Medical and Legal Implications of MRI Scout Imaging in a Surgical Patient with Case Presentation

Albano, Stephen; Ramnot, Ajay; Siddiqi, Javed; Mahato, Deependra

doi:10.7759/cureus.6833

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Previous studies have demonstrated the utility of MRI localizer images for the detection of cerebral large vessel obstructions and intracranial hemorrhages. [46][47][48][49] The results of this study bolstered this argument with regard to neurodegenerative changes. As illustrated in Fig.…”

Section: S Inui Et Alsupporting

confidence: 75%

Voxel-based Morphometry of Alzheimer’s Disease Using a Localizer Image: A Comparative Study with Magnetization Prepared Rapid Acquisition with Gradient Echo

Inui,

Kaneda,

Sakurai

et al. 2024

magn reson med sci

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Purpose: Magnetization prepared rapid acquisition with gradient echo (MPRAGE) sequence is a goldstandard technique for voxel-based morphometry (VBM) because of high spatial resolution and excellent tissue contrast, especially between gray matter (GM) and white matter (WM). Despite its benefits, MPRAGE exhibits distinct challenge for VBM in some patients with neurological disease because of long scan time and motion artifacts. Speedily acquired localizer images may alleviate this problem. This study aimed to evaluate the feasibility of VBM using 3D Fast Low Angle Shot image captured for localizer (L3DFLASH).Methods: Consecutive 13 patients with pathologically confirmed Alzheimer's disease (AD) (82 ± 9 years) and 21 healthy controls (HC) (79 ± 4 years) were included in this study. Whole-brain L3DFLASH and MPRAGE were captured and preprocessed using the Computational Anatomy Toolbox 12 (CAT12). Agreement with MPRAGE was evaluated for L3DFLASH using regional normalized volume for segmented brain areas. In addition to brain volume difference on VBM and Bland-Altman analysis, atrophic pattern of AD on VBM was evaluated using L3DFLASH and MPRAGE.Results: Acquisition time was 18 s for L3DFLASH and 288 s for MPRAGE. There was a slight systematic difference in all regional normalized volumes from L3DFLASH and MPRAGE. For the whole cohort, GM volume measured from MPRAGE was greater than that from L3DFLASH in most of the region on VBM. When AD and HC were compared, AD-related atrophic pattern was demonstrated in both L3DFLASH and MPRAGE on VBM, although the difference was noted in significant clusters between them. Conclusion:Although systematic difference was noted in regional brain volume measured from L3DFLASH and MPRAGE, AD-related atrophic pattern was preserved in L3DFLASH on VBM. VBM, using speedily acquired localizer image, may provide limited but useful information for evaluating brain atrophy.

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Section: S Inui Et Alsupporting

confidence: 75%

Voxel-based Morphometry of Alzheimer’s Disease Using a Localizer Image: A Comparative Study with Magnetization Prepared Rapid Acquisition with Gradient Echo

Inui,

Kaneda,

Sakurai

et al. 2024

magn reson med sci

View full text Add to dashboard Cite

show abstract

“…At present, the main method of diagnosis based on chest X-ray images is to rely on radiologists to retrieve the images and compare them with the patient’s medical history [ 22 ]. The manual reading and large workloads significantly increase the job-related pressure of radiologists in examinations, leading to long-term, high-load, and high-tension work conditions for radiologists [ 23 ]. Such long-term work increases the fatigue of radiologists and increases the odds of missing the diagnosis of small lesions [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning for Chest X-ray Diagnosis: Competition Between Radiologists with or Without Artificial Intelligence Assistance

Guo,

Zhou,

et al. 2024

J Digit Imaging. Inform. med.

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This study aimed to assess the performance of a deep learning algorithm in helping radiologist achieve improved efficiency and accuracy in chest radiograph diagnosis. We adopted a deep learning algorithm to concurrently detect the presence of normal findings and 13 different abnormalities in chest radiographs and evaluated its performance in assisting radiologists. Each competing radiologist had to determine the presence or absence of these signs based on the label provided by the AI. The 100 radiographs were randomly divided into two sets for evaluation: one without AI assistance (control group) and one with AI assistance (test group). The accuracy, false-positive rate, false-negative rate, and analysis time of 111 radiologists (29 senior, 32 intermediate, and 50 junior) were evaluated. A radiologist was given an initial score of 14 points for each image read, with 1 point deducted for an incorrect answer and 0 points given for a correct answer. The final score for each doctor was automatically calculated by the backend calculator. We calculated the mean scores of each radiologist in the two groups (the control group and the test group) and calculated the mean scores to evaluate the performance of the radiologists with and without AI assistance. The average score of the 111 radiologists was 597 (587–605) in the control group and 619 (612–626) in the test group (P < 0.001). The time spent by the 111 radiologists on the control and test groups was 3279 (2972–3941) and 1926 (1710–2432) s, respectively (P < 0.001). The performance of the 111 radiologists in the two groups was evaluated by the area under the receiver operating characteristic curve (AUC). The radiologists showed better performance on the test group of radiographs in terms of normal findings, pulmonary fibrosis, heart shadow enlargement, mass, pleural effusion, and pulmonary consolidation recognition, with AUCs of 1.0, 0.950, 0.991, 1.0, 0.993, and 0.982, respectively. The radiologists alone showed better performance in aortic calcification (0.993), calcification (0.933), cavity (0.963), nodule (0.923), pleural thickening (0.957), and rib fracture (0.987) recognition. This competition verified the positive effects of deep learning methods in assisting radiologists in interpreting chest X-rays. AI assistance can help to improve both the efficacy and efficiency of radiologists.

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Demographic and clinical data of patients with spinal epidural angiolipomas

Lu,

Wang,

Liu

et al. 2024

Sci Rep

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Spinal epidural angiolipomas are rare, benign, mesenchymal tumors. It remains unclear whether spinal epidural angiolipomas are genuinely rare or merely underreported. Herein, we assessed the demographic and clinical characteristics of patients with spinal epidural angiolipoma. We collected data from patients with spinal epidural angiolipoma from three sources. First, we retrospectively analyzed data from patients diagnosed with spinal epidural angiolipoma in our hospital between January 1, 2014, and December 31, 2023. Second, we performed a literature review of studies retrieved from PubMed. Third, we retrieved detailed data of patients with spinal angiolipoma from the Surveillance, Epidemiology, and End Results (SEER) database. We conducted a descriptive analysis to investigate the demographic and clinical characteristics of patients with spinal epidural angiolipoma. At our institution, three patients were diagnosed with spinal epidural angiolipoma. Additionally, we identified 116 patients from the literature review and 15 patients from the SEER database. We reviewed the treatment history and imaging features of the three patients from our institution. The descriptive analysis of the data collected from the literature review was consistent with previous reports. For example, 63.0% of lesions were located at the thoracic level. 31.9% of these lesions involved two vertebral bodies, while 75.6% involved 2–4 vertebral bodies. The most common symptoms experienced by patients were back pain, paraparesis, and numbness in the legs. Surgery was the primary treatment option for most patients, and complete tumor resection was achieved in the majority of patients. The male:female ratio was 1:1.4, the median age at diagnosis for the patients from the literature was 49 years old, and the median follow-up was 24 months. Notably, most of the reports came from Asia and there were few reports from Africa. The findings from the SEER database indicated a male:female ratio of 2:1. The peak incidence, which is typically reported in the fifth decade of life, was not observed. We presented three cases of spinal epidural angiolipoma and supplemented our findings with a literature review and population-based analysis according to the SEER database for the United States population. We believe that our research will enhance clinicians’ comprehension of this uncommon tumor.

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Medical and Legal Implications of MRI Scout Imaging in a Surgical Patient with Case Presentation

Cited by 4 publications

References 11 publications

Voxel-based Morphometry of Alzheimer’s Disease Using a Localizer Image: A Comparative Study with Magnetization Prepared Rapid Acquisition with Gradient Echo

Voxel-based Morphometry of Alzheimer’s Disease Using a Localizer Image: A Comparative Study with Magnetization Prepared Rapid Acquisition with Gradient Echo

Deep Learning for Chest X-ray Diagnosis: Competition Between Radiologists with or Without Artificial Intelligence Assistance

Demographic and clinical data of patients with spinal epidural angiolipomas

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