scholarly journals Impact of deep learning reconstruction on intracranial 1.5 T magnetic resonance angiography

2021 ◽  
Author(s):  
Koichiro Yasaka ◽  
Hiroyuki Akai ◽  
Haruto Sugawara ◽  
Taku Tajima ◽  
Masaaki Akahane ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Image Quality ◽  
Magnetic Resonance Angiography ◽  
Basilar Artery ◽  
Signal To Noise Ratio ◽  
Superior Cerebellar Artery ◽  
Prepontine Cistern ◽  
Noise Ratio

Abstract Purpose The purpose of this study was to evaluate whether deep learning reconstruction (DLR) improves the image quality of intracranial magnetic resonance angiography (MRA) at 1.5 T. Materials and methods In this retrospective study, MRA images of 40 patients (21 males and 19 females; mean age, 65.8 ± 13.2 years) were reconstructed with and without the DLR technique (DLR image and non-DLR image, respectively). Quantitative image analysis was performed by placing regions of interest on the basilar artery and cerebrospinal fluid in the prepontine cistern. We calculated the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for analyses of the basilar artery. Two experienced radiologists evaluated the depiction of structures (the right internal carotid artery, right ophthalmic artery, basilar artery, and right superior cerebellar artery), artifacts, subjective noise and overall image quality in a qualitative image analysis. Scores were compared in the quantitative and qualitative image analyses between the DLR and non-DLR images using Wilcoxon signed-rank tests. Results The SNR and CNR for the basilar artery were significantly higher for the DLR images than for the non-DLR images (p < 0.001). Qualitative image analysis scores (p < 0.003 and p < 0.005 for readers 1 and 2, respectively), excluding those for artifacts (p = 0.072–0.565), were also significantly higher for the DLR images than for the non-DLR images. Conclusion DLR enables the production of higher quality 1.5 T intracranial MRA images with improved visualization of arteries.

scholarly journals Applicability of deep learning-based reconstruction trained by brain and knee 3T MRI to lumbar 1.5T MRI

2021 ◽  
Vol 10 (6) ◽  
pp. 205846012110239
Author(s):  
Nobuo Kashiwagi ◽  
Hisashi Tanaka ◽  
Yuichi Yamashita ◽  
Hiroto Takahashi ◽  
Yoshimori Kassai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Magnetic Resonance ◽  
Image Quality ◽  
Signal To Noise Ratio ◽  
Magnetic Resonance Images ◽  
Reconstruction Method ◽  
Signal To Noise ◽  
Scanning Time ◽  
Noise Ratio ◽  
Weighted Sequences

Background Several deep learning-based methods have been proposed for addressing the long scanning time of magnetic resonance imaging. Most are trained using brain 3T magnetic resonance images, but is unclear whether performance is affected when applying these methods to different anatomical sites and at different field strengths. Purpose To validate the denoising performance of deep learning-based reconstruction method trained by brain and knee 3T magnetic resonance images when applied to lumbar 1.5T magnetic resonance images. Material and Methods Using a 1.5T scanner, we obtained lumber T2-weighted sequences in 10 volunteers using three different scanning times: 228 s (standard), 119 s (double-fast), and 68 s (triple-fast). We compared the images obtained by the standard sequence with those obtained by the deep learning-based reconstruction-applied faster sequences. Results Signal-to-noise ratio values were significantly higher for deep learning-based reconstruction-double-fast than for standard and did not differ significantly between deep learning-based reconstruction-triple-fast and standard. Contrast-to-noise ratio values also did not differ significantly between deep learning-based reconstruction-triple-fast and standard. Qualitative scores for perceived signal-to-noise ratio and overall image quality were significantly higher for deep learning-based reconstruction-double fast and deep learning-based reconstruction-triple-fast than for standard. Average scores for sharpness, contrast, and structure visibility were equal to or higher for deep learning-based reconstruction-double-fast and deep learning-based reconstruction-triple-fast than for standard, but the differences were not statistically significant. The average scores for artifact were lower for deep learning-based reconstruction-double-fast and deep learning-based reconstruction-triple-fast than for standard, but the differences were not statistically significant. Conclusion The deep learning-based reconstruction method trained by 3T brain and knee images may reduce the scanning time of 1.5T lumbar magnetic resonance images by one-third without sacrificing image quality.

scholarly journals Extremely tortuous superior cerebellar artery mimicking an aneurysm

2021 ◽  
Vol 12 ◽  
pp. 569
Author(s):  
Megumi Matsuda ◽  
Hideki Endo ◽  
Kohei Ishikawa ◽  
Ryota Nomura ◽  
Tomoaki Ishizuka ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Angiography ◽  
Basilar Artery ◽  
Vascular Lesions ◽  
Superior Cerebellar Artery ◽  
Case Description ◽  
Unruptured Cerebral Aneurysm ◽  
Cerebellar Artery ◽  
The Right ◽  
Angiography Catheter

Background: An extremely tortuous superior cerebellar artery is a rare anomaly. We report a case of an extremely tortuous superior cerebellar artery mimicking an aneurysm. Case Description: A 77-year-old woman was initially diagnosed with unruptured cerebral aneurysm at the right basilar artery-superior cerebellar artery junction by magnetic resonance angiography. Catheter angiogram revealed that there was no apparent aneurysm at the basilar artery-superior cerebellar artery junction and the lesion was actually an extremely tortuous superior cerebellar artery. Conclusion: Although an extremely tortuous superior cerebellar artery is rare, it should be considered when examining other vascular lesions.

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

2021 ◽  
pp. 028418512110358
Author(s):  
Aurélien Delabie ◽  
Roger Bouzerar ◽  
Raphaël Pichois ◽  
Xavier Desdoit ◽  
Jérémie Vial ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Low Dose ◽  
Signal To Noise Ratio ◽  
Intraclass Correlation ◽  
Reconstruction Algorithm ◽  
Urinary Stones ◽  
Abdominopelvic Ct ◽  
Noise Ratio

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.

Clinical Experience in Timed Arterial Compression Contrast-enhanced Magnetic Resonance Angiography of the Hand

2010 ◽  
Vol 61 (4) ◽  
pp. 206-216 ◽  
Author(s):  
Andreas Gutzeit ◽  
Boris Eckhardt ◽  
Jan Beranek ◽  
Klaus U. Wentz ◽  
Edwin Willemse ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Angiography ◽  
Spatial Resolution ◽  
Diagnostic Performance ◽  
Signal To Noise Ratio ◽  
Final Diagnosis ◽  
Pressure Cuff ◽  
Contrast Enhanced ◽  
Noise Ratio ◽  
Contrast Bolus

Purpose A retrospective analysis of the diagnostic performance of the timed arterial compression (TAC) technique, which allows freezing of the contrast bolus during first-pass contrast-enhanced (CE) magnetic resonance angiography (MRA) to diagnose vascular pathologies in the hand. Material and Methods A total of 14 consecutive CE-MRAs of the hand were acquired by using the TAC technique. By inflating a blood pressure cuff up to 200 mm Hg triggered to the arterial contrast filling of the hand, prolonged measurement times up to 144 seconds, with a spatial resolution of 0.59 × 0.59 × 0.8 mm3, could be realized. Overall image qualities, arterial signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and vessel conspicuity besides the final diagnosis were evaluated. Results All 14 TAC-CE-MRAs of the hand were successfully accomplished without any adverse events and yielded, in all cases, a final diagnosis with a high total number of vascular pathologies (57). High arterial SNR and CNR values exceeding the soil of 85 and 60, respectively, resulted. Thus, overall vessel visibility (>90%), vessel conspicuity (mean Δ signal intensity [SI]/mm = 1,193) and image quality on a per patient level (>60%) were rated as excellent or good. Conclusions TAC-CE-MRA of the hand offers high diagnostic performance because of its increased spatial resolution while preserving contrast, which allowed detection of tiny stenoses of the digital arteries.

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

ANALISIS SENSITIVITAS VIDEO MPEG-4 BERDASARKAN STRUKTUR FRAME PADA TRANSMISI DVB-T

2020 ◽  
Vol 25 (2) ◽  
pp. 86-97
Author(s):  
Sandy Suryo Prayogo ◽  
Tubagus Maulana Kusuma
Keyword(s):  
Deep Learning ◽  
Bit Error Rate ◽  
Error Rate ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Signal To Noise ◽  
Structural Similarity Index ◽  
Noise Ratio

DVB merupakan standar transmisi televisi digital yang paling banyak digunakan saat ini. Unsur terpenting dari suatu proses transmisi adalah kualitas gambar dari video yang diterima setelah melalui proses transimisi tersebut. Banyak faktor yang dapat mempengaruhi kualitas dari suatu gambar, salah satunya adalah struktur frame dari video. Pada tulisan ini dilakukan pengujian sensitifitas video MPEG-4 berdasarkan struktur frame pada transmisi DVB-T. Pengujian dilakukan menggunakan simulasi matlab dan simulink. Digunakan juga ffmpeg untuk menyediakan format dan pengaturan video akan disimulasikan. Variabel yang diubah dari video adalah bitrate dan juga group-of-pictures (GOP), sedangkan variabel yang diubah dari transmisi DVB-T adalah signal-to-noise-ratio (SNR) pada kanal AWGN di antara pengirim (Tx) dan penerima (Rx). Hasil yang diperoleh dari percobaan berupa kualitas rata-rata gambar pada video yang diukur menggunakan metode pengukuran structural-similarity-index (SSIM). Dilakukan juga pengukuran terhadap jumlah bit-error-rate BER pada bitstream DVB-T. Percobaan yang dilakukan dapat menunjukkan seberapa besar sensitifitas bitrate dan GOP dari video pada transmisi DVB-T dengan kesimpulan semakin besar bitrate maka akan semakin buruk nilai kualitas gambarnya, dan semakin kecil nilai GOP maka akan semakin baik nilai kualitasnya. Penilitian diharapkan dapat dikembangkan menggunakan deep learning untuk memperoleh frame struktur yang tepat di kondisi-kondisi tertentu dalam proses transmisi televisi digital.

Frame-Level Signal-to-Noise Ratio Estimation Using Deep Learning

2020 ◽  
Author(s):  
Hao Li ◽  
DeLiang Wang ◽  
Xueliang Zhang ◽  
Guanglai Gao
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
Ratio Estimation ◽  
Signal To Noise ◽  
Noise Ratio
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