Prospective Deployment of Deep Learning in <scp>MRI</scp>: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices

Chaudhari, Akshay; Sandino, Christopher M.; Cole, Elizabeth K.; Larson, David B.; Gold, Garry E.; Vasanawala, Shreyas S.; Lungren, Matthew P.; Hargreaves, Brian A.; Langlotz, Curtis P.

doi:10.1002/jmri.27331

Cited by 61 publications

(45 citation statements)

References 92 publications

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“…Maintaining comparable SUV quantitation between the low-count-enhanced and standard PET images is important to assess tumor avidity and response to therapy. Coupled with the high concordance in the depiction of lesions and the SUV quantification between the standard and low-count-enhanced scans, the proposed method maintained pixelwise accuracy and imagewise quality, two criteria important for prospective image-acquisition studies 34 . Overall, this shows that the proposed method can be used for accurate staging and prognostication.…”

Section: Discussionmentioning

confidence: 89%

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

et al. 2021

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More 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.

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Section: Discussionmentioning

confidence: 89%

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

et al. 2021

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“…The novelty of this work compared with previous studies of rapid imaging protocols is the simultaneous acquisition of anatomic and quantitative imaging along with the incorporation of quantitative T2 mapping into the MRI interpretation. Additionally, our study presents a prospective implementation of deep learning to enhance image quality from lower-resolution acquired data as opposed to a simply retrospective undersampling of high-resolution data, as is more commonly performed [28]. To our knowledge, this is the first study to use both simultaneous T2 map generation and analysis for evaluating knee internal derangement and prospective deep learning for 3D knee MRI.…”

Section: Bmentioning

confidence: 99%

Diagnostic Accuracy of Quantitative Multicontrast 5-Minute Knee MRI Using Prospective Artificial Intelligence Image Quality Enhancement

Chaudhari

Grissom

Fang³

et al. 2021

American Journal of Roentgenology

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BACKGROUND. Potential approaches for abbreviated knee MRI, including prospective acceleration with deep learning, have achieved limited clinical implementation.OBJECTIVE. The objective of this study was to evaluate the interreader agreement between conventional knee MRI and a 5-minute 3D quantitative double-echo steadystate (qDESS) sequence with automatic T2 mapping and deep learning super-resolutionaugmentation and to compare the diagnostic performance of the two methods regarding findings from arthroscopic surgery.METHODS. Fifty-one patients with knee pain underwent knee MRI that included an additional 3D qDESS sequence with automatic T2 mapping. Fourier interpolation was followed by prospective deep learning super resolution to enhance qDESS slice resolution twofold. A musculoskeletal radiologist and a radiology resident performed independent retrospective evaluations of articular cartilage, menisci, ligaments, bones, extensor mechanism, and synovium using conventional MRI. Following a 2-month washout period, readers reviewed qDESS images alone followed by qDESS with the automatic T2 maps. Interreader agreement between conventional MRI and qDESS was computed using percentage agreement and Cohen kappa. The sensitivity and specificity of conventional MRI, qDESS alone, and qDESS plus T2 mapping were compared with arthroscopic findings using exact McNemar tests.RESULTS. Conventional MRI and qDESS showed 92% agreement in evaluating all tissues. Kappa was 0.79 (95% CI, 0.76-0.81) across all imaging findings. In 43 patients who underwent arthroscopy, sensitivity and specificity were not significantly different (p = .23 to > .99) between conventional MRI (sensitivity, 58-93%; specificity, 27-87%) and qDESS alone (sensitivity, 54-90%; specificity, 23-91%) for cartilage, menisci, ligaments, and synovium. For grade 1 cartilage lesions, sensitivity and specificity were 33% and 56%, respectively, for conventional MRI; 23% and 53% for qDESS (p = .81); and 46% and 39% for qDESS with T2 mapping (p = .80). For grade 2A lesions, values were 27% and 53% for conventional MRI, 26% and 52% for qDESS (p = .02), and 58% and 40% for qDESS with T2 mapping (p < .001).CONCLUSION. The qDESS method prospectively augmented with deep learning showed strong interreader agreement with conventional knee MRI and near-equivalent diagnostic performance regarding arthroscopy. The ability of qDESS to automatically generate T2 maps increases sensitivity for cartilage abnormalities.CLINICAL IMPACT. Using prospective artificial intelligence to enhance qDESS image quality may facilitate an abbreviated knee MRI protocol while generating quantitative T2 maps.

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Section: Machine Learning For Mri Datamentioning

confidence: 99%

“…Despite the large number of retrospective studies ( Figure 2 ), there are fewer applications of deep learning in the clinic on a routine basis [ 127 ]. The three major use cases that deep learning can have in MRI diagnostics: (1) model-free image synthesis, (2) model-based image reconstruction, and (3) image or pixel-level classification [ 127 ]. Hence, deep learning has the potential to improve every step of the MRI diagnostic workflow and to provide value for every user, from the technologists performing the scan, the physicians ordering the imaging, the radiologists providing the interpretation, and most importantly, the patients who are receiving health care.…”

Section: Machine Learning For Mri Datamentioning

confidence: 99%

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

et al. 2021

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The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.

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Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices

Cited by 61 publications

References 92 publications

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

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

Diagnostic Accuracy of Quantitative Multicontrast 5-Minute Knee MRI Using Prospective Artificial Intelligence Image Quality Enhancement

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

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