Contributions

Welcome! The MIDI Lab started in November of 2018 at MSU

Abstracts

  1. Toia GV, Alessio AM, Mileto A. “Development of a Nonlinear Algorithm to Identify Minimal Detectable Concentrations of Trace Metals (Iron, Copper, Zinc) Using Dual-Energy CT in a Simulated Abdominal Phantom Experiment,” Society of Computed Body Tomography and Magnetic Resonance, Washington DC, 2018.

  2. Bindschadler, , Ferguson, Blackledge, Friedman, Otto, “Phantom for optimizing flow protocols in pediatric cardiac MRI,” Society for Cardiovascular Magnetic Resonance, Bellevue, 2019.

  3. Holste, Sullivan, Nagy, Bindschadler, Alessio, "Automatic Segmentation of Chest Radiographs with Deep Learning," MID-SURE Symposium, East Lansing, MI, 2019.

  4. Sullivan, Holste, Alessio, "Deep Learning Methods for Automatic Evaluation of Lines in Chest Radiographs," MID-SURE Symposium, East Lansing, MI, 2019.

  5. Adams, Pereira, Dighe, Cox, Rubin, Alessio, "Classification of Thyroid Nodules using Machine Learned One Class Autoencoders," MID-SURE Symposium, East Lansing, MI, 2019.

  6. Cox, Pereira, Dighe, Alessio, "Adaptation of the ResNet-50 Classification Architecture for the Prediction of Malignancy of Thyroid Nodules," MID-SURE Symposium, East Lansing, MI, 2019.

  7. Carras, Alessio, "Automatic Machine Learning Architecture Selection for Breast MRI Classification," MID-SURE Symposium, East Lansing, MI, 2019.

  8. Rubin, Adams, Cox, Pereira, Dighe, Wolf, and Alessio, “Towards automated structed reporting of thyroid ultrasound to reduce fine needle aspirations in thyroid cancer,” Michigan Osteopathic Association Annual Meeting, Grand Rapids, MI, 2019.

 

Conference Proceedings

  1. Pereira, Dighe, Alessio, “Comparison of machine learned approaches for thyroid nodule characterization from shear wave elastography images,” SPIE Medical Imaging, Houston 2018.

  2. Bindschadler, Branch, Alessio, “Evaluation of radiation dose reduction via myocardial frame reduction in dynamic cardiac CT for perfusion quantitation,” SPIE Medical Imaging, Houston 2018.

  3. Colbry, Murillo, Alessio, Christlieb, "Computational Mathematics, Science and Engineering (CMSE): Establishing an Academic Department Dedicated to Scientific Computation as a Discipline," J Computational Science Education, vol 11, pp 68-72, 2020.

  4. P Carras, C Pereira, D Biswas, C Lee, S Partridge, Alessio, "Genetic algorithm for machine learning architecture selection for breast MRI classification," SPIE Medical Imaging, Houston 2020.

  5. G Holste, R Sullivan, M Bindschadler, N Nagy, Alessio, "Multi-class semantic segmentation of pediatric chest radiographs," SPIE Medical Imaging, Houston 2020.

  6. R Sullivan, G Holste, J Burkow, Alessio, "Deep learning methods for segmentation of lines in pediatric chest radiographs," SPIE Medical Imaging, Houston 2020.

  7. J Cox, S Rubin, J Adams, C Pereira, M Dighe, Alessio, "Hyperparameter selection for ResNet classification of malignancy from thyroid ultrasound images," SPIE Medical Imaging, Houston 2020.

 

 

Papers

  1. Mileto, Zamora, Alessio, Pereira, … Kanal,  “CT Detectability of Small Low-Contrast Hypoattenuating Focal Lesions: Iterative Reconstructions vs. Filtered Backprojection,” Radiology vol 289:2, pp 443-454, 2018

  2. Doris, Otaki, Krishnanb, Kwiecinski, Rubeaux, Alessio, Pan, Cadet, Dey, Dweck, Newby, Berman, Slomka, "Optimization of reconstruction and quantification of motion-corrected coronary PET-CT," Journal of Nuclear Cardiology 383:705-11, 2018

  3. Machado, Menezes, Namías, Vieira, Queiroz, Matheoud, Alessio, Oliveira, “Protocols for Harmonized Quantification and Noise Reduction in Low Dose Oncological 18F-FDG PET/CT Imaging," Journal of Nuclear Cardiology, vol 47:1, pp 47-54, 2019

  4. Chapman, Menashe, Zare, Alessio, Ishak, “Establishment of normative values for the fetal posterior fossa by magnetic resonance imaging,” Prenatal Diagnosis, vol 38:13, pp 1035-1041, 2018.

  5. Saxena, Friedman, Bly, Otjen, Alessio, Li, Hannaford, Whipple, Moe, “Comparison of Micro–Computed Tomography and Clinical Computed Tomography Protocols for Visualization of Nasal Cartilage Before Surgical Planning for Rhinoplasty.” JAMA Facial Plastic Surgery, 2019.

  6. Hunter, Klein, Alessio, deKemp, “Patient body motion correction for dynamic cardiac PET-CT by attenuation-emission alignment according to projection consistency conditions,” Medical Physics, vol 46:4, pp 1697-1706, 2019.

  7. Pan, Einstein, Kappadath, Grogg, Lois-Gomez, Alessio, Hunter, El Fakhri, Kinahan, and Mawlawi, "Performance Evaluation of the 5-Ring GE Discovery MI PET/CT System Using the NEMA NU 2-2012 Standard," Medical Physics, vol. 57: 2, May 2019.

  8. Alessio, Bindschadler, Busey, Shuman, Caldwell, Branch, Accuracy of Myocardial Blood Flow Estimation from Dynamic Contrast Enhanced Cardiac CT Compared to PET," Circulation: Cardiovascular Imaging, vol. 12:6, 2019.

  9. Zhang, Zukić, Byrd, Enquobahrie, Alessio, Cleary, Banovac, and Kinahan, “PET/CT-guided biopsy with respiratory motion correction,” Int J Computer Assisted Radiology and Surgery, vol. 14:12, pp. 2187–2198, 2019.

  10. Otjen, Stanescu, Alessio, Parisi, "Ovarian torsion: developing a machine learned algorithm for diagnosis," to appear Pediatric Radiology, 2020.