Neuroradiology最新文献

Purpose: To determine a practical virtual monoenergetic imaging (VMI) energy range that improves in‑stent lumen assessment and reduces metal‑related artifacts on dual‑energy CT venography (DECT‑CTV) after intracranial venous sinus stenting.

Methods: Retrospective single‑center study including 10 patients (13 stents) who underwent DECT‑CTV after venous sinus stenting for idiopathic intracranial hypertension or pulsatile tinnitus. VMI series were reconstructed from 40 to 140 keV in 10‑keV increments. Two neuroradiologists independently scored in‑stent lumen visibility and beam‑hardening/streak artifacts (5‑point Likert). Quantitative analysis used standardized ROI measurements (3 in‑stent and 2 adjacent out‑of‑stent ROIs per stent per keV) and derived metrics including in‑stent attenuation, background noise, CNR, SNR, and dCT_in-out (in‑stent minus out‑of‑stent attenuation).

Results: Qualitatively, both readers preferred intermediate‑high energies (90-100 keV) for overall interpretability. Quantitatively, in‑stent attenuation decreased from 1180.7 ± 245.5 HU (40 keV) to 112.5 ± 15.7 HU (140 keV). Noise and dCT_in-out decreased with increasing energy, whereas CNR decreased with increasing energy. SNR showed no statistically significant differences across energies.

Conclusion: For intracranial venous sinus stents on DECT‑CTV, VMI around 90-100 keV provided the best perceived balance between lumen accessibility and artifact suppression. Findings require validation in larger cohorts with device‑specific subgroup analyses and complementary artifact indices.

Purpose: The Alberta Stroke Program Early CT Score (ASPECTS) and advances in CT reconstruction play important roles in the neurodiagnostic workflow. This study examines the effect of these reconstruction techniques on automated ASPECTS.

Methods: In this retrospective study, 173 patients (median age 79 years, 39% female) with suspected middle cerebral artery infarction underwent non-contrast CT scans reconstructed with Filtered Back Projection (FBP), ASIR-V (30% and 60%), and DLIR (low, medium, and high). Automated ASPECTS were analyzed, with FBP as the reference standard.

Results: Bland-Altman analysis revealed a mean bias with ASIR and DLIR underestimating ASPECTS compared to FBP. This underestimation was less pronounced for ASIR-V 30% (-0.057 ) and DLIR-L (-0.069) than for ASIR-V 60% (-0.126), DLIR-M (-0.121), and DLIR-H (-0.086). The region with the greatest overestimation relative to FBP was M3 (n = 23), while the region with the greatest underestimation was the insular ribbon (n = 51). Regarding the ASPECTS < 6 threshold, most patients were reclassified from ASPECTS ≤ 5 to ASPECTS ≥ 6 with DLIR-M (n = 5), which also showed the strongest agreement with expert consensus (κ = 0.352).

Conclusion: Both ASIR-V and DLIR resulted in only minor underestimation of ASPECTS compared to FBP. However, most patients became eligible for endovascular therapy due to ASPECTS reclassification with DLIR-M. DLIR-M also exhibited the highest agreement with expert consensus for automated ASPECTS. Therefore, careful selection of reconstruction parameters, as well as further optimization and standardization of these techniques, is essential for broader application in stroke imaging.

Purpose: Neuromelanin-sensitive imaging visualizes degeneration of the substantia nigra pars compacta (SNc) and locus coeruleus (LC), characteristic features of Parkinson's disease (PD). Spectral presaturation with inversion recovery (SPIR), using fat-selective radiofrequency pulses, has been reported to provide superior delineation of the SNc and LC in healthy individuals and offers shorter acquisition times than conventional magnetization transfer (MT) imaging. This study evaluated the clinical utility of SPIR imaging for assessing PD compared with MT imaging.

Methods: Neuromelanin-sensitive images were acquired from 24 patients with PD and 24 healthy controls using MT and SPIR sequences, each with an acquisition time of approximately five minutes. Signal ratios (SRs) of the SNc and LC were automatically quantified using established brain atlases. For each sequence and brain region, diagnostic performance in distinguishing PD from controls was assessed using receiver operating characteristic curve analysis. In patients with PD, associations between SRs and nigrostriatal degeneration, as measured by dopamine transporter SPECT imaging, were investigated.

Results: SPIR images yielded higher SRs in the SNc than MT images. Diagnostic accuracy for PD with SPIR imaging (87.50%) was significantly greater than that with MT imaging (77.08%). SRs of the SNc and LC on SPIR images were correlated with nigrostriatal degeneration on dopamine transporter SPECT, unlike MT images.

Conclusion: SPIR imaging demonstrated superior visualization of the SNc and LC, and outperformed MT imaging in the evaluation of PD. With shorter acquisition time and stronger correlation with nigrostriatal degeneration, SPIR represents a promising and practical tool for diagnosing and monitoring PD.