Todd Pietila

Advanced Visualization
Tips for implementing 3D printing at your hospital
Although 3D printing as a core service in healthcare is still in its infancy, many innovative institutions have been blazing a path. In this article, Todd Pietila of 3D software developer Materialise offers tips for starting a 3D printing service at your facility.
November 3, 2015
2015 11 03 14 55 43 360 3d Printing Fig8 200
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CIPHER, an AI-powered CT foundation model, identifies patients with lung cancer at increased risk of immunotherapy-induced pneumonitis before treatment begins. The AI-generated output, shown here, highlights lung abnormalities to support early risk assessment and personalized patient care.
CT
CT-based AI model predicts pneumonitis in lung cancer patients
The algorithm outperformed clinical and radiomics models in these patients.
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Women's Imaging MinnieCast, Episode 18: On the road with the LBCA
Representative longitudinal chest CT images and five-year parametric response mapping (PRM), pulmonary vascular CT reconstructions, and hyperpolarized xenon-129 (Xe-129) ventilation MRI scans from survivors of nonsevere and severe COVID-19 pneumonia. (A–F) Images in a 68-year-old male survivor of nonsevere COVID-19 pneumonia, (G–L) 62-year-old male survivor of severe COVID-19 pneumonia, and (M–R) 55-year-old male survivor of severe COVID-19 pneumonia. For each participant, the first three columns show coronal chest CT images from the acute phase (A, G, M), two-year follow-up (B, H, N), and five-year follow-up (C, I, O) (left to right). The fourth column (D, J, P) shows a coronal PRM image for each participant at five-year follow-up derived from paired inspiratory and expiratory CT scans, showing the spatial distribution of lung functional categories. Green indicates normal lung tissue, yellow indicates functional small airway disease, and red indicates emphysema. The second-to-last column shows volume-rendered small pulmonary vessel visualization (red) at five-year follow-up per participant (E, K, Q). The column on the far right (F, L, R) shows a hyperpolarized Xe-129 MRI ventilation defect percentage (VDP) map at five-year follow-up per participant. Dark blue represents high Xe-129 signal intensity, corresponding to well-ventilated lung regions, whereas red represents no Xe-129 signal intensity, corresponding to ventilation defects. At five-year follow-up, the 68-year-old survivor of nonsevere COVID-19 pneumonia (top row) showed near-complete radiologic resolution, no respiratory symptoms, and preserved pulmonary function. In contrast, the 62-year-old survivor of severe COVID-19 pneumonia (middle row) showed persistent fibrotic linear opacities without respiratory symptoms and mildly reduced pulmonary function. The 55-year-old survivor of severe COVID-19 pneumonia (bottom row) showed persistent scattered fibrotic linear opacities and focal emphysematous changes in both lungs, with dyspnea and impaired gas exchange function. The VDP values obtained from F, L, and R were 7.7%, 7.1%, and 6.3%, respectively.
CT
What are the effects of COVID-19 pneumonia after 5 years?
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CT-based AI model predicts pneumonitis in lung cancer patients
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CIPHER, an AI-powered CT foundation model, identifies patients with lung cancer at increased risk of immunotherapy-induced pneumonitis before treatment begins. The AI-generated output, shown here, highlights lung abnormalities to support early risk assessment and personalized patient care.
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What are the effects of COVID-19 pneumonia after 5 years?
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After five years, residual lesions persisted in 35.6% of survivors (37 of 104).
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Representative longitudinal chest CT images and five-year parametric response mapping (PRM), pulmonary vascular CT reconstructions, and hyperpolarized xenon-129 (Xe-129) ventilation MRI scans from survivors of nonsevere and severe COVID-19 pneumonia. (A–F) Images in a 68-year-old male survivor of nonsevere COVID-19 pneumonia, (G–L) 62-year-old male survivor of severe COVID-19 pneumonia, and (M–R) 55-year-old male survivor of severe COVID-19 pneumonia. For each participant, the first three columns show coronal chest CT images from the acute phase (A, G, M), two-year follow-up (B, H, N), and five-year follow-up (C, I, O) (left to right). The fourth column (D, J, P) shows a coronal PRM image for each participant at five-year follow-up derived from paired inspiratory and expiratory CT scans, showing the spatial distribution of lung functional categories. Green indicates normal lung tissue, yellow indicates functional small airway disease, and red indicates emphysema. The second-to-last column shows volume-rendered small pulmonary vessel visualization (red) at five-year follow-up per participant (E, K, Q). The column on the far right (F, L, R) shows a hyperpolarized Xe-129 MRI ventilation defect percentage (VDP) map at five-year follow-up per participant. Dark blue represents high Xe-129 signal intensity, corresponding to well-ventilated lung regions, whereas red represents no Xe-129 signal intensity, corresponding to ventilation defects. At five-year follow-up, the 68-year-old survivor of nonsevere COVID-19 pneumonia (top row) showed near-complete radiologic resolution, no respiratory symptoms, and preserved pulmonary function. In contrast, the 62-year-old survivor of severe COVID-19 pneumonia (middle row) showed persistent fibrotic linear opacities without respiratory symptoms and mildly reduced pulmonary function. The 55-year-old survivor of severe COVID-19 pneumonia (bottom row) showed persistent scattered fibrotic linear opacities and focal emphysematous changes in both lungs, with dyspnea and impaired gas exchange function. The VDP values obtained from F, L, and R were 7.7%, 7.1%, and 6.3%, respectively.
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Representative MRI scans for which the independently assigned imaging-derived extranodal extension (iENE) grades differed among the readers. (A) Axial and sagittal images in a 56-year-old male patient with nasopharyngeal carcinoma and cervical nodal metastases. Readers 1 and 3 interpreted the finding as a metastatic lymph node invading the left sternocleidomastoid muscle (arrow) and assessed an iENE grade of 3, whereas reader 2 assigned a grade of 2. After joint review, the radiologists reached a consensus on the absence of definite sternocleidomastoid muscle invasion, and the final iENE grade was 2. (B) Axial and sagittal images in a 52-year-old male patient with nasopharyngeal carcinoma and cervical nodal metastases. Reader 1 interpreted the findings as a single enlarged metastatic lymph node and assessed an iENE grade of 1, whereas readers 2 and 3 interpreted them as two coalescent metastatic lymph nodes (arrows) and assessed a grade of 2. After joint review, the radiologists reached a consensus on the coalescence of the two metastatic lymph nodes, and the final iENE grade was 2.
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