UNASSIGNED: To evaluate the feasibility of synthetic MRI for quantitative and morphologic assessment of head and neck tumors and compare the results with the conventional MRI approach.
UNASSIGNED: A total of 92 patients with different head and neck tumor histology who underwent conventional and synthetic MRI were retrospectively recruited. The quantitative T1, T2, proton density (PD), and apparent diffusion coefficient (ADC) values of 38 benign and 54 malignant tumors were measured and compared. Diagnostic efficacy for differentiating malignant and benign tumors was evaluated with receiver operating characteristic (ROC) analysis and integrated discrimination index. The image quality of conventional and synthetic T1W/T2W images on a 5-level Likert scale was also compared with Wilcoxon signed rank test.
UNASSIGNED: T1, T2 and ADC values of malignant head and neck tumors were smaller than those of benign tumors (all p < 0.05). T2 and ADC values showed better diagnostic efficacy than T1 for distinguishing malignant tumors from benign tumors (both p < 0.05). Adding the T2 value to ADC increased the area under the curve from 0.839 to 0.886, with an integrated discrimination index of 4.28% (p < 0.05). In terms of overall image quality, synthetic T2W images were comparable to conventional T2W images, while synthetic T1W images were inferior to conventional T1W images.
UNASSIGNED: Synthetic MRI can facilitate the characterization of head and neck tumors by providing quantitative relaxation parameters and synthetic T2W images. T2 values added to ADC values may further improve the differentiation of tumors.

Computed tomography (CT) is preferred for evaluating solitary pulmonary nodules (SPNs) but access or availability may be lacking, in addition, overlapping anatomy can hinder detection of SPNs on chest radiographs. We developed and evaluated the clinical feasibility of a deep learning algorithm to generate digitally reconstructed tomography (DRT) images of the chest from digitally reconstructed frontal and lateral radiographs (DRRs) and use them to detect SPNs.
This single-institution retrospective study included 637 patients with noncontrast helical CT of the chest (mean age 68 years, median age 69 years, standard deviation 11.7 years; 355 women) between 11/2012 and 12/2020, with SPNs measuring 10-30 mm. A deep learning model was trained on 562 patients, validated on 60 patients, and tested on the remaining 15 patients. Diagnostic performance (SPN detection) from planar radiography (DRRs and CT scanograms, PR) alone or with DRT was evaluated by two radiologists in an independent blinded fashion. The quality of the DRT SPN image in terms of nodule size and location, morphology, and opacity was also evaluated, and compared to the ground-truth CT images RESULTS: Diagnostic performance was higher from DRT plus PR than from PR alone (area under the receiver operating characteristic curve 0.95-0.98 versus 0.80-0.85; p < 0.05). DRT plus PR enabled diagnosis of SPNs in 11 more patients than PR alone. Interobserver agreement was 0.82 for DRT plus PR and 0.89 for PR alone; and interobserver agreement for size and location, morphology, and opacity of the DRT SPN was 0.94, 0.68, and 0.38, respectively.
For SPN detection, DRT plus PR showed better diagnostic performance than PR alone. Deep learning can be used to generate DRT images and improve detection of SPNs.

OBJECTIVE: To compare the imaging quality, T stage and extramural venous invasion (EMVI) evaluation between the conventional and synthetic T2-weighted imaging (T2WI), and to investigate the role of quantitative values obtained from synthetic magnetic resonance imaging (MRI) for assessing nodal staging in rectal cancer (RC).
METHODS: Ninety-four patients with pathologically proven RC who underwent rectal MRI examinations including synthetic MRI were retrospectively recruited. The image quality of conventional and synthetic T2WI was compared regarding signal-to-noise ratio (SNR), contrast-to-noise (CNR), sharpness of the lesion edge, lesion conspicuity, absence of motion artifacts, and overall image quality. The accuracy of T stage and EMVI evaluation on conventional and synthetic T2WI were compared using the Mc-Nemar test. The quantitative T1, T2, and PD values were used to predict the nodal staging of MRI-evaluated node-negative RC.
RESULTS: There were no statistically significant differences between conventional and synthetic T2WI in SNR, CNR, overall image quality, lesion conspicuity, and absence of motion artifacts (p = 0.058-0.978). There were no significant differences in the diagnostic accuracy of T stage and EMVI between conventional and synthetic T2WI from two observers (p = 0.375 and 0.625 for T stage; p = 0.625 and 0.219 for EMVI). The T2 value showed good diagnostic performance for predicting the nodal staging of RC with the area under the receiver operating characteristic, sensitivity, specificity, and accuracy of 0.854, 90.0%, 71.4%, and 80.3%, respectively.
CONCLUSIONS: Synthetic MRI may facilitate preoperative staging and EMVI evaluation of RC by providing synthetic T2WI and quantitative maps in one acquisition.

For an accurate dynamic contrast-enhanced (DCE) MRI analysis, exact baseline T1 mapping is critical. The purpose of this study was to compare the pharmacokinetic parameters of DCE MRI using synthetic MRI with those using fixed baseline T1 values.
This retrospective study included 102 patients who underwent both DCE and synthetic brain MRI. Two methods were set for the baseline T1: one using the fixed value and the other using the T1 map from synthetic MRI. The volume transfer constant (Ktrans), volume of the vascular plasma space (vp), and the volume of the extravascular extracellular space (ve) were compared between the two methods. The interclass correlation coefficients and the Bland-Altman method were used to assess the reliability.
In normal-appearing frontal white matter (WM), the mean values of Ktrans, ve, and vp were significantly higher in the fixed value method than in the T1 map method. In the normal-appearing occipital WM, the mean values of ve and vp were significantly higher in the fixed value method. In the putamen and head of the caudate nucleus, the mean values of Ktrans, ve, and vp were significantly lower in the fixed value method. In addition, the T1 map method showed comparable interobserver agreements with the fixed baseline T1 value method.
The T1 map method using synthetic MRI may be useful for reflecting individual differences and reliable measurements in clinical applications of DCE MRI.