Low temperature deposition of 2D WS2 layers from WF6 and H2S precursors: impact of reducing agents

We demonstrate the impact of reducing agents for Chemical Vapor Deposition (CVD) and Atomic Layer Deposition (ALD) of WS₂ from WF₆ and H₂S precursors.

Diagnostic performance and image quality of deep learning image reconstruction (DLIR) on unenhanced low-dose abdominal CT for urolithiasis

BACKGROUND

Patients with urolithiasis undergo radiation overexposure from computed tomography (CT) scans. Improvement of image reconstruction is necessary for radiation dose reduction.

PURPOSE

To evaluate a deep learning-based reconstruction algorithm for CT (DLIR) in the detection of urolithiasis at low-dose non-enhanced abdominopelvic CT.

MATERIAL AND METHODS

A total of 75 patients who underwent low-dose abdominopelvic CT for urolithiasis were retrospectively included. Each examination included three reconstructions: DLIR; filtered back projection (FBP); and hybrid iterative reconstruction (IR; ASiR-V 70%). Image quality was subjectively and objectively assessed using attenuation and noise measurements in order to calculate the signal-to-noise ratio (SNR), absolute contrast, and contrast-to-noise ratio (CNR). Attenuation of the largest stones were also compared. Detectability of urinary stones was assessed by two observers.

RESULTS

Image noise was significantly reduced with DLIR: 7.2 versus 17 and 22 for ASiR-V 70% and FBP, respectively. Similarly, SNR and CNR were also higher compared to the standard reconstructions. When the structures had close attenuation values, contrast was lower with DLIR compared to ASiR-V. Attenuation of stones was also lowered in the DLIR series. Subjective image quality was significantly higher with DLIR. The detectability of all stones and stones >3 mm was excellent with DLIR for the two observers (intraclass correlation [ICC] = 0.93 vs. 0.96 and 0.95 vs. 0.99). For smaller stones (<3 mm), results were different (ICC = 0.77 vs. 0.86).

CONCLUSION

For low-dose abdominopelvic CT, DLIR reconstruction exhibited image quality superior to ASiR-V and FBP as well as an excellent detection of urinary stones.

[Relationships between piriformis muscle and sciatic nerve: radioanatomical study with 104 buttocks]

OBJECTIVE

The relationships between the piriformis muscle and the sciatic nerve are close and may be changing. In some cases, these relationships are the cause of a piriformis syndrome, an under-diagnosed etiology of non discal sciatica. The aim of the study was to explore, by the MRI, the sciatic nerve and the piriformis muscle. MATERIELS AND METHODS: One hundred and four buttocks were explored by MRI for 52 randomly selected patients.

RESULTS

In 59.6% of cases, no variation of the piriformis muscle and sciatic nerve were found. In 26% of cases, the sciatic nerve was divided into the pelvis. In 9.6% of cases, it was divided into the pelvis, the piriformis muscle was bifid and the common peroneal nerve ran between both heads. The piriformis muscle was bifid in 10.6% of cases, hypertrophic in 13.5% of cases and atrophic in 2.9% of cases.

On the Nature of Weak Spots in High-k Layers Submitted to Anneals

In the path to the introduction of high-k dielectric into IC components, a large number of challenges have still to be solved. Some of the major issues concern the low mobility of carriers and the reliability of the devices. Trapped charges in the stack have been identified as being the cause of these issues. With this in mind, we used Conducting Atomic Force Microscopy, combined with physical analysis to understand the nature of these charges. In this contribution, we have studied the uniformity of thin HfO2 layers, with and without anneal. The Conducting Atomic Force microscopy measurements show spots of higher conductivity. Recording local IV's in those ‘weak’ spots suggests that they consist of positive charge. On the other hand, XPS and ToFSIMS analysis show a diffusion of the interfacial SiO2 upwards into the high-k layer. Finally, the comparison of samples with differing high-k material and crystallinity indicates a strong correlation between the weak spots and the presence of silicon in the film.

Detection of urinary tract stones on submillisievert abdominopelvic CT imaging with deep-learning image reconstruction algorithm (DLIR)

PURPOSE

Urolithiasis is a chronic condition that leads to repeated CT scans throughout the patient's life. The goal was to assess the diagnostic performance and image quality of submillisievert abdominopelvic computed tomography (CT) using deep learning-based image reconstruction (DLIR) in urolithiasis.

METHODS

57 patients with suspected urolithiasis underwent both non-contrast low-dose (LD) and ULD abdominopelvic CT. Raw image data of ULD CT were reconstructed using hybrid iterative reconstruction (ASIR-V 70%) and high-strength-level DLIR (DLIR-H). The performance of ULD CT for the detection of urinary stones was assessed by two readers and compared with LD CT with ASIR-V 70% as a reference standard. Image quality was assessed subjectively and objectively.

RESULTS

266 stones were detected in 38 patients. Mean effective dose was 0.59 mSv for ULD CT and 1.96 mSv for LD CT. For diagnostic performance, sensitivity and specificity were 89% and 94%, respectively, for ULDCT with DLIR-H. There was an almost perfect intra-observer concordance on ULD CT with DLIR-H versus LDCT with ASIR-V 70% (ICC = 0.90 and 0.90 for the two readers). Image noise was significantly lower and signal-to-noise ratio significantly higher with DLIR-H compared to ASIR-V 70%. Subjective image quality was also significantly better with ULDCT with DLIR-H.

CONCLUSION

ULD CT with Deep Learning Image Reconstruction maintains a good diagnostic performance in urolithiasis, with better image quality than hybrid iterative reconstruction and a significant radiation dose reduction.