Dr. Matthew D. Holbrook Profile

Dr. Matthew D. Holbrook

AI Scientist at Duke Univ. School of Medicine

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Publications (14)

Proceedings Article | 4 April 2022 Presentation + Paper

Spectral micro-CT and radiomic analysis for classification of tumors based on lymphocytic burden in cancer therapy studies

A. Allphin, Y. Mowery, K. Lafata, D. Clark, A. Basil, Rico Castillo, M. Holbrook, K. Ghaghada, C. Badea

Proceedings Volume 12036, 120361H (2022) https://doi.org/10.1117/12.2611519

KEYWORDS: Tumors, Iodine, Computed tomography, Statistical analysis, Cancer, In vivo imaging, Image segmentation, Nanoparticles

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Proceedings Article | 15 February 2021 Presentation + Paper

Deep learning based spectral distortion correction and decomposition for photon counting CT using calibration provided by an energy integrated detector

M. Holbrook, D. Clark, C. Badea

Proceedings Volume 11595, 1159520 (2021) https://doi.org/10.1117/12.2581124

KEYWORDS: Distortion, Sensors, Photon counting, Calibration, Data modeling, Spectral models, Spectral calibration, Spatial resolution, Data integration, Computed tomography

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While the benefits of photon counting (PC) CT are significant, the performance of current PCDs is limited by physical effects distorting the spectral response. In this work, we examine a deep learning (DL) approach for spectral correction and material decomposition of PCCT data using multi-energy CT data sets acquired with an energy integrating detector (EID) for spectral calibration. We use a convolutional neural network (CNN) with a U-net structure and compare image domain and projection domain approaches to spectral correction and material decomposition. We trained our networks using noisy, spectrally distorted PCD projections as input data while the labels were derived from multi-energy EID data. For this study, we have scanned: 1) phantoms containing vials of iodine and calcium in water and 2) mice injected with an iodine-based liposomal contrast agent. Our results show that the image-based approach corrects best for spectral distortions and provides the lowest errors in material maps (RMSE in iodine: 0.34 mg/mL) compared with the projection-based approach (0.74 mg/mL) and image domain decomposition without correction (2.67 mg/mL). Both DL methods are, however, affected by a loss of spatial resolution (from 3 lp/mm in EID labels to ~2.2 lp/mm in corrected reconstructions). In summary, we demonstrate that multi-energy EID acquisitions can provide training data for DL-based spectral distortion correction. This data-driven correction does not require intimate knowledge of the spectral response or spectral distortions of the PCD. While the benefits of photon counting (PC) CT are significant, the performance of current PCDs is limited by physical effects distorting the spectral response. In this work, we examine a deep learning (DL) approach for spectral correction and material decomposition of PCCT data using multi-energy CT data sets acquired with an energy integrating detector (EID) for spectral calibration. We use a convolutional neural network (CNN) with a U-net structure and compare image domain and projection domain approaches to spectral correction and material decomposition. We trained our networks using noisy, spectrally distorted PCD projections as input data while the labels were derived from multi-energy EID data. For this study, we have scanned: 1) phantoms containing vials of iodine and calcium in water and 2) mice injected with an iodine-based liposomal contrast agent. Our results show that the image-based approach corrects best for spectral distortions and provides the lowest errors in material maps (RMSE in iodine: 0.34 mg/mL) compared with the projection-based approach (0.74 mg/mL) and image domain decomposition without correction (2.67 mg/mL). Both DL methods are, however, affected by a loss of spatial resolution (from 3 lp/mm in EID labels to ~2.2 lp/mm in corrected reconstructions). In summary, we demonstrate that multi-energy EID acquisitions can provide training data for DL-based spectral distortion correction. This data-driven correction does not require intimate knowledge of the spectral response or spectral distortions of the PCD.

Proceedings Article | 15 February 2021 Presentation + Paper

Towards deep learning detection of lung nodules using micro-CT

M. Holbrook, D. Clark, R. Patel, Y. Qi, Y. Mowery, C. Badea

Proceedings Volume 11600, 116000I (2021) https://doi.org/10.1117/12.2581120

KEYWORDS: Lung, Image segmentation, Tumors, Virtual reality, Tomography, Radiotherapy, Motion models, In vivo imaging, Data acquisition, Computer simulations

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Proceedings Article | 16 March 2020 Presentation + Paper

Development of a spectral micro-CT system using a photon counting detector with anti-coincidence corrections

M. Holbrook, D. Clark, C. Badea

Proceedings Volume 11312, 113121I (2020) https://doi.org/10.1117/12.2549617

KEYWORDS: Barium, Gadolinium, Sensors, Calcium, Tumors, Imaging systems, Cancer, Photon counting, Iodine, Modulation transfer functions

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Proceedings Article | 16 March 2020 Presentation + Paper

Hybrid energy integrating and photon counting micro-CT

M. Holbrook, D. Clark, C. Badea

Proceedings Volume 11312, 113120F (2020) https://doi.org/10.1117/12.2549603

KEYWORDS: Gold, Sensors, Gadolinium, X-ray computed tomography, Modulation transfer functions, Calcium, Photon counting, X-ray technology, X-rays, Prototyping

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Proceedings Article | 28 February 2020 Presentation + Paper

MRI-based radiomics of sarcomas in the preclinical arm of a co-clinical trial

M. Holbrook, S. Blocker, Y. Mowery, A. Badea, Y. Qi, D. Kirsch, C. Badea

Proceedings Volume 11317, 1131711 (2020) https://doi.org/10.1117/12.2549628

KEYWORDS: Tumors, Magnetic resonance imaging, Image segmentation, Data modeling, Tissues, Radiotherapy, Neural networks, Statistical analysis, Mouse models, Receivers

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Proceedings Article | 28 February 2020 Presentation + Paper

A spectral CT study on iodine augmentation of radiation therapy and its potential for combination with chemotherapy

C. Badea, K. Ghaghada, M. Holbrook, Prajwal Bhandari, D. Clark, Y. Qi, Y. Mowery

Proceedings Volume 11317, 113171N (2020) https://doi.org/10.1117/12.2549583

KEYWORDS: Tumors, Gadolinium, Iodine, X-ray computed tomography, Computed tomography, Nanoparticles, Calcium, In vivo imaging, Cancer

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Proceedings Article | 15 March 2019 Presentation + Paper

To gate or not to gate: an evaluation of respiratory gating techniques to improve volume measurement of murine lung tumors in micro-CT imaging

S. Blocker, M. Holbrook, Y. Mowery, C. Badea

Proceedings Volume 10953, 109531H (2019) https://doi.org/10.1117/12.2512534

KEYWORDS: Tumors, Lung, Image segmentation, In vivo imaging, Binary data, Preclinical imaging, Computed tomography, Cancer, Reconstruction algorithms

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Proceedings Article | 1 March 2019 Paper

Multi-modal MRI segmentation of sarcoma tumors using convolutional neural networks

M. Holbrook, S. Blocker, Y. Mowery, C. Badea

Proceedings Volume 10948, 109484D (2019) https://doi.org/10.1117/12.2512822

KEYWORDS: Image segmentation, Tumors, Magnetic resonance imaging, Tissues, Convolutional neural networks, Computer programming, 3D image processing, Convolution, Preclinical imaging

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SPIE Journal Paper | 24 August 2018

Overcoming detector limitations of x-ray photon counting for preclinical microcomputed tomography

Matthew Holbrook, Darin Clark, Cristian Badea

JMI, Vol. 6, Issue 01, 011004, (August 2018) https://doi.org/10.1117/12.10.1117/1.JMI.6.1.011004

KEYWORDS: Sensors, Gold, Iodine, X-ray computed tomography, Photon counting, X-rays, X-ray detectors, Microcomputed tomography, Spatial resolution, Reconstruction algorithms

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Proceedings Article | 9 March 2018 Presentation + Paper

Spectral imaging of iodine and gadolinium nanoparticles using dual-energy CT

C. Badea, M. Holbrook, D. Clark, K. Ghaghada

Proceedings Volume 10573, 105731I (2018) https://doi.org/10.1117/12.2293625

KEYWORDS: Gadolinium, Nanoparticles, In vivo imaging, Iodine, X-ray computed tomography, X-rays, Copper, Spleen, Visualization, Dual energy imaging

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Advances in CT hardware have propelled the development of novel CT contrast agents. Combined with the spectral capabilities of X-ray CT, molecular imaging is possible using multiple heavy-metal contrast agents. Nanoparticle platforms make particularly attractive agents because of (1) their ability to carry a large payload of imaging moieties, and (2) their ease of surface modification to facilitate molecular targeting. While several novel imaging moieties based on high atomic number elements are being explored, iodine (I) and gadolinium (Gd) are particularly attractive because they are already in clinical use. In this work, we investigate the feasibility for in vivo discrimination of iodine and gadolinium nanoparticles using dual energy micro-CT. Phantom experiments were performed to measure the CT enhancement for I and Gd over a range of voltages from 40 to 80 kVp using a dual-source micro-CT system with energy integrating detectors having cesium iodide scintillators. The two voltages that provide maximum discrimination between I and Gd were determined to be 50 kVp with Cu filtration and 40 kVp without any filtration. Serial dilutions of I and Gd agents were imaged to determine detection sensitivity using the optimal acquisition parameters. Next, an in vivo longitudinal small animal study was performed using Liposomal I (Lip-I) and Liposomal Gd (Lip-Gd) nanoparticles. The mouse was intravenously administered Lip-Gd and imaged within 1 h post-contrast to visualize Gd in the vascular compartment. The animal was reimaged at 72 h post-contrast with dual-energy micro-CT at 40 kVp and 50 kVp to visualize the accumulation of Lip-Gd in the liver and spleen. Immediately thereafter, the animal was intravenously administered Lip-I and re-imaged. The dual energy sets were used to estimate the concentrations of Gd and I via a two-material decomposition with a non-negativity constraint. The phantom results indicated that the relative contrast enhancement per mg/ml of I to Gd was 0.85 at 40 kVp and 1.79 at 50 kVp. According to the Rose criterion (CNR<5), the detectability limits were 2.67 mg/ml for I and 2.46 mg/ml for Gd. The concentration maps confirmed the expected biodistribution, with Gd concentrated in the spleen and with I in the vasculature of the kidney, liver, and spleen. Iterative reconstruction provided higher sensitivity to detect relatively low concentrations of gadolinium. In conclusion, dual energy micro-CT can be used to discriminate and simultaneously image probes containing I and Gd.

Proceedings Article | 9 March 2018 Presentation + Paper

Multi-energy CT decomposition using convolutional neural networks

D. Clark, M. Holbrook, C. Badea

Proceedings Volume 10573, 105731O (2018) https://doi.org/10.1117/12.2293728

KEYWORDS: Barium, Iodine, Gold, X-ray computed tomography, Tumors, Signal attenuation, In vivo imaging, Computed tomography, Convolutional neural networks

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Proceedings Article | 9 March 2018 Paper

Multiscale dual energy micro-CT for imaging using iodinated and gold nanoparticles

M. Holbrook, D. Clark, C. Badea

Proceedings Volume 10573, 105734M (2018) https://doi.org/10.1117/12.2293688

KEYWORDS: In vivo imaging, Imaging systems, Gold, Image resolution, Nanoparticles, Iodine, Dual energy imaging, Sensors, Spatial resolution, Computed tomography

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Proceedings Article | 9 March 2018 Presentation + Paper

Development of a spectral photon-counting micro-CT system with a translate-rotate geometry

M. Holbrook, D. Clark, W. Barber, C. Badea

Proceedings Volume 10573, 105731D (2018) https://doi.org/10.1117/12.2293373

KEYWORDS: Sensors, Iodine, Gold, Prototyping, Calibration, Imaging systems, X-rays, Spectral calibration, Reconstruction algorithms, Computed tomography

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Showing 5 of 14 publications

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