Artificial intelligence based image quality enhancement in liver MRI: a quantitative and qualitative evaluation

Artificial intelligence in fracture detection on radiographs: a literature review

Fractures are one of the most common reasons of admission to emergency department affecting individuals of all ages and regions worldwide that can be misdiagnosed during radiologic examination. Accurate and timely diagnosis of fracture is crucial for patients, and artificial intelligence that uses algorithms to imitate human intelligence to aid or enhance human performs is a promising solution to address this issue. In the last few years, numerous commercially available algorithms have been developed to enhance radiology practice and a large number of studies apply artificial intelligence to fracture detection. Recent contributions in literature have described numerous advantages showing how artificial intelligence performs better than doctors who have less experience in interpreting musculoskeletal X-rays, and assisting radiologists increases diagnostic accuracy and sensitivity, improves efficiency, and reduces interpretation time. Furthermore, algorithms perform better when they are trained with big data on a wide range of fracture patterns and variants and can provide standardized fracture identification across different radiologist, thanks to the structured report. In this review article, we discuss the use of artificial intelligence in fracture identification and its benefits and disadvantages. We also discuss its current potential impact on the field of radiology and radiomics.

Accelerated T2W Imaging with Deep Learning Reconstruction in Staging Rectal Cancer: A Preliminary Study

Deep learning reconstruction (DLR) has exhibited potential in saving scan time. There is limited research on the evaluation of accelerated acquisition with DLR in staging rectal cancers. Our first objective was to explore the best DLR level in saving time through phantom experiments. Resolution and number of excitations (NEX) adjusted for different scan time, image quality of conventionally reconstructed T2W images were measured and compared with images reconstructed with different DLR level. The second objective was to explore the feasibility of accelerated T2W imaging with DLR in image quality and diagnostic performance for rectal cancer patients. 52 patients were prospectively enrolled to undergo accelerated acquisition reconstructed with highly-denoised DLR (DLR_H40sec) and conventional reconstruction (ConR2min). The image quality and diagnostic performance were evaluated by observers with varying experience and compared between protocols using κ statistics and area under the receiver operating characteristic curve (AUC). The phantom experiments demonstrated that DLR_H could achieve superior signal-to-noise ratio (SNR), detail conspicuity, sharpness, and less distortion within the least scan time. The DLR_H40sec images exhibited higher sharpness and SNR than ConR2min. The agreements with pathological TN-stages were improved using DLR_H40sec images compared to ConR2min (T: 0.846vs. 0.771, 0.825vs. 0.700, and 0.697vs. 0.512; N: 0.527vs. 0.521, 0.421vs. 0.348 and 0.517vs. 0.363 for junior, intermediate, and senior observes, respectively). Comparable AUCs to identify T3-4 and N1-2 tumors were achieved using DLR_H40sec and ConR2min images (P > 0.05). Consequently, with 2/3-time reduction, DLR_H40sec images showed improved image quality and comparable TN-staging performance to conventional T2W imaging for rectal cancer patients.

Conceptual review of outcome metrics and measures used in clinical evaluation of artificial intelligence in radiology

Artificial intelligence (AI) has numerous applications in radiology. Clinical research studies to evaluate the AI models are also diverse. Consequently, diverse outcome metrics and measures are employed in the clinical evaluation of AI, presenting a challenge for clinical radiologists. This review aims to provide conceptually intuitive explanations of the outcome metrics and measures that are most frequently used in clinical research, specifically tailored for clinicians. While we briefly discuss performance metrics for AI models in binary classification, detection, or segmentation tasks, our primary focus is on less frequently addressed topics in published literature. These include metrics and measures for evaluating multiclass classification; those for evaluating generative AI models, such as models used in image generation or modification and large language models; and outcome measures beyond performance metrics, including patient-centered outcome measures. Our explanations aim to guide clinicians in the appropriate use of these metrics and measures.

Effectiveness of deep learning-based reconstruction for improvement of image quality and liver tumor detectability in the hepatobiliary phase of gadoxetic acid-enhanced magnetic resonance imaging

PURPOSE

To evaluate the effectiveness of deep learning-based reconstruction (DLR) in improving image quality and tumor detectability of isovoxel high-resolution breath-hold fat-suppressed T1-weighted imaging (HR-BH-FS-T1WI) in the hepatobiliary phase (HBP) of Gadoxetic acid-enhanced magnetic resonance imaging (Gd-EOB-MRI).

MATERIALS AND METHODS

This retrospective evaluated 42 patients with 98 liver tumors who underwent Gd-EOB-MRI between March 2023 and May 2023 using three techniques based on HBP imaging: isovoxel HR-BH-FS-T1WI reconstructed (1) with DLR (BH-DLR +) and (2) without DLR (BH-DLR -) and (3) HR-FS-T1WI scanned with a free-breathing technique using a navigator-echo-triggered technique and DLR (Navi-DLR +). The three techniques were qualitatively and quantitatively compared by the Friedman test and the Bonferroni post-hoc test. Tumor detectability was compared using the McNemar test.

RESULTS

BH-DLR + (3.85, average score of two radiologists) showed significantly better qualitative scores for image noise than BH-DLR - (2.84) and Navi-DLR + (3.37) (p < 0.0167), and Navi-DLR + showed significantly better scores than BH-DLR - (p < 0.0167). BH-DLR + (3.77) and BH-DLR - (3.77) showed significantly better qualitative scores for respiratory motion artifact than Navi-DLR + (2.75) (p < 0.0167), but there was no significant difference in scores between BH-DLR + and BH-DLR - (p > 0.0167). BH-DLR + (0.32) and Navi-DLR + (0.33) showed significantly higher lesion-to-nonlesion CR than BH-DLR - (0.29) (p < 0.0167), but there was no significant difference in lesion-to-nonlesion CR between BH-DLR + and Navi-DLR + (p > 0.0167). BH-DLR + (89.8%) showed significantly better tumor detectability than BH-DLR - (76.0%) and Navi-DLR + (77.6%) (p < 0.05).

CONCLUSION

The use of DLR for isovoxel HR-BH-FS-T1WI was effective in improving image quality and tumor detectability.

Advanced MRI techniques in abdominal imaging

Magnetic resonance imaging (MRI) is a crucial modality for abdominal imaging evaluation of focal lesions and tissue properties. However, several obstacles, such as prolonged scan times, limitations in patients' breath-hold capacity, and contrast agent-associated artifacts, remain in abdominal MR images. Recent techniques, including parallel imaging, three-dimensional acquisition, compressed sensing, and deep learning, have been developed to reduce the scan time while ensuring acceptable image quality or to achieve higher resolution without extending the scan duration. Quantitative measurements using MRI techniques enable the noninvasive evaluation of specific materials. A comprehensive understanding of these advanced techniques is essential for accurate interpretation of MRI sequences. Herein, we therefore review advanced abdominal MRI techniques.

Three-dimensional heavily T2-weighted FLAIR in the detection of blood-labyrinthine barrier leakage in patients with sudden sensorineural hearing loss: comparison with T1 sequences and application of deep learning-based reconstruction

OBJECTIVE

Blood-labyrinthine barrier leakage has been reported in sudden sensorineural hearing loss (SSNHL). We compared immediate post-contrast 3D heavily T2-weighted fluid-attenuated inversion recovery (FLAIR), T1 spin echo (SE), and 3D T1 gradient echo (GRE) sequences, and heavily T2-weighted FLAIR (hvT2F) with and without deep learning-based reconstruction (DLR) in detecting perilymphatic enhancement.

METHODS

Fifty-four patients with unilateral SSNHL who underwent ear MRI with three sequences were included. We compared asymmetry scores, confidence scores, and detection rates of perilymphatic enhancement among the three sequences and obtained 3D hvT2F with DLR from 35 patients. The above parameters and subjective image quality between 3D hvT2F with and without DLR were compared.

RESULTS

Asymmetry scores and detection rate of 3D hvT2F were significantly higher than 3D GRE T1 and SE T1 (respectively, 1.37, 0.11, 0.19; p < 0.001). Asymmetry scores significantly increased with DLR compared to 3D hvT2F for experienced and inexperienced readers (respectively, 1.77 vs. 1.40, p = 0.036; 1.49 vs. 1.03, p = 0.012). The detection rate significantly increased only for the latter (57.1% vs. 31.4%, p = 0.022). Patients with perilymphatic enhancement had significantly higher air conduction thresholds on initial (77.96 vs. 57.79, p = 0.002) and 5 days after presentation (63.38 vs. 41.85, p = 0.019).

CONCLUSION

3D hvT2F significantly increased the detectability of perilymphatic enhancement compared to 3D GRE T1 and SE T1. DLR further improved the conspicuity of perilymphatic enhancement in 3D hvT2F. 3D hvT2F and DLR are useful for evaluating blood-labyrinthine barrier leakage; furthermore, they might provide prognostic value in the early post-treatment period.

CLINICAL RELEVANCE STATEMENT

Ten-minute post-contrast 3D heavily T2-weighed FLAIR imaging is a potentially efficacious sequence in demonstrating perilymphatic enhancement in patients with sudden sensorineural hearing loss and may be further improved by deep learning-based reconstruction.

KEY POINTS

• 3D heavily T2-weighted FLAIR (3D hvT2F) is a sequence sensitive in detecting low concentrations of contrast in the perilymphatic space. • 3D hvT2F sequences properly demonstrated perilymphatic enhancement in sudden sensorineural hearing loss compared to T1 sequences and were further improved by deep learning-based reconstruction (DLR). • 3D hvT2F and DLR are efficacious sequences in detecting blood-labyrinthine barrier leakage and with potential prognostic information.

Quantitative ultrasound (QUS) in the evaluation of liver steatosis: data reliability in different respiratory phases and body positions

Liver steatosis is the most common chronic liver disease and affects 10-24% of the general population. As the grade of disease can range from fat infiltration to steatohepatitis and cirrhosis, an early diagnosis is needed to set the most appropriate therapy. Innovative noninvasive radiological techniques have been developed through MRI and US. MRI-PDFF is the reference standard, but it is not so widely diffused due to its cost. For this reason, ultrasound tools have been validated to study liver parenchyma. The qualitative assessment of the brightness of liver parenchyma has now been supported by quantitative values of attenuation and scattering to make the analysis objective and reproducible. We aim to demonstrate the reliability of quantitative ultrasound in assessing liver fat and to confirm the inter-operator reliability in different respiratory phases. We enrolled 45 patients examined during normal breathing at rest, peak inspiration, peak expiration, and semi-sitting position. The highest inter-operator agreement in both attenuation and scattering parameters was achieved at peak inspiration and peak expiration, followed by semi-sitting position. In conclusion, this technology also allows to monitor uncompliant patients, as it grants high reliability and reproducibility in different body position and respiratory phases.

Advanced Abdominal MRI Techniques and Problem-Solving Strategies

MRI plays an important role in abdominal imaging because of its ability to detect and characterize focal lesions. However, MRI examinations have several challenges, such as comparatively long scan times and motion management through breath-holding maneuvers. Techniques for reducing scan time with acceptable image quality, such as parallel imaging, compressed sensing, and cutting-edge deep learning techniques, have been developed to enable problem-solving strategies. Additionally, free-breathing techniques for dynamic contrast-enhanced imaging, such as extra-dimensional-volumetric interpolated breath-hold examination, golden-angle radial sparse parallel, and liver acceleration volume acquisition Star, can help patients with severe dyspnea or those under sedation to undergo abdominal MRI. We aimed to present various advanced abdominal MRI techniques for reducing the scan time while maintaining image quality and free-breathing techniques for dynamic imaging and illustrate cases using the techniques mentioned above. A review of these advanced techniques can assist in the appropriate interpretation of sequences.

Diffusion kurtosis imaging and standard diffusion imaging in the magnetic resonance imaging assessment of prostate cancer

Background and Objective

In recent years, magnetic resonance imaging (MRI) has shown excellent results in the study of the prostate gland. MRI has indeed shown to be advantageous in the prostate cancer (PCa) detection, as in guiding targeting biopsy, improving its diagnostic yield. Although current acquisition protocols provide for multiparametric acquisition, recent evidence has shown that biparametric protocols can be non-inferior in PCa detection. Diffusion-weighted imaging (DWI) sequence, in particular, plays a key role, particularly in the peripheral zone which accounts for the larger part of the prostate. High b-values are generally recommended, although with the possibility of obtaining non-Gaussian diffusion effects, which requires a more sophisticated model for the analysis, namely through the diffusion kurtosis imaging (DKI). Purpose of this narrative review was to analyze the current applications and clinical evidence regarding the use of DKI with a main focus on PCa detection, also in comparison with DWI.

Methods

This narrative review synthesized the findings of literature retrieved from main researches, narrative and systematic reviews, and meta-analyses obtained from PubMed.

Key Content and Findings

DKI analyses the non-Gaussian water diffusivity and describe the effect of signal intensity decay related to high b-value through two main metrics (Dapp and Kapp). Differently from DWI-apparent diffusion coefficient (DWI-ADC) which reflects only water restriction outside of cells, DKI metrics are supposed to represent also the direct interaction of water molecules with cell membranes and intracellular compounds. This review describes current evidence on ADC and DKI metrics in clinical imaging, and finally collect the results derived from the main articles focused on DWI and DKI models in detecting PCa.

Conclusions

DKI advantages, compared to conventional ADC analysis, still remain controversial. Wider application and greater technical knowledge of DKI, however, may help in proving its intrinsic validity in the field of oncology and therefore in the study of clinically significant PCa. Finally, a deep understanding of DKI is important for radiologists to better understand what Kapp and Dapp mean in the context of different cancer and how these metrics may vary specifically in PCa imaging.

Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment

OBJECTIVE

The aim of this study was the evaluation radiomics analysis efficacy performed using computed tomography (CT) and magnetic resonance imaging in the prediction of colorectal liver metastases patterns linked to patient prognosis: tumor growth front; grade; tumor budding; mucinous type. Moreover, the prediction of liver recurrence was also evaluated.

METHODS

The retrospective study included an internal and validation dataset; the first was composed by 119 liver metastases from 49 patients while the second consisted to 28 patients with single lesion. Radiomic features were extracted using PyRadiomics. Univariate and multivariate approaches including machine learning algorithms were employed.

RESULTS

The best predictor to identify tumor growth was the Wavelet_HLH_glcm_MaximumProbability with an accuracy of 84% and to detect recurrence the best predictor was wavelet_HLH_ngtdm_Complexity with an accuracy of 90%, both extracted by T1-weigthed arterial phase sequence. The best predictor to detect tumor budding was the wavelet_LLH_glcm_Imc1 with an accuracy of 88% and to identify mucinous type was wavelet_LLH_glcm_JointEntropy with an accuracy of 92%, both calculated on T2-weigthed sequence. An increase statistically significant of accuracy (90%) was obtained using a linear weighted combination of 15 predictors extracted by T2-weigthed images to detect tumor front growth. An increase statistically significant of accuracy at 93% was obtained using a linear weighted combination of 11 predictors by the T1-weigthed arterial phase sequence to classify tumor budding. An increase statistically significant of accuracy at 97% was obtained using a linear weighted combination of 16 predictors extracted on CT to detect recurrence. An increase statistically significant of accuracy was obtained in the tumor budding identification considering a K-nearest neighbors and the 11 significant features extracted T1-weigthed arterial phase sequence.

CONCLUSIONS

The results confirmed the Radiomics capacity to recognize clinical and histopathological prognostic features that should influence the choice of treatments in colorectal liver metastases patients to obtain a more personalized therapy.

Radiomics in gastrointestinal stromal tumours: an up-to-date review

Gastrointestinal stromal tumours are rare mesenchymal neoplasms originating from the Cajal cells and represent the most common sarcomas in the gastroenteric tract. Symptoms may be absent or non-specific, ranging from fatigue and weight loss to acute abdomen. Nowadays endoscopy, echoendoscopy, contrast-enhanced computed tomography, magnetic resonance imaging and positron emission tomography are the main methods for diagnosis. Because of their rarity, these neoplasms may not be included immediately in the differential diagnosis of a solitary abdominal mass. Radiomics is an emerging technique that can extract medical imaging information, not visible to the human eye, transforming it into quantitative data. The purpose of this review is to demonstrate how radiomics can improve the already known imaging techniques by providing useful tools for the diagnosis, treatment, and prognosis of these tumours.

Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics

In this narrative review, we reported un up-to-date on the role of radiomics to assess prognostic features, which can impact on the liver metastases patient treatment choice. In the liver metastases patients, the possibility to assess mutational status (RAS or MSI), the tumor growth pattern and the histological subtype (NOS or mucinous) allows a better treatment selection to avoid unnecessary therapies. However, today, the detection of these features require an invasive approach. Recently, radiomics analysis application has improved rapidly, with a consequent growing interest in the oncological field. Radiomics analysis allows the textural characteristics assessment, which are correlated to biological data. This approach is captivating since it should allow to extract biological data from the radiological images, without invasive approach, so that to reduce costs and time, avoiding any risk for the patients. Several studies showed the ability of Radiomics to identify mutational status, tumor growth pattern and histological type in colorectal liver metastases. Although, radiomics analysis in a non-invasive and repeatable way, however features as the poor standardization and generalization of clinical studies results limit the translation of this analysis into clinical practice. Clear limits are data-quality control, reproducibility, repeatability, generalizability of results, and issues related to model overfitting.

Exploring Radiologists' Burnout in the COVID-19 Era: A Narrative Review

Since its beginning in March 2020, the COVID-19 pandemic has claimed an exceptionally high number of victims and brought significant disruption to the personal and professional lives of millions of people worldwide. Among medical specialists, radiologists have found themselves at the forefront of the crisis due to the pivotal role of imaging in the diagnostic and interventional management of COVID-19 pneumonia and its complications. Because of the disruptive changes related to the COVID-19 outbreak, a proportion of radiologists have faced burnout to several degrees, resulting in detrimental effects on their working activities and overall wellbeing. This paper aims to provide an overview of the literature exploring the issue of radiologists' burnout in the COVID-19 era.

Post-Surgical Imaging Assessment in Rectal Cancer: Normal Findings and Complications

Rectal cancer (RC) is one of the deadliest malignancies worldwide. Surgery is the most common treatment for RC, performed in 63.2% of patients. The type of surgical approach chosen aims to achieve maximum residual function with the lowest risk of recurrence. The selection is made by a multidisciplinary team that assesses the characteristics of the patient and the tumor. Total mesorectal excision (TME), including both low anterior resection (LAR) and abdominoperineal resection (APR), is still the standard of care for RC. Radical surgery is burdened by a 31% rate of major complications (Clavien-Dindo grade 3-4), such as anastomotic leaks and a risk of a permanent stoma. In recent years, less-invasive techniques, such as local excision, have been tested. These additional procedures could mitigate the morbidity of rectal resection, while providing acceptable oncologic results. The "watch and wait" approach is not a globally accepted model of care but encouraging results on selected groups of patients make it a promising strategy. In this plethora of treatments, the radiologist is called upon to distinguish a physiological from a pathological postoperative finding. The aim of this narrative review is to identify the main post-surgical complications and the most effective imaging techniques.