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2024

  1. Diaz E, et al. Data Format Standardization and DICOM Integration for Hyperpolarized (13)C MRI, J Imaging Inform Med 2024 May 6. doi: 10.1007/s10278-024-01100-2. https://pubmed.ncbi.nlm.nih.gov/38710970/
  2. Robison TH, et al. Quantitative MRI reveals heterogeneous impacts of treatment on diseased bone marrow in a mouse model of myelofibrosis, Magn Reson Med 2024, 91, 2568-257. https://pubmed.ncbi.nlm.nih.gov/38265182/
  3. Li Y, et al. Learning ADC maps from accelerated radial k-space diffusion-weighted MRI in mice using a deep CNN-transformer model, Magn Reson Med 2024, 91, 105-117. https://pubmed.ncbi.nlm.nih.gov/37598398/.
  4. Roudi R, et al., Novel Clinically Translatable Iron Oxide Nanoparticle for Monitoring Anti-CD47 Cancer Immunotherapy, Invest Radiol 2024, 59, 391-403, https://www.ncbi.nlm.nih.gov/pubmed/37812494.
  5. Dutta K, et al. Deep learning generation of preclinical positron emission tomography (PET) images from low-count PET with task-based performance assessment, Med Phys 2024, 51, 4324-4339, https://pubmed.ncbi.nlm.nih.gov/38710222/.

2023 

  1. Moore SM, et al. Co-clinical Imaging Metadata Information (CIMI) for Cancer Research to Promote Open Science, Standardization, and Reproducibility in Preclinical Imaging. Tomography 2023, 9, 995-1009. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10204428/
  2. Zhang H. The National Cancer Institute's Co-Clinical Imaging Quantitative Research Resources for Precision Medicine in Preclinical and Clinical Settings. Tomography 2023, 9, 931-941. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10204454/
  3. Alkim E, et al. Toward Practical Integration of Omic and Imaging Data in Co-Clinical Trials. Tomography 2023, 9, 810–828. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10144684/
  4. Gammon ST, et al. An Online Repository for Pre-clinical Imaging Protocols (PIPs). Tomography 2023, 9, 750–758. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10145184/
  5. Sahin SI, et al. Metabolite-specific Echo Planar Imaging for Preclinical Studies with Hyperpolarized 13C-pyruvate MRI. Tomography 2023, 9, 736–749. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10143874/
  6. Peehl DM, et al. Animal Models and Their Role in Imaging-assisted Co-clinical Trials. Tomography 2023, 9, 657–680. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10037611/
  7. Kushwaha A, et al. Improved repeatability of mouse tibia volume segmentation in murine myelofibrosis model using deep learning. Tomography 2023, 9, 589–602. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10037585/
  8. Ross BD, et al. Repeatability of Quantitative Imaging Biomarkers in the Tibia Bone Marrow of a Murine Myelofibrosis Model. Tomography 2023, 9, 552–566. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10037563/
  9. Bae S-W, et al. Feasibility of [18F] FSPG-PET for Early Response Assessment to Blockade of EGFR and Glutamine Metabolism in Wild-type KRAS Colorectal Cancer. Tomography 2023, 9, 497–508. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10037609/
  10. Malyarenko D, et al. Evaluation of ADC Repeatability and Reproducibility of Pre-Clinical MRIs Using Standardized Procedures and DWI Phantom. Tomography 2023, 9, 375–386. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9964373/
  11. Patel R, et al. Neoadjuvant Radiation Therapy and Surgery Improves Metastasis-Free Survival over Surgery Alone in a Primary Mouse Model of Soft Tissue Sarcoma, Mol Cancer Ther 2023, 22, 112-122. https://pubmed.ncbi.nlm.nih.gov/36162051/
  12. Cao J, et al. Impact of Arterial Input Function and Pharmacokinetic Models on DCE-MRI Biomarkers for Detection of Vascular Effect Induced by Stroma-Directed Drug in an Orthotopic Mouse Model of Pancreatic Cancer, Mol Imaging Biol 2023, 25, 638-647, https://pubmed.ncbi.nlm.nih.gov/37166575/.
  13. Lin M, et al. Simplified and highly-reliable automated production of [(18)F]FSPG for clinical studies,  JNMMI Radiopharm Chem 2023, 8, 15, https://www.ncbi.nlm.nih.gov/pubmed/37486582.
  14. Nadkarni R, et al. A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images, Tomography 2023, 9, 1286-1302, https://www.ncbi.nlm.nih.gov/pubmed/37489470.
  15. Clark DP, et al, MCR toolkit: A GPU-based toolkit for multi-channel reconstruction of preclinical and clinical x-ray CT data, Med Phys 2023, 50, 4775-4796, https://www.ncbi.nlm.nih.gov/pubmed/37285215.
  16. Ramasamy SK, et al. Measurement of Tumor T2* Relaxation Times after Iron Oxide Nanoparticle Administration, J Vis Exp 2023, 195, https://pubmed.ncbi.nlm.nih.gov/37318243/.


2022

  1. Pickup S, et al. Dynamic Contrast Enhanced MRI in the Abdomen of Mice with High Temporal and Spatial Resolution using Stack of Stars Sampling and KWIC Reconstruction. Tomography 2022, 8, 2113–2128. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498490/
  2. Pemmaraju R, et al. Web-Based Application for Biomedical Image Registry, Analysis, and Translation (BiRAT). Tomography 2022, 8, 1453–1462. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9228304/
  3. Allphin AJ, et al. Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden. Tomography 2022, 8, 740–753. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8938796/
  4. Joaquim MR, et al. DWI Metrics Differentiating Benign Intraductal Papillary Mucinous Neoplasms from Invasive Pancreatic Cancer: A Study in GEM Models, Cancers (Basel) 2022, 14, 4017, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9406679/  
  5. Roy S, et al. Co‑clinical FDG‑PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple‑negative breast cancer, European Journal of Nuclear Medicine and Molecular Imaging, Eur J Nucl Med Mol Imaging2022, 49, 550-562. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8800941/
  6. Cohen AS, et al. First-in-Human PET Imaging and Estimated Radiation Dosimetry of l-[5-11C]-Glutamine in Patients with Metastatic Colorectal Cancer, J Nucl Med. 2022, 63, 36-43. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8717201/ 

  7. Ross BD, et al., A lymphatic-absorbed multi-targeted kinase inhibitor for myelofibrosis therapy, Nat Commun. 2022, 13:4730. https://pubmed.ncbi.nlm.nih.gov/35977945/.

  8. Robison TH, et al, Multiparametric MRI to quantify disease and treatment response in mice with myeloproliferative neoplasms, JCI Insight, 2022, 110;7(19):e161457. https://pubmed.ncbi.nlm.nih.gov/35998053/.


2021

  1. Dutta K, et al. Deep Learning Segmentation of Triple-Negative Breast Cancer (TNBC) Patient Derived Tumor Xenograft (PDX) and Sensitivity of Radiomic Pipeline to Tumor Probability Boundary, Cancers 2021, 13, 3795. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8345151/
  2. Holbrook MD, et al. Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning, Tomography 2021, 7, 358–372. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8396172/
  3. Blocker S, et al. Ex Vivo MR Histology and Cytometric Feature Mapping Connect Three-dimensional in Vivo MR Images to Two dimensional Histopathologic Images of Murine Sarcomas, Radiology: Imaging Cancer 2021, 3, e200103. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8183263/
  4. Cao J, et al. Respiratory Motion Mitigation and Repeatability of Two Diffusion- Weighted MRI Methods Applied to a Murine Model of Spontaneous Pancreatic Cancer. Tomography 2021, 7, 66–79. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8048371/
  5. Du T, et al.  Adaptive convolutional neural networks for accelerating magnetic resonance imaging via k-space data interpolation, Med Image Anal 2021, 72, e102098. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8734583/


2020

  1. Cohen AS, et al. Combined blockade of EGFR and glutamine metabolism in preclinical models of colorectal cancer, Translational Oncology 2020, 13, e100828, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7348062/
  2. Shoghi KI, et al., Co-Clinical Imaging Resource Program (CIRP): Bridging the Translational Divide to Advance Precision Medicine, Tomography 2020, 6, 273-287, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442091/
  3. Holbrook MD, et al. MRI-Based Deep Learning Segmentation and Radiomics of Sarcoma in Mice, Tomography 2020, 6, 23–33, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7138523/
  4. Wisdom AJ, et al. Single cell analysis reveals distinct immune landscapes in transplant and primary sarcomas that determine response or resistance to immunotherapy, NATURE COMMUNICATIONS 2020, 6410 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7746723/
  5. Roy S, et al. Shoghi, Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging, EBioMedicine 2020,59, e102963, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7479492/
  6. Savaikar MA, et al. Preclinical PERCIST and 25% of SUVmax Threshold: Precision Imaging of Response to Therapy in Co-clinical 18F-FDG PET Imaging of Triple-Negative Breast Cancer Patient–Derived Tumor Xenografts, J Nucl Med 2020, 61, 842–849, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7262224/
  7. Blocker SJ, et al. The impact of respiratory gating on improving volume measurement of murine lung tumors in micro-CT imaging, PLoS ONE 2020, 15, e0225019, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7041814/

2019

  1. Ge X, et al. Test–Retest Performance of a 1-Hour Multiparametric MR Image Acquisition Pipeline with Orthotopic Triple-Negative Breast Cancer Patient-Derived Tumor Xenografts, Tomography 2019, 5, 320-331, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6752291/
  2. Cao J, et al. Dynamic Contrast-enhanced MRI Detects Responses to Stroma-directed Therapy in Mouse Models of Pancreatic Ductal Adenocarcinoma, Clin Cancer Res 2019, 25, 2314-2322, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6445712/
  3. Blocker SJ, et al. Bridging the translational gap: Implementation of multimodal small animal imaging strategies for tumor burden assessment in a co-clinical trial, PLoS ONE 2019, 14, e0207555, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6453461/
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