Purpose: To assess the diagnostic accuracy of ChatGPT-4V in interpreting a set of four chest CT slices for each case of COVID-19, non-small cell lung cancer (NSCLC), and control cases, thereby evaluating its potential as an AI tool in radiological diagnostics. Materials and methods: In this retrospective study, 60 CT scans from The Cancer Imaging Archive, covering COVID-19, NSCLC, and control cases were analyzed using ChatGPT-4V. A radiologist selected four CT slices from each scan for evaluation. ChatGPT-4V’s interpretations were compared against the gold standard diagnoses and assessed by two radiologists. Statistical analyses focused on accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), along with an examination of the impact of pathology location and lobe involvement. Results: ChatGPT-4V showed an overall diagnostic accuracy of 56.76%. For NSCLC, sensitivity was 27.27% and specificity was 60.47%. In COVID-19 detection, sensitivity was 13.64% and specificity of 64.29%. For control cases, the sensitivity was 31.82%, with a specificity of 95.24%. The highest sensitivity (83.33%) was observed in cases involving all lung lobes. The chi-squared statistical analysis indicated significant differences in Sensitivity across categories and in relation to the location and lobar involvement of pathologies. Conclusion: ChatGPT-4V demonstrated variable diagnostic performance in chest CT interpretation, with notable proficiency in specific scenarios. This underscores the challenges of cross-modal AI models like ChatGPT-4V in radiology, pointing toward significant areas for improvement to ensure dependability. The study emphasizes the importance of enhancing these models for broader, more reliable medical use.

The purpose of this study was to compare a conventional T1-weighted volumetric interpolated breath-hold examination (VIBE) sequence with a DL-reconstructed accelerated high-resolution VIBE sequence (HR-VIBE_DL) in terms of image quality, lesion conspicuity, and lesion detection. Consecutive patients referred for upper abdominal MRI between December 2023 and March 2024 at a single tertiary center were prospectively enrolled. Participants underwent 1.5 T upper abdominal MRI with acquisition of spectrally fat-saturated unenhanced and gadobutrol-enhanced conventional VIBE (fourfold acceleration, 3.0 mm slice thickness, 72 axial slices) and HR-VIBE_DL (sixfold acceleration, 2.0 mm, 108 slices). Both sequences had an identical acquisition time of 16 s. Image analysis was performed by three readers in a blinded and randomized fashion, with respect to image quality, lesion conspicuity, and lesion detection in liver, pancreas, spleen, lymph nodes and adrenal glands. Image quality parameters were compared using repeated measures analysis of variance. Lesion detection rates were compared using Fisher exact test. Inter-reader agreement was assessed using Fleiss κ test. Among 744 consecutive patients, 50 participants were evaluated. There were 30 men and 20 women, with a mean age of 60 ± 15 (standard deviation [SD]) years (age range: 18-88 years). HR-VIBE_DL images demonstrated superior signal-to-noise ration and edge sharpness by comparison with conventional VIBE images (P < 0.001 for both), with substantial interreader agreement (κ: 0.70-0.90). Lesion conspicuity was higher with for HR-VIBE_DL images (3.50 ± 0.83 [SD]) by comparison with conventional VIBE images (3.21 ± 0.98 [SD]) (P = 0.005). There were 171 upper abdominal lesions, yielding a total of 513 for all three readers. HR-VIBE_DL images yielded higher lesion detection rate (97.5 %; 500/513) compared to conventional VIBE images (93.2 %; 478/513) (P = 0.002). HR-VIBE_DL images of the upper abdomen result in superior image quality, better lesion conspicuity, and improved lesion detection without time penalty by comparsion with conventional VIBE images.

Photon Counting CT (PCCT) offers advanced imaging capabilities with potential for substantial radiation dose reduction; however, achieving this without compromising image quality remains a challenge due to increased noise at lower doses. This study aims to evaluate the effectiveness of a deep learning (DL)-based denoising algorithm in maintaining diagnostic image quality in whole-body PCCT imaging at reduced radiation levels, using real intraindividual cadaveric scans. Twenty-four cadaveric human bodies underwent whole-body CT scans on a PCCT scanner (NAEOTOM Alpha, Siemens Healthineers) at four different dose levels (100%, 50%, 25%, and 10% mAs). Each scan was reconstructed using both ADMIRE level 2 and a DL algorithm (ClariCT.AI, ClariPi Inc.), resulting in 192 datasets. Objective image quality was assessed by measuring CT value stability, image noise, and contrast-to-noise ratio (CNR) across consistent regions of interest (ROIs) in the liver parenchyma. Two radiologists independently evaluated subjective image quality based on overall image clarity, sharpness, and contrast. Inter-rater agreement was determined using Spearman's correlation coefficient, and statistical analysis included mixed-effects modeling to assess objective and subjective image quality. Objective analysis showed that the DL denoising algorithm did not significantly alter CT values (p ≥ 0.9975). Noise levels were consistently lower in denoised datasets compared to the Original (p < 0.0001). No significant differences were observed between the 25% mAs denoised and the 100% mAs original datasets in terms of noise and CNR (p ≥ 0.7870). Subjective analysis revealed strong inter-rater agreement (r ≥ 0.78), with the 50% mAs denoised datasets rated superior to the 100% mAs original datasets (p < 0.0001) and no significant differences detected between the 25% mAs denoised and 100% mAs original datasets (p ≥ 0.9436). The DL denoising algorithm maintains image quality in PCCT imaging while enabling up to a 75% reduction in radiation dose. This approach offers a promising method for reducing radiation exposure in clinical PCCT without compromising diagnostic quality.

In the present case report, a 50-year-old female presented with hemiparesis and blurred vision and was subsequently diagnosed with posterior reversible encephalopathy syndrome (PRES) associated with coronavirus disease 2019 (COVID-19). Magnetic resonance imaging revealed cortico-subcortical edema with hyperintensities bilaterally in the frontoparietal and bi-occipital regions. Although PRES is a neurotoxic disorder that typically affects white matter of the brain and often is associated with hypertension, renal failure, and autoimmune disorders, recent studies have suggested that COVID-19 increases the risk of PRES. This case report presents a unique instance of COVID-19–related PRES. Unlike most previously reported cases occurring during the acute phase of severe COVID-19, our patient experienced PRES during the recovery phase with mild initial symptoms, such as fatigue and mild fever. The article discusses the pathophysiology of PRES, the potential mechanisms by which COVID-19 leads to PRES, and the treatment and outcome of the patient.

Purpose: This study evaluates a deep learning-based denoising algorithm to improve the trade-off between radiation dose, image noise, and motion artifacts in TIPSS procedures, aiming for shorter acquisition times and reduced radiation with maintained diagnostic quality. Methods: In this retrospective study, TIPSS patients were divided based on CBCT acquisition times of 6 s and 3 s. Traditional weighted filtered back projection (Original) and an AI denoising algorithm (AID) were used for image reconstructions. Objective assessments of image quality included contrast, noise levels, and contrast-to-noise ratios (CNRs) through place-consistent region-of-interest (ROI) measurements across various critical areas pertinent to the TIPSS procedure. Subjective assessments were conducted by two blinded radiologists who evaluated the overall image quality, sharpness, contrast, and motion artifacts for each dataset combination. Statistical significance was determined using a mixed-effects model (p ≤ 0.05). Results: From an initial cohort of 60 TIPSS patients, 44 were selected and paired. The mean dose-area product (DAP) for the 6 s acquisitions was 5138.50 ± 1325.57 µGy·m², significantly higher than the 2514.06 ± 691.59 µGym² obtained for the 3 s series. CNR was highest in the 6 s-AID series (p < 0.05). Both denoised and original series showed consistent contrast for 6 s and 3 s acquisitions, with no significant noise differences between the 6 s Original and 3 s AID images (p > 0.9). Subjective assessments indicated superior quality in 6 s-AID images, with no significant overall quality difference between the 6 s-Original and 3 s-AID series (p > 0.9). Conclusions: The AI denoising algorithm enhances CBCT image quality in TIPSS procedures, allowing for shorter scans that reduce radiation exposure and minimize motion artifacts.