scholarly journals Low-count whole-body PET with deep learning in a multicenter and externally validated study

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Akshay S. Chaudhari ◽  
Erik Mittra ◽  
Guido A. Davidzon ◽  
Praveen Gulaka ◽  
Harsh Gandhi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Pet Imaging ◽  
External Validation ◽  
Lesion Detection ◽  
Whole Body ◽  
Diagnostic Image Quality ◽  
Diagnostic Image ◽  
Scan Time ◽  
Kappa Agreement

AbstractMore widespread use of positron emission tomography (PET) imaging is limited by its high cost and radiation dose. Reductions in PET scan time or radiotracer dosage typically degrade diagnostic image quality (DIQ). Deep-learning-based reconstruction may improve DIQ, but such methods have not been clinically evaluated in a realistic multicenter, multivendor environment. In this study, we evaluated the performance and generalizability of a deep-learning-based image-quality enhancement algorithm applied to fourfold reduced-count whole-body PET in a realistic clinical oncologic imaging environment with multiple blinded readers, institutions, and scanner types. We demonstrate that the low-count-enhanced scans were noninferior to the standard scans in DIQ (p < 0.05) and overall diagnostic confidence (p < 0.001) independent of the underlying PET scanner used. Lesion detection for the low-count-enhanced scans had a high patient-level sensitivity of 0.94 (0.83–0.99) and specificity of 0.98 (0.95–0.99). Interscan kappa agreement of 0.85 was comparable to intrareader (0.88) and pairwise inter-reader agreements (maximum of 0.72). SUV quantification was comparable in the reference regions and lesions (lowest p-value=0.59) and had high correlation (lowest CCC = 0.94). Thus, we demonstrated that deep learning can be used to restore diagnostic image quality and maintain SUV accuracy for fourfold reduced-count PET scans, with interscan variations in lesion depiction, lower than intra- and interreader variations. This method generalized to an external validation set of clinical patients from multiple institutions and scanner types. Overall, this method may enable either dose or exam-duration reduction, increasing safety and lowering the cost of PET imaging.

Low-dose whole-body CT using deep learning image reconstruction: image quality and lesion detection

2021 ◽  
Vol 94 (1121) ◽  
pp. 20201329
Author(s):  
Yoshifumi Noda ◽  
Tetsuro Kaga ◽  
Nobuyuki Kawai ◽  
Toshiharu Miyoshi ◽  
Hiroshi Kawada ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Low Dose ◽  
Standard Dose ◽  
Lesion Detection ◽  
Whole Body ◽  
Hybrid Iterative Reconstruction ◽  
Whole Body Ct ◽  
Attenuation Values

Objectives: To evaluate image quality and lesion detection capabilities of low-dose (LD) portal venous phase whole-body computed tomography (CT) using deep learning image reconstruction (DLIR). Methods: The study cohort of 59 consecutive patients (mean age, 67.2 years) who underwent whole-body LD CT and a prior standard-dose (SD) CT reconstructed with hybrid iterative reconstruction (SD-IR) within one year for surveillance of malignancy were assessed. The LD CT images were reconstructed with hybrid iterative reconstruction of 40% (LD-IR) and DLIR (LD-DLIR). The radiologists independently evaluated image quality (5-point scale) and lesion detection. Attenuation values in Hounsfield units (HU) of the liver, pancreas, spleen, abdominal aorta, and portal vein; the background noise and signal-to-noise ratio (SNR) of the liver, pancreas, and spleen were calculated. Qualitative and quantitative parameters were compared between the SD-IR, LD-IR, and LD-DLIR images. The CT dose-index volumes (CTDIvol) and dose-length product (DLP) were compared between SD and LD scans. Results: The image quality and lesion detection rate of the LD-DLIR was comparable to the SD-IR. The image quality was significantly better in SD-IR than in LD-IR (p < 0.017). The attenuation values of all anatomical structures were comparable between the SD-IR and LD-DLIR (p = 0.28–0.96). However, background noise was significantly lower in the LD-DLIR (p < 0.001) and resulted in improved SNRs (p < 0.001) compared to the SD-IR and LD-IR images. The mean CTDIvol and DLP were significantly lower in the LD (2.9 mGy and 216.2 mGy•cm) than in the SD (13.5 mGy and 1011.6 mGy•cm) (p < 0.0001). Conclusion: LD CT images reconstructed with DLIR enable radiation dose reduction of >75% while maintaining image quality and lesion detection rate and superior SNR in comparison to SD-IR. Advances in knowledge: Deep learning image reconstruction algorithm enables around 80% reduction in radiation dose while maintaining the image quality and lesion detection compared to standard-dose whole-body CT.

scholarly journals MR imaging of the fetal brain at 1.5T and 3.0T field strengths: comparing specific absorption rate (SAR) and image quality

2015 ◽  
Vol 43 (2) ◽  
Author(s):  
Uday Krishnamurthy ◽  
Jaladhar Neelavalli ◽  
Swati Mody ◽  
Lami Yeo ◽  
Pavan K. Jella ◽  
...  
Keyword(s):  
Image Quality ◽  
Fetal Brain ◽  
Whole Body ◽  
Diagnostic Image Quality ◽  
Similar Data ◽  
Diagnostic Image ◽  
Spin Echo Sequence ◽  
Tissue Contrast ◽  
3.0T Mri ◽  
Field Strengths

Abstract: Our two objectives were to evaluate the feasibility of fetal brain magnetic resonance imaging (MRI) using a fast spin echo sequence at 3.0T field strength with low radio frequency (: T2 weighted images of the fetal brain at 1.5T were compared to similar data obtained in the same fetus using a modified sequence at 3.0T. Quantitative whole-body SAR and normalized image signal to noise ratio (SNR), a nominal scoring scheme based evaluation of diagnostic image quality, and tissue contrast and conspicuity for specific anatomical structures in the brain were compared between 1.5T and 3.0T.: Twelve pregnant women underwent both 1.5T and 3.0T MRI examinations. The image SNR was significantly higher (P=0.03) and whole-body SAR was significantly lower (P<0.0001) for images obtained at 3.0T compared to 1.5T. All cases at both field strengths were scored as having diagnostic image quality. Images from 3.0T MRI (compared to 1.5T) were equal (57%; 21/37) or superior (35%; 13/37) for tissue contrast and equal (61%; 20/33) or superior (33%, 11/33) for conspicuity.It is possible to obtain fetal brain images with higher resolution and better SNR at 3.0T with simultaneous reduction in SAR compared to 1.5T. Images of the fetal brain obtained at 3.0T demonstrated superior tissue contrast and conspicuity compared to 1.5T.

Minimizing table time in patients with claustrophobia using focused ferumoxytol-enhanced MR angiography (f-FEMRA): a feasibility study

2021 ◽  
Vol 94 (1125) ◽  
pp. 20210430
Author(s):  
Puja Shahrouki ◽  
Kim-Lien Nguyen ◽  
John M. Moriarty ◽  
Adam N. Plotnik ◽  
Takegawa Yoshida ◽  
...  
Keyword(s):  
Image Quality ◽  
Mr Angiography ◽  
Signal To Noise Ratio ◽  
Diagnostic Image Quality ◽  
Diagnostic Confidence ◽  
Motion Artefact ◽  
Scan Time ◽  
Wilcoxon Rank Sum Test ◽  
Significant Difference ◽  
Noise Ratio

Objectives: To assess the feasibility of a rapid, focused ferumoxytol-enhanced MR angiography (f-FEMRA) protocol in patients with claustrophobia. Methods: In this retrospective study, 13 patients with claustrophobia expressed reluctance to undergo conventional MR angiography, but agreed to a trial of up to 10 min in the scanner bore and underwent f-FEMRA. Thirteen matched control patients who underwent gadolinium-enhanced MR angiography (GEMRA) were identified for comparison of diagnostic image quality. For f-FEMRA, the time from localizer image acquisition to completion of the angiographic acquisition was measured. Two radiologists independently scored images on both f-FEMRA and GEMRA for arterial and venous image quality, motion artefact and diagnostic confidence using a 5-point scale, five being best. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in the aorta and IVC were measured. The Wilcoxon rank-sum test, one-way ANOVA with Tukey correction and two-tailed t tests were utilized for statistical analyses. Results: All scans were diagnostic and assessed with high confidence (scores ≥ 4). Average scan time for f-FEMRA was 6.27 min (range 3.56 to 10.12 min), with no significant difference between f-FEMRA and GEMRA in diagnostic confidence (4.86 ± 0.24 vs 4.69 ± 0.25, p = 0.13), arterial image quality (4.62 ± 0.57 vs 4.65 ± 0.49, p = 0.78) and motion artefact score (4.58 ± 0.49 vs 4.58 ± 0.28, p > 0.99). f-FEMRA scored significantly better for venous image quality than GEMRA (4.62 ± 0.42 vs 4.19 ± 0.56, p = 0.04). CNR in the IVC was significantly higher for steady-state f-FEMRA than GEMRA regardless of the enhancement phase (p < 0.05). Conclusions: Comprehensive vascular MR imaging of the thorax, abdomen and pelvis can be completed in as little as 5 min within the magnet bore using f-FEMRA, facilitating acceptance by patients with claustrophobia and streamlining workflow. Advances in knowledge: A focused approach to vascular imaging with ferumoxytol can be performed in patients with claustrophobia, limiting time in the magnet bore to 10 min or less, while acquiring fully diagnostic images of the thorax, abdomen and pelvis.

scholarly journals Enhancing Patient Experience With Internet Protocol Addressable Digital LED Lighting in Imaging Environments: A Phase I Study (Preprint)

2018 ◽  
Author(s):  
Melanie U Knopp ◽  
Katherine Binzel ◽  
Chadwick L Wright ◽  
Jun Zhang ◽  
Michael V Knopp
Keyword(s):  
Image Quality ◽  
Patient Experience ◽  
Patient Comfort ◽  
Patient Specific ◽  
Diagnostic Image Quality ◽  
Imaging Studies ◽  
Diagnostic Image ◽  
Clinical Imaging ◽  
Ambient Lighting ◽  
Led Lights

BACKGROUND Conventional approaches to improve the quality of clinical patient imaging studies focus predominantly on updating or replacing imaging equipment; however, it is often not considered that patients can also highly influence the diagnostic quality of clinical imaging studies. Patient-specific artifacts can limit the diagnostic image quality, especially when patients are uncomfortable, anxious, or agitated. Imaging facility or environmental conditions can also influence the patient’s comfort and willingness to participate in diagnostic imaging studies, especially when performed in visually unesthetic, anxiety-inducing, and technology-intensive imaging centers. When given the opportunity to change a single aspect of the environmental or imaging facility experience, patients feel much more in control of the otherwise unfamiliar and uncomfortable setting. Incorporating commercial, easily adaptable, ambient lighting products within clinical imaging environments allows patients to individually customize their environment for a more personalized and comfortable experience. OBJECTIVE The aim of this pilot study was to use a customizable colored light-emitting diode (LED) lighting system within a clinical imaging environment and demonstrate the feasibility and initial findings of enabling healthy subjects to customize the ambient lighting and color. Improving the patient experience within clinical imaging environments with patient-preferred ambient lighting and color may improve overall patient comfort, compliance, and participation in the imaging study and indirectly contribute to improving diagnostic image quality. METHODS We installed consumer-based internet protocol addressable LED lights using the ZigBee standard in different PET/CT scan rooms within a clinical imaging environment. We recruited healthy volunteers (n=35) to generate pilot data in order to develop a subsequent clinical trial. The visual perception assessment procedure utilized questionnaires with preprogrammed light/color settings and further assessed how subjects preferred ambient light and color within a clinical imaging setting. RESULTS Technical implementation using programmable LED lights was performed without any hardware or electrical modifications to the existing clinical imaging environment. Subject testing revealed substantial variabilities in color perception; however, clear trends in subject color preference were noted. In terms of the color hue of the imaging environment, 43% (15/35) found blue and 31% (11/35) found yellow to be the most relaxing. Conversely, 69% (24/35) found red, 17% (6/35) found yellow, and 11% (4/35) found green to be the least relaxing. CONCLUSIONS With the majority of subjects indicating that colored lighting within a clinical imaging environment would contribute to an improved patient experience, we predict that enabling patients to customize environmental factors like lighting and color to individual preferences will improve patient comfort and patient satisfaction. Improved patient comfort in clinical imaging environments may also help to minimize patient-specific imaging artifacts that can otherwise limit diagnostic image quality. CLINICALTRIAL ClinicalTrials.gov NCT03456895; https://clinicaltrials.gov/ct2/show/NCT03456895

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