Evidence map›Paper›PMID 42635411›Full record

ArticleOsteoarthritis imaging2026

Fusion of photon-counting CT and MRI improves assessment of the knee: a pilot study.

Zhaoye Zhou, Theresa Sophie Patzer, Mohamad Alzaher, Ali Guermazi, Dorra Guermazi, Karim Sariahmed, Allyson Zheng, Marco L Loggia, Mohamed Jarraya, Fang Liu

Abstract read
In one paragraph

Article in Osteoarthritis imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Zhaoye ZhouAthinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA, United States; Department of Radiology, Massachusetts General Hospital, Boston, MA, United States.
Theresa Sophie PatzerDepartment of Radiology, Massachusetts General Hospital, Boston, MA, United States; Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany.
Mohamad AlzaherDepartment of Radiology, Massachusetts General Hospital, Boston, MA, United States.
Ali GuermaziQuantitative Imaging Center, Department of Radiology, Boston University School of Medicine, Boston, MA, United States; Department of Radiology, VA Boston Healthcare System, West Roxbury, MA, United States.
Dorra GuermaziDepartment of Radiology, Massachusetts General Hospital, Boston, MA, United States; Warren Alpert Medical School of Brown University, Providence, RI, United States.
Karim SariahmedSection of General Internal Medicine, Department of Medicine, Boston Medical Center, Boston, MA, United States.
Allyson ZhengAthinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA, United States; Department of Radiology, Massachusetts General Hospital, Boston, MA, United States.
Marco L LoggiaAthinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA, United States; Department of Radiology, Massachusetts General Hospital, Boston, MA, United States.
Mohamed JarrayaDepartment of Radiology, Massachusetts General Hospital, Boston, MA, United States. Electronic address: mjarraya@mgh.harvard.edu.
Fang LiuAthinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA, United States; Department of Radiology, Massachusetts General Hospital, Boston, MA, United States.

Funding

Deep Learning Technology for Rapid Morphological and Quantitative Imaging of Knee PathologyR01AR079442 · NIAMS · MASSACHUSETTS GENERAL HOSPITAL · PI Fang Liu · 2022 to 2026
$2.2M
Rapid Three-dimensional Simultaneous Knee Multi-Relaxation MappingR01AR081344 · NIAMS · MASSACHUSETTS GENERAL HOSPITAL · PI Fang Liu · 2022 to 2026
$2.0M
Deep Learning Reconstruction for Rapid Multi-Component RelaxometryR21EB031185 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI LIU, FANG · 2022 to 2024
$690k
Ultra-Fast High-Resolution Multi-Parametric MRI for Characterizing Cartilage Extracellular MatrixR56AR081017 · NIAMS · MASSACHUSETTS GENERAL HOSPITAL · PI LIU, FANG · 2023 to 2023
$643k
Unraveling the discordance between structural damage and pain phenotypes in knee osteoarthritisK23AR084603 · NIAMS · MASSACHUSETTS GENERAL HOSPITAL · PI Mohamed Jarraya · 2024 to 2026
$525k
NIAMS NIH HHS K23 AR084603NIAMS NIH HHS R01 AR079442NIAMS NIH HHS R01 AR081344NIAMS NIH HHS R56 AR081017NIBIB NIH HHS R21 EB031185
6 · The paper itself

Abstract

purposeThe complementary information gained by combining photon counting CT (PCCT) and MRI may enhance the evaluation of the osteochondral unit. Our aim is to evaluate whether fusing PCCT and MRI improves diagnostic clarity and segmentation consistency for osteochondral lesions compared with MRI alone. MATERIALS AND

methods24 participants (15 with knee OA and 9 healthy controls) underwent PCCT and 3T MRI between October 2024 and May 2025. Images were co-registered and fused using a mutual information-based algorithm. Two musculoskeletal radiologists rated diagnostic clarity on 5-point Likert scales. Inter-reader agreement was quantified with the intraclass correlation coefficient (ICC) and Cohen kappa. Another musculoskeletal radiologist and a radiology trainee performed manual segmentations of bones, cartilage, meniscus, and osteophytes on PCCT-MRI fusion or MRI alone. Inter- and intra-rater segmentation consistency was evaluated using Dice similarity coefficient and surface Dice (sDice). Paired comparisons were analyzed with the Wilcoxon signed-rank test.

resultsFusion imaging yielded significantly higher clarity scores for cartilage thickness (3.91 vs 4.95), articular surface (3.93 vs 4.63), and osteophyte boundaries (4.00 vs 4.83; all p < .05). Inter-reader agreement improved (ICC, from 0.43 to 0.88; Cohen kappa, from 0.42 to 0.89). Quantitative analysis showed greater inter- and intra-rater consistency for cartilage (Dice, 0.70 vs 0.81; sDice, 0.66 vs 0.78) and bone (Dice, 0.93 vs 0.95; sDice, 0.61 vs 0.86; all p < .01).

conclusionPCCT-MRI fusion enhances diagnostic clarity and inter-reader agreement. Fusion also increases segmentation consistency for cartilage and bone, supporting its potential clinical value for comprehensive osteochondral evaluation.

Indexed as

CartilageKneeMRIOsteoarthritisPhoton counting CT

Identifiers

PMID42635411
PMCPMC13590918

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.