3 observers. RESULTS: The accuracy of the linear measurements was 0.35 +/- 1.31 mm (U70u) and 0.06 +/- 1.23 mm (U90u) for the Somatom VolumeZoom, 0.24 +/- 1.20 mm (H60s) and 0.54 +/- 1.14 mm (H30s) for the Sensation 16, and -0.09 +/- 1.64 mm for the Accuitomo 3D. Statistical analysis with 2-way analysis of variance showed no significant inter- or intraobserver disagreement for the physical or the radiologic measurements. There was also no significant difference for the measurements on the different reconstruction filters. CONCLUSION: Both CBCT and MSCT yield submillimeter accuracy for linear measurements on an ex vivo specimen.